Posts Tagged ‘optical coherence tomography’

Coronary Circulation Combined Assessment: Optical Coherence Tomography (OCT), Near-Infrared Spectroscopy (NIRS) and Intravascular Ultrasound (IVUS) – Detection of Lipid-Rich Plaque and Prevention of Acute Coronary Syndrome (ACS)

Author, and Content Consultant to e-SERIES A: Cardiovascular Diseases: Justin Pearlman, MD, PhD, FACC


Article Curator: Aviva Lev-Ari, PhD, RN

The clinical motivations for coronary artery imaging include identifying and characterizing obstructive lesions, analyzing suitability for various feasible interventions, and assessing comparative risk with and without interventions. With improvements in non-invasive detection of fixed obstructions in the coronary arteries, it should not be surprising that half of the lesions that cause heart attacks (myocardial infarction) among those who had recent imaging consisted of unstable plaques that were less than 50% obstructive. Therefore there is growing interest not only in more reliable detection of lesions that exceed 50% obstruction, but also improved characterization of lesions that are not obstructive but may be unstable.

By way of analogy, think of impaired blood supply to the heart as a traffic jam in a roadway. The best time to check for a traffic jam is during rush hour. The corresponding clinical scenario is stress testing. There are three major roadways in the heart: left anterior, left circumflex, and right, each with branches (forks). The two left major vessels stem from a short but treacherous left main (“widow maker”). A temporary traffic jam results in symptoms of impaired delivery (angina, from hunger due to late delivery of food). Alternatively, a prolonged traffic disruption can result in suicidal tissue destruction (starvation). A fixed obstruction consists of potholes and landslides resulting in a persisting shutdown of half or more of the lanes in the highway. An unstable plaque consists of a less severe abnormality that can cause accidents (plaque rupture, local hemorrhage, sudden occlusion). A road may shutdown not only from progressive road damage, but also a truck can flip over and shutdown a relatively clean roadway.

Among patients who had recent coronary imaging prior to the onset of a heart attack, half do not have occlusive lesions. Instead of slow progressive reduction in vessel diameter leading to a critically severe flow reduction, the mechanism in the cases of no severe narrowing is attributed to unstable plaque, meaning plaque with thin fibrous caps that rupture, causing sudden thrombosis. Stress tests focus on detection of fixed obstructions and do not warn who has unstable plaque. Thus the next great frontier for coronary imaging is not just to identify flow reducing lesions, but also to identify unstable plaque even if it is not currently flow limiting. This article presents candidate imaging methods and their current capabilities.

Coronary imaging methods include:

  • intra-coronary ultrasound (IVUS)
  • optical coherence imaging (fiberoptic)
  • computed tomographic xray angiography (CTA)
  • magnetic resonance angiography (MRA)
  • near infra-red spectroscopic imaging (NIRS)

    NIRS-IVUS Imaging To Characterize the Composition and Structure of Coronary Plaques 

    David Rizik, MD1 and James, A. Goldstein, MD2

    1. Scottsdale Healthcare Hospital, Scottsdale, AZ

    2. Department Cardiovascular Medicine, William Beaumont Hospital, Royal Oak, MI

    This supplement,


    authored by highly experienced interventional cardiologists expert in the field of coronary plaque characterization, contains a detailed description of the new NIRS-IVUS combination catheter, and the clinical information obtained during its use in over 90 hospitals in over 10 countries. Case vignettes, cohort outcomes, reviews, and plans for future studies are also presented. It is our hope that this information will be useful in the near term to those seeking to improve PCI. For the longer term, we believe that the NIRS-IVUS system is an excellent candidate for evaluation as a detector of vulnerable plaque. Success in the prospective studies that are planned will make it possible to detect vulnerable plaques and thereby enhance efforts to prevent coronary events.

    Imaging Methods for Detection of Intravascular Plaque – Direct, Robust and/or Validated

    Cap Thickness – OCT

    Expansive Remodeling – IVUS & NIRS-IVUS [Combination TVC System & TVC Insight Catheter]

    Plaque Volume – IVUSNIRS-IVUS

    Calcification – Angiography, IVUS & NIRS-IVUS

    Thrombus – Angioscopy & OCT

    Inflammation Macrophages – Indirect by OCT

    Lipid Core – IVUS & NIRS-IVUS

    Requires Blood-Free FOV – Angioscopy & OCT

    based on Table 1 p.5


    Comparative Intravascular Imaging for Lipid Core Plaque: VH-IVUS vs OCT vs NIRS

    Eric Fuh, MD and Emmanouil S. Brilakis, MD, PhD

    VA North Texas Healthcare System, Dallas, TX and Division of Cardiology, Dept of Medicine, UT Southwestern Medical Center, Dallas, TX


    VH-IVUS, OCT, and NIRS can assist in the detection and evaluation of lipid core plaque. Comparative studies have shown important differences between modalities, but are all limited from lack of comparison with the gold standard of histology. Given the different strengths and weaknesses of each modality, combination imaging will likely provide the best results.41 Further refinement of the clinical implications of LCP detection and its impact on optimizing treatment strategy selection will stimulate advances in LCP detection imaging.

    OCT and NIRS can image through calcified lesions, whereas IVUS cannot. LCPs are often accompanied by neovascularization, which can only be visualized by OCT. VH-IVUS may classify stents, which usually appear white (misclassified as “calcium”) surrounded by red (misclassified as “necrotic core”), although this does not appear to be a limitation for NIRS and OCT.54

    Reference 41:

    Bourantas CV, Gracia-Gracia HM, Naka KK, et al. Hybrid intravascular imaging: current applications and prospective potential in the study of coronary atherosclerosis, JACC 2013;61:1369-1378


    The miniaturization of medical devices and the progress in image processing have allowed the development of a multitude of intravascular imaging modalities that permit more meticulous examination of coronary pathology. However, these techniques have significant inherent limitations that do not allow a complete and thorough assessment of coronary anatomy. To overcome these drawbacks, fusion of different invasive and noninvasive imaging modalities has been proposed. This integration has provided models that give a more detailed understanding of coronary artery pathology and have proved useful in the study of the atherosclerotic process. In this review, the authors describe the currently available hybrid imaging approaches, discuss the technological innovations and efficient algorithms that have been developed to integrate information provided by different invasive techniques, and stress the advantages of the obtained models and their potential in the study of coronary atherosclerosis.


    Reference 54

    Kim SW, Mintz GS, Hong YJ, et al. The virtual histology intravascular ultrasound appearance of newly placed drug-eluting stents. Am J Cardiol. 2008;102:1182-1186.

    American Journal of Cardiology
    Volume 102, Issue 9 , Pages 1182-1186, 1 November 2008

    The Virtual Histology Intravascular Ultrasound Appearance of Newly Placed Drug-Eluting Stents

    Received 17 January 2008; received in revised form 17 March 2008; accepted 17 March 2008. published online 13 June 2008.

    Intravascular ultrasound (IVUS) is used before and after intervention and at follow-up to assess the quality of the acute result as well as the long-term effects of stent implantation. Virtual histology (VH) IVUS classifies tissue into fibrous and fibrofatty plaque, dense calcium, and necrotic core. Although most interventional procedures include stent implantation, VH IVUS classification of stent metal has not been validated. In this study, the VH IVUS appearance of acutely implanted stents was assessed in 27 patients (30 lesions). Most stent struts (80%) appeared white (misclassified as “calcium”) surrounded by red (misclassified as “necrotic core”); 2% appeared just white, and 17% were not detectable (compared with grayscale IVUS because of the software-imposed gray medial stripe). The rate of “white surrounded by red” was similar over the lengths of the stents; however, undetectable struts were mostly at the distal edges (31%). Quantitatively, including the struts within the regions of interest increased the amount of “calcium” from 0.23 ± 0.35 to 1.07 ± 0.66 mm2 (p <0.0001) and the amount of “necrotic core” from 0.59 ± 0.65 to 1.31 ± 0.87 mm2 (p <0.0001). Most important, because this appearance occurs acutely, it is an artifact, and the red appearance should not be interpreted as peristrut inflammation or necrotic core when it is seen at follow-up. In conclusion, acutely implanted stents have an appearance that can be misclassified by VH IVUS as “calcium with or without necrotic core.” It is important not to overinterpret VH IVUS studies of chronically implanted stents when this appearance is observed at follow-up. A separate classification for stent struts is necessary to avoid these misconceptions and misclassifications.

    Table 2. Comparison of three intravascular imaging modalities for the detection of coronary lipid core plaque.

    Intravascular Imaging Modalities for Detecting LCP

    Vol. 25, Supplement A, 2013


     VH-IVUS (20 MHz)                        OCT                          NIRS-IVUS (40 MHz)

    Hybrid intravascular imaging  No No Yes

    Axial resolution, μm 200 10 100

    Imaging through blood ++ – ++

    Need for blood column clearance during image acquisition No Yes No

    Imaging through stents No Yes Yes

    Imaging through calcium No Yes Yes for NIRS – No for IVUS

    Imaging neovascularization No Yes No

    Detection of non-superficial LCPs Yes No No

    Evaluation of LCP cap thickness + ++ *

    Detection of thrombus – + *

    Expansive remodeling ++ – ++

    Need for manual image processing for LCP detection Yes Yes No

    ++ = excellent; + = good; ± = possible; – = impossible; * = potential under investigation

    VH-IVUS = virtual histology intravascular ultrasound; OCT = optical coherence tomogra-phy; NIRS = near-infrared spectroscopy; LCP = lipid core plaque 

    The Search for Vulnerable Plaque — The Pace Quickens


    Ryan D. Madder, MD1, Gregg W. Stone, MD2, David Erlinge, MD3, James E. Muller, MD4


    1Frederik Meijer Heart & Vascular Institute, Spectrum Health, Grand Rapids, Michigan;

    2New York Presbyterian Hospital, Columbia University and Car-diovascular Research Foundation, New York, New York;

    3Department of Cardiology, Lund University, Lund, Sweden;

    4Infraredx, Inc., Burlington, Massachusetts

    Disclosure: Drs. Madder and Erlinge report no financial relationships or conflicts of interest regarding the content herein.

    Dr. Stone is a consultant for Infraredx, Inc., Volcano Corp., Medtronic, and Boston Scientific, and is a member of the scientific advisory boards for Boston Scientific and Abbott Vascular.

    Dr. Muller is a full-time employee of Infraredx, Inc from which he receives salary and equity.

    Address for Correspondence: Email: ryan.madder@spectrumhealth.org

    The search for the vulnerable plaque has been a lengthy endeavor requiring the work of multiple individuals and institutions over many years. It is disappointing that in more than 2 decades since the “vulnerable plaque” concept was formulated, over 40 million coronary events have occurred. However, it is encouraging that positive answers are now available for most of the questions related to a vulnerable plaque detection and treatment strategy. As shown in Table 1, most of the essential preconditions of a successful vulnerable plaque strategy are present. This positive information has accelerated the pace of work in this area. The pathophysiology of coronary events is well-understood; powerful imaging methods are available; and therapies, both existing and novel, may well be effective (although appropriately powered randomized trials are required to demonstrate their safety and effectiveness). The time is approaching for the conduct of prospective outcome trials to determine the value of a vulnerable plaque strategy for more effective prevention of coronary events.

    Table 1. Essential Components of a Strategy to Prevent Coronary Events by the Detection and Treatment of Vulnerable Plaques

    Essential Components Evidencefrom  Published Research
    Pathophysiology of Coronary Events
    Are the causes of coronary events known? Yes Constantinides and others have shown that most coronary events are caused by rupture of a thin-capped LRP with subsequent formation of an occlusive thrombus.1-5
    Are LRPs focal? Yes Cheruvu et al demonstrated that ruptures and TCFA occupy less than 4% of the length of arteries studied at autopsy.8
    Are LRPs stable over time? Yes Kubo et al demonstrated that most fibroatheromas by radiofrequency IVUS remain fibroatheromas over time.39
    Detection of Suspected Vulnerable Plaque by Invasive Imaging (For Secondary Prevention)
    Can invasive imaging safely detect LRP? Yes Waxman et al, Ino et al, and many others have demonstrated the safety of detecting LRP in patients.40
    Do cross-sectional studies show increased LRP concentrated at culprit sites? Yes Madder et al, Erlinge et al, Ino et al have shown LRP concentrated at the culprit site across the spectrum of ACS.14,16,41
    Do prospective studies show that suspected vulnerable plaque can be detected in advance? Yes PROSPECT, VIVA, PREDICTION established the principle by proving that increased plaque burden predicted events but prediction lacked specificity.23-25
    Is more specific detection of vulnerable plaque possible? ? NIRS-IVUS and OCT may provide more specific detection of VP, but have not yet been tested in a prospective study.
    Can Vulnerable Plaques be Treated?
    Is systemic treatment of LRPs possible with current agents? Yes YELLOW study showed a reduction in LRP with rosuvastatin.33
    Is focal treatment of LRPs possible with current methods? Yes Ruptured LRPs are routinely stented in ACS in clinical practice with good outcomes.
    Can systemic treatment be enhanced with new agents? ? PCSK9 inhibitors, Apo A1 Milano, other agents in development may be more effective than statins, but more costly.35,36
    Can focal treatments be enhanced with new methods? ? Bioresorbable vascular scaffolds and/or drug-coated balloons may be useful for VP.
    Primary Prevention
    Can demographic and serum biomarkers be used as a first step in a screening strategy? Yes Framingham Risk Score, improved serum biomarkers, and genetic markers can identify individuals at increased risk.
    Can non-invasive imaging with CTA detect LRP and increased risk? Yes Motoyama et al have identified CTA markers associated with future events.26
    Will a strategy of detection and treatment of vulnerable plaque, if proven to be successful, be cost-effective for secondary prevention? Probably Bosch et al demonstrated that for patients already undergoing invasive imaging, the added costs of detection and treatment of VP are likely to be less than the cost of second events, leading to a cost-saving approach that also improves health.38
    Will a strategy of detection and treatment of vulnerable plaque, if proven to be successful, be cost-effective for primary prevention? ? Bosch et al: For primary prevention the cost of screening would be greater than for secondary prevention. Cost-effectiveness would depend upon cost, the accuracy of detection, and effectiveness of therapy.38
    ACS = acute coronary syndrome; CTA = coronary computed tomographic angiography; LRP = lipid-rich plaque; TCFA = thin-capped fibroatheroma; 


    1. Constantinides P. Plaque fissures in human coronary thrombosis. J Atheroscler Res. 1966;6:1-17.

    2. Friedman M, Van den Bovenkamp GJ. The pathogenesis of a coronary thrombus. Am J Pathol. 1966;48:19-44.

    3. Burke AP, Farb A, Malcom GT, et al. Coronary risk factors and plaque morphology in men with coronary disease who died suddenly. N Engl J Med. 1997;336:1276-1282.

    4. Farb A, Tang AL, Burke AP, et al. Sudden coronary death. Frequency of active coronary lesions, inactive coronary lesions, and myocardial infarction. Circulation. 1995;92:1701-1709.

    5. Virmani R, Kolodgie FD, Burke AP, Farb A, Schwartz SM. Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. Arterioscler Thromb Vasc Biol. 2000;20:1262-1275.

    6. Waksman R, Serruys PW. Handbook of the Vulnerable Plaque. Martin Dunitz: London, England, 2004.

    7. Libby P, Ridker PM, Hansson GK. Progress and challenges in translating the biology of atherosclerosis. Nature. 2011;473:317-325.

    8. Cheruvu P, Finn A, Gardner C, et al. Frequency and distribution of thin-cap fibroatheroma and ruptured plaques in human coronary arteries – a pathologic study. J Am Coll Cardiol. 2007;50:940-949.

    9. Hong M, Mintz GS, Lee CW, et al. Comparison of coronary plaque rupture between stable angina and acute myocardial infarction: a three-vessel intravascular ultrasound study in 235 patients. Circulation. 2004;110:928-933.

    10. Fujii K, Kobayashi Y, Mintz GS, et al. Intravascular ultrasound assessment of ulcerated ruptured plaques. A comparison of culprit and non-culprit lesions of patients with acute coronary syndromes and lesions in patients without acute coronary syndromes. Circulation. 2003;108:2473-2478.

    11. Ehara S, Kobayashi Y, Yoshiyama M, et al. Spotty calcification typifies the culprit plaque in patients with acute myocardial infarction. An intravascular ultrasound study. Circulation. 2004;110:3424-3429.

    12. Lee SY, Mintz GS, Kim SY, et al. Attenuated plaque detected by intravascular ultrasound: clinical, angiographic, and morphologic features and post-percutaneous coronary intervention complications in patients with acute coronary syndromes. J Am Coll Cardiol Intv. 2009;2:65-72.

    13. Asakura M, Ueda Y, Yamaguchi O, et al. Extensive development of vulnerable plaques as a pan-coronary process in patients with myocardial infarction: an angioscopic study. J Am Coll Cardiol. 2001;37:1284-1288.

    14. Ino Y, Kubo T, Tanaka A, et al. Difference of culprit lesion morphologies between ST-segment elevation myocardial infarction and non-ST-segment elevation acute coronary syndrome. J Am Coll Cardiol Intv. 2011;4:76-82.

    15. Madder RD, Smith JL, Dixon SR, Goldstein JA. Composition of target lesions by near-infrared spectroscopy in patients with acute coronary syndrome versus stable angina. Circ Cardiovasc Interv. 2012;5:55-61.

    16. Madder RD, Goldstein JA, Madden SP, et al. Detection by near-infrared spectroscopy of large lipid core plaques at culprit sites in patients with acute ST-segment elevation myocardial infarction. J Am Coll Cardiol Intv. In press, 2013.

    17. Hoffmann U, Moselewski F, Nieman K, et al. Noninvasive assessment of plaque morphology and composition in culprit and stable lesions in acute coronary syndrome and stable lesions in stable angina by mulitdetector computed tomography. J Am Coll Cardiol. 2006;47:1655-1662.

    18. Motoyama S, Kondo T, Sarai M, et al. Multislice computed tomographic characteristics of coronary lesions in acute coronary syndromes. J Am Coll Cardiol. 2007;50:319-326.

    19. Madder RD, Chinnaiyan KM, Marandici AM, Goldstein JA. Features of disrupted plaques by coronary computed tomographic angiography: correlates with invasively proven complex lesions. Circ Cardiovasc Imaging. 2011;4:105-113.

    20. Muller JE, Tofler GH, Stone PH. Circadian variation and triggers of onset of acute cardiovascular disease. Circulation. 1989;79;733-743.

    21. Kolodgie FD, Burke AP, Farb A, et al. The thin-cap fibroatheroma: a type of vulnerable plaque: the major precursor lesion to acute coronary syndromes. Curr Opin Cardiol. 2001;16:285-292.

    22. Yamagishi M, Terashima M, Awano K, et al. Morphology of vulnerable coronary plaque: insights from follow-up of patients examined by intravascular ultrasound before an acute coronary syndrome. J Am Coll Cardiol. 2000;35:106-111.

    23. Stone GW, Maehara A, Lansky A, et al. A prospective natural-history study of coronary atherosclerosis. N Engl J Med. 2011;364:226-235.

    24. Calvert PA, Obaid DR, O’Sullivan M, et al. Association between IVUS findings and adverse outcomes in patients with coronary artery disease: the VIVA (VH-IVUS in Vulnerable Atherosclerosis) study. J Am Coll Cardiol Imaging. 2011;4:894-901.

    25. Stone PH, Saito S, Takahashi S, et al. Prediction of progression of coronary artery disease and clinical outcomes using vascular profiling of endothelial shear stress and arterial plaque characteristics: the PREDICTION study. Circulation. 2012;126:172-181.

    26. Motoyama S, Sarai M, Harigaya H, et al. Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome. J Am Coll Cardiol. 2009;54:49-57.

    27. Stone GW, Maehara A, Mintz GS. The reality of vulnerable plaque detection. J Am Coll Cardiol Imaging. 2011;4:902-904.

    28. Madder RD, Steinberg DH, Anderson RD. Multimodality direct coronary imaging with combined near-infrared spectroscopy and intravascular ultrasound: Initial US experience. Catheter Cardiovasc Interv. 2013;81:551-7.

    29. Kume T, Akasaka T, Kawamoto T, et al. Measurement of the thickness of the fibrous cap by optical coherence tomography. Am Heart J. 2006;152:755.e1-4.

    30. Nissen SE, Tuzcu EM, Schoenhagen P, et al. Effect of intensive compared with moderate lipid-lowering therapy on progression of coronary atherosclerosis: a randomized controlled trial. JAMA. 2004;291:1071-1080.

    31. Nissen SE, Nicholls SJ, Sipahi I, et al. Effect of very high-intensity statin therapy on regression of coronary atherosclerosis: the ASTEROID trial. JAMA. 2006;295:1556-1565.

    32. Nicholls SJ, Ballantyne CM, Barter PJ, et al. Effect of two intensive statin regimens on progression of coronary disease. N Engl J Med. 2011;365:2078-2087.

    33. Kini AS, Baber U, Kovacic JC, et al. Changes in plaque lipid content after short-term, intensive versus standard statin therapy: the YELLOW trial. J Am Coll Cardiol. 2013 (In press).

    34. Takarada S, Imanishi T, Kubo T, et al. Effect of statin therapy on coronary fibrous-cap thickness in patients with acute coronary syndrome: assessment by optical coherence tomography study. Atherosclerosis. 2009;202:491-497.

    35. Stein EA, Gipe D, Bergeron J, et al. Effect of a monoclonal antibody to PCSK9, REGN727/SAR236553, to reduce low-density lipoprotein cholesterol in patients with heterozygous familial hypercholesterolaemia on stable statin dose with or without ezetimibe therapy: a phase 2 randomised controlled trial. Lancet. 2012;380:29-36.

    36. Nissen SE, Tsunoda T, Tuzcu EM, et al. Effect of recombinant ApoA-I Milano on coronary atherosclerosis in patients with acute coronary syndromes: a randomized controlled trial. JAMA. 2003;290:2292-2300.

    37. Braunwald, E. Epilogue: What do clinicians expect from imagers? J Am Coll Cardiol. 2006;47:C101-C103.

    38. Bosch JL, Beinfeld MT, Muller JE, Brady T, Gazelle GS. A cost-effectiveness analysis of a hypothetical catheter-based strategy for the detection and treatment of vulnerable coronary plaques with drug-eluting stents. J Interv Cardiol. 2005;18:339-349.

    39. Kubo T, Maehara A, Mintz GS, et al. The dynamic nature of coronary artery lesion morphology assessed by serial virtual histology intravascular ultrasound tissue characterization. J Am Coll Cardiol. 2010;55:1590-1597.

    40. Waxman S, Dixon SR, L’Allier P, et al. In vivo validation of a catheter-based near-infrared spectroscopy system for detection of lipid core coronary plaques: initial results and exploratory analysis of the SPECTroscopic Assessment of Coronary Lipid (SPECTACL) multicenter study. J Am Coll Cardiol Imaging. 2009;2:858-868.

    41. Erlinge D, Muller JE, Puri R, et al. Validation of a near-infrared spectroscopic signature of lipid located at culprit lesions in ST-segment elevation myocardial infarction. European Atherosclerosis Society. June 2013 (abstract).


    Proposed Algorithm for Vulnerable Plaque Screening and Treatment 


    Page 31A in


    Long-term Consequences of a Lipid Core Plaque

    Christos V. Bourantas, MD, PhD1, Hector M. Garcia, MD, PhD1, Roberto Diletti, MD1, Carlos A.M. Campos, MD1, Yaojun Zhang, MD, PhD1, Scot Garg, MRCP, PhD2, Patrick W. Serruys, MD, PhD1

    1Department of Interventional Cardiology, Erasmus University Medical Centre, Thoraxcenter, Rotterdam, The Netherlands and 2Department of Cardiology, East Lancashire NHS Trust, Haslingden Road, Blackburn, Lancashire, United Kingdom.

    Disclosures: The authors report no financial relationships or conflicts of interest regarding the content herein.

    Address for correspondence:  Email: p.w.j.c.serruys@erasmusmc.nl

    The advent of intravascular imaging in the 1980s allowed us to study in vivo plaque morphology and its prognostic implications.

    • Angioscopy and intravascular ultrasound (IVUS) were the first imaging techniques that provided information about the composition of plaque and allowed detection of its lipid component.7,8

    However, the first applications of these modalities in the clinical setting not only underscored their potential value in the study of atherosclerosis but also highlighted their limitations in characterizing atheroma.9-11 Therefore an effort was made over the last few years to develop advanced techniques that would allow more reliable assessment of a plaque’s composition. Today several modalities are available for this purpose including:

    • the radiofrequency analysis of the IVUS backscatter signal (RF-IVUS),
    • near-infrared spectroscopy (NIRS),
    • optical coherence tomography (OCT),
    • magnetic resonance spectroscopy,
    • intravascular magnetic resonance imaging,
    • Raman spectroscopy,
    • photoacoustic imaging, and
    • time resolved spectroscopic imaging (Figure 1).

    Some of these modalities are still in their infancy, while others have already been used in the clinical setting providing robust evidence about the prognostic implications of the differing compositions of the plaque. The aim of this review article is to present the most recent evidence about the long-term consequences of the atheroma’s phenotype. 

    Current Evidence from NIRS-based Clinical Studies

    NIRS relies on the principle that different organic molecules absorb and scatter NIRS light to different degrees and wavelengths. Recent advances in device technology enabled the development of a catheter suitable for assessing the plaque in human coronaries that is able to emit NIR light and acquire the scattered signal. Spectral analysis of the obtained signal provides a color-coded display, called a chemogram (Figure 1C), which provides the probability that lipid core is present in the superficial plaque (studied depth approximately: 1 mm). Several studies have examined the reliability of this technique using histology as the gold standard and demonstrated a high overall accuracy in detecting lipid-rich plaques while others demonstrated its feasibility in the clinical setting.19-20

    The European Collaborative Project on Inflammation and Vascular Wall Remodeling in Atherosclerosis (NCT01789411) – NIRS sub-study was the first prospective trial designed to evaluate the prognostic implications of an increased lipid component, as detected by NIRS, in coronary plaques. Two hundred three patients that underwent X-ray angiography, and PCI if it was indicated, had NIRS in a non-culprit coronary segment and were followed-up for 1 year. Twenty-eight patients sustained a MACE during the follow-up period; 21 of these events were non-culprit lesion related. Lipid plaque burden index appeared to be an independent predictor of MACE (hazard ratio: 4.04, 95% confidence interval: 1.33-12.29; P=0.01). 

    Currently, the Chemometric Observation of Lipid Rich Plaque of Interest in Native Coronary Arteries (COLOR, NCT00831116) registry is recruiting patients. This study is planning to recruit 2000 patients that will be investigated with NIRS imaging, and aims to examine the association between the presence of a necrotic core in the atheroma and subsequent coronary events. Preliminary results indicate that the absence of lipid-rich plaques is related with better outcomes (www.infraredx.com/the-color-registry). 

    Current Evidence From OCT-based Clinical Studies

    OCT imaging with its high resolution appears able to provide detailed assessment of the superficial plaque and visualize structures that are unseen by other techniques such as the presence of micro calculations of thin-capped fibroatheroma (TCFA). However, a significant limitation of this technique is its poor penetration (1-2 mm), which does not permit through visualization of plaque burden, as well as its low capacity in differentiating lipid from calcific tissue when these are deeply embedded in the vessel wall.21

    In this analysis, 53 patients who underwent PCI had OCT imaging in non-obstructive lesion sat baseline and repeat angiography at 7 months follow-up. They found that plaques with a TCFA phenotype, exhibiting vessel walldiscontinuities, macrophages, neo-vessels, and thrombi were morelikely to progress and cause significant angiographic obstructions.22

    Future Perspective in Plaque Imaging – Conclusions

    Cumulative data derived from intravascular imaging studies have provided robust evidence about the prognostic implications of plaque’s composition and burden, and demonstrated a strong association between the presence of lipid-rich plaques and future cardiovascular events. Plaque pathology and quantification of lipid components is done by hybrid catheters able to acquire different intravascular imaging data.23

    References on page 26A in


    1.Kragel AH, Reddy SG, Wittes JT, Roberts WC. Morphometric analysis of the composition ofatherosclerotic plaques in the four major epicardial coronary arteries in acute myocardial infarctionand in sudden coronary death. Circulation. 1989;80:1747-1756.

    2.ᆳacteristics of coronary atherosclerotic plaques underlying fatal occlusive thrombi. Br Heart J.1983;50:127-134.

    3.Clark E, Graef I, Chasis H. Thrombosis of the aorta and coronary arteries. Archives of Pathology.1936;22:183-212.

    4.Virmani R, Kolodgie FD, Burke AP, Farb A, Schwartz SM. Lessons from sudden coronary death:a comprehensive morphological classification scheme for atherosclerotic lesions. ArteriosclerThromb Vasc Biol. 2000;20:1262-1275.

    5.Stary HC, Chandler AB, Glagov S, et al. A definition of initial, fatty streak, and intermediatelesions of atherosclerosis. A report from the Committee on Vascular Lesions of the Council onArteriosclerosis, American Heart Association. Circulation. 1994;89:2462-2478.

    6.ᆳrotic lesions and a histological classification of atherosclerosis. A report from the Committee onVascular Lesions of the Council on Arteriosclerosis, American Heart Association. Circulation.1995;92:1355-1374.

    7.Di Mario C, The SH, Madretsma S, et al. Detection and characterization of vascular lesionsby intravascular ultrasound: an in vitro study correlated with histology. J Am Soc Echocardiogr. 1992;5:135-146.

    8.ᆳdation by histomorphologic analysis and association with stable and unstable coronary syndromes.J Am Coll Cardiol. 1996;28:1-6.

    9.Hiro T, Leung CY, Russo RJ, et al. Variability in tissue characterization of atherosclerotic plaqueby intravascular ultrasound: a comparison of four intravascular ultrasound systems. Am J CardImaging. 1996;10:209-218.

    10.ᆳdial infarction: ability of optical coherence tomography compared with intravascular ultrasoundand coronary angioscopy. J Am Coll Cardiol. 2007;50:933-939.

    11.ᆳated with future risk of acute coronary syndrome: detection of vulnerable patients by angioscopy.J Am Coll Cardiol. 2006;47:2194-2200.

    12.ᆳnary syndrome using integrated backscatter intravascular ultrasound. J Am Coll Cardiol.2006;47:734-741.

    13.Amano T, Matsubara T, Uetani T, et al. Lipid-rich plaques predict non-target-lesion ischemicevents in patients undergoing percutaneous coronary intervention. Circ J. 2011;75:157-166.

    14.ᆳsclerosis. N Engl J Med. 2011;364:226-235.

    15.Calvert PA, Obaid DR, O’Sullivan M, et al. Association between IVUS findings and adverseᆳsclerosis) Study. JACC Cardiovasc Imaging. 2011;4:894-901.

    16.Granada JF, Wallace-Bradley D, Win HK, et al. In vivo plaque characterization using intravascularultrasound-virtual histology in a porcine model of complex coronary lesions. Arterioscler ThrombVasc Biol. 2007;27:387-393.

    17.Sales FJ, Falcao BA, Falcao JL, et al. Evaluation of plaque composition by intravascular ultrasound“virtual histology”: the impact of dense calcium on the measurement of necrotic tissue. ᆳvention. 2010;6:394-399.

    18.ᆳtual histology intravascular ultrasound in porcine coronary artery disease. Circ Cardiovasc Imaging. 2010;3:384-391.

    19.ᆳmens with a novel catheter-based near-infrared spectroscopy system. JACC Cardiovasc Imaging. 2008;1:638-648.

    20.Waxman S, Dixon SR, L’Allier P, et al. In vivo validation of a catheter-based near-infrared spectrosᆳcopy system for detection of lipid core coronary plaques: initial results of the SPECTACL study.JACC Cardiovasc Imaging. 2009;2:858-868.

    21.Manfrini O, Mont E, Leone O, et al. Sources of error and interpretation of plaque morphology byoptical coherence tomography. Am J Cardiol. 2006;98:156-159.

    22.Uemura S, Ishigami K, Soeda T, et al. Thin-cap fibroatheroma and microchannel findings inoptical coherence tomography correlate with subsequent progression of coronary atheromatousplaques. Eur Heart J. 2012;33:78-85.

    23.ᆳplications and prospective potential in the study of coronary atherosclerosis. J Am Coll Cardiol.2013;61:1369-378.

    24.ᆳtroscopy and intra-vascular ultrasound catheter to identify composition and structure of coronaryplaque. EuroIntervention. 2010;5:755-756.

    25.ᆳᆳgrated Biomarker and Imaging Study-3 (IBIS-3). EuroIntervention. 2012;8:235-241.


    NIRS-IVUS Imaging Identifies Lesions at High Risk of Peri-Procedural Myocardial Infarction

    James A. Goldstein, MD, Simon R. Dixon, MBChB*, Gregg W. Stone, MD

    From the Department of Cardiovascular Medicine, William Beaumont Hospital, Royal Oak, MI.

    Address for correspondence: James A. Goldstein, MD, FACC, Department of Cardiovascular Medicine, William Beaumont Hospital, 3601 West 13 Mile Road, Royal Oak, Michigan 48073. Email: jgoldstein@beaumont.edu

    Disclosures: Dr. Goldstein is a consultant for and owns equity in Infraredx, Inc. Dr. Stone is a consultant for Infraredx, Inc., Volcano Corp., Medtronic, and Boston Scientific, and is a member of the scientific advisory boards for Boston Scientific and Abbott Vascular. Dr. Dixon reports no financial relationships or conflicts


    Percutaneous coronary intervention (PCI) is associated with distal embolization complications, including peri-procedural myocardial infarction (PPMI), including no-reflow, in 3%-15% of cases. These complications are predominantly related to distal embolization of lipid core plaque (LCP) components. Catheter-based near-infrared spectroscopy (NIRS) provides rapid, automated detection of LCPs, the magnitude of which appears associated with a high-risk of PPMI. Employing this technique may facilitate development of preventive measures such as embolic protection devices (EPDs).

    J INVASIVE CARDIOL 2013;25 (Suppl A):14A-16A

    Key words: Distal embolization, lipid core plaque, near-infrared spectroscopy, peri-procedural myocardial infarction

    Figures 1. A 62-year-old man with stable angina underwent coronary angiography, which demonstrated a complex hazy ulcerated culprit lesion in the mid-right coronary artery (Figure 1A, solid arrow). Neither the angiogram nor an intravascular ultrasound image indicated the presence of thrombus. NIRS demonstrated a large yellow signal spanning the circumference of the culprit site (Figure 1B, white rectangle), indicating the presence of a napkin-ring LCP; a smaller LCP was evident distally (Figure 1, open arrow).

    Figure 2. Balloon angioplasty was performed (Figure 2A, arrow), which led to prompt no-reflow (Figure 2B, arrow) associated with severe bradyarrhythmia and profound hypotension (Figure 2C). After brief cardiopulmonary resuscitation and pharmacological support with atropine and dopamine, physiologic rhythm and blood pressure were restored and stenting resulted in excellent angiographic outcome. However, the patient developed a peri-stenting non-transmural infarction (peak creatine kinase of 512 ng/mL) and required an additional day of hospital care in an intensive care unit. (Goldstein JA, et al. JACC Cardiovasc Imaging. 2009;2(12):1420-1424. Reproduced with permission.)

    On Page 14A in


    Pharmacological Therapy of Lipid Core Plaque

    Jason C. Kovacic, MD, PhD, Annpoorna Kini, MD, MRCP

    From The Zena and Michael A. Wiener Cardiovascular Institute, Mount Sinai School of Medicine, New York, New York.

    Address for correspondence: Dr. Annapoorna Kini, Mount Sinai School of Medicine, One Gustave L. Levy Place, Box 1030, New York, NY, 10029. Email: annapoorna.kini@mountsinai.org

    Disclosures: Dr. Kovacic is supported by National Institutes of Health Grant K08HL111330 and has received research support from AstraZeneca. Dr. Kini acknowledges honoraria from Medscape and has received research grant support from InfraReDx.

    A new group of terms is slowly creeping in to the atherosclerotic disease lexicon: “Lipid Arc,” “Lipid Core Plaque,” “Lipid-Rich Plaque,” “Lipid Core Burden Index” and other similar phrases. While clinicians and researchers have long been aware of the central importance of lipid in the biology of atherosclerosis, the growing use of these terms is driven by the recent widespread use of novel imaging modalities that provide accurate detection, and even quantification, of the extent of lipid that is contained within the core of an atherosclerotic plaque. Our ability to detect and quantify lipid in plaques is opening up new therapeutic opportunities for modifying the atherosclerotic disease process, which may ultimately be of benefit to patients.

    At the present time there are 3 methods that are commonly used to measure the extent of lipid in atherosclerotic plaques. Perhaps most familiar of these is coronary computer tomographic (CT) scanning. While more commonly used to quantitate calcification or luminal stenosis, CT scanning is readily able to quantitate the extent of lipid associated with an atherosclerotic lesion. However, while several studies have reported various Hounsfield Unit (HU)-based criteria to distinguish lipid-rich from fibrous plaques, the HU cut-off points have so far been inconsistent. The use of CT for detecting lipid-rich plaque is further limited by its relatively low spatial resolution and the fact that the HU values for distinguishing between fibrous and lipid-rich plaques are overlapping.1 In contrast, optical coherence tomography (OCT) offers perhaps the greatest spatial resolution of all clinically available coronary imaging devices. OCT can offer exquisite detail of abluminal coronary artery anatomy, including detection of lipid core plaque. However, while automated systems are being developed, at the present time the quantitation of lipid by OCT is a somewhat specialized process that typically involves detailed off-line analysis.

    A specific intra-coronary imaging catheter for the quantitation of coronary artery lipid content is now available and FDA approved: diffuse reflectance near-infrared spectroscopy (NIRS). The application of NIRS to identify lipid deposition within coronary arteries has been validated ex vivo2-5 and in vivo.6,7 Although NIRS itself is essentially only able to detect and quantitate lipid, design changes and technological advances to this catheter have now made it possible to combine intravascular ultrasound (IVUS) and NIRS technology on a single instrument. In one of the few clinical studies published to date using this device, NIRS has already shown that a high lipid burden in a target lesion undergoing percutaneous coronary intervention (PCI) is associated with an increased likelihood of peri-procedural myocardial infarction.7

    It is well known that the reduction of cholesterol levels by statin therapy is associated with significant decreases in plaque burden. REVERSAL,8 ASTEROID,9 and more recently the SATURN II10 trial showed that in patients with coronary artery disease (CAD), lipid lowering with high-dose statin therapy reduced progression of plaque atheroma burden, even causing plaque regression of some lesions. However, while reduction in atheroma burden and plaque size are important anatomical endpoints, a major unresolved question had been the mechanism of action of statins and the unanswered question of whether they reduce plaque lipid content. Indeed, a high burden of plaque lipid is one of the cardinal features of a rupture-prone vulnerable lesion.11 Therefore, the ability to reduce plaque lipid content may have important effects on lesion stability and therefore, might impact clinical endpoints.

    The advent of sensitive imaging tools for the evaluation of plaque lipid content has paved the way for the investigation of potential pharmacological therapies for lipid core plaque. In particular, the ability of NIRS to provide an automated quantitation of plaque lipid provides a ready-made platform for this task. We recently completed the YELLOW study of high-dose statin therapy for the potential reduction of coronary artery lipid content as assessed by NIRS. We randomized 87 patients with multivessel CAD undergoing elective PCI to rosuvastatin 40 mg daily vs conventional statin therapy. Following PCI of the culprit lesion, non-culprit lesions with a fractional flow reserve (FFR) <0.8 were interrogated using IVUS and NIRS. Changes in plaque composition were assessed after 6-12 weeks during follow-up angiography. The core finding of this study was that high-dose statin therapy was associated with significant reductions in the lipid content of coronary atherosclerotic plaques. Interestingly, despite reduced plaque lipid content, in this relatively short time period concordant changes in gross lesion characteristics such as total atheroma volume or % plaque burden were not observed.12 In short, the YELLOW study identified that even before gross atheroma regression occurs, lipid removal from plaques is an early event upon initiation of high-dose statin therapy. Furthermore, the results of the YELLOW study are concordant with the known acute benefits of statin therapy in patients presenting with acute coronary syndromes, where the early introduction of these agents is known to be of clinical benefit.13 While the YELLOW study was the first of this nature and the results remain to be replicated in a larger trial, these findings have revived interest in the concept of the “vulnerable plaque” because it appears possible that by causing lipid core reduction over a just few weeks, high-dose statin therapy may have rapid plaque stabilizing effects. We are now embarking on the YELLOW II study, where we will further explore the utility of high-dose rosuvastatin for the early reduction of plaque lipid content and potential mechanistic pathways.

    What other agents might have therapeutic efficacy for lipid core reduction? This question is perhaps more complex than it might first appear, because at the present time we do not know the specific mechanism whereby high-dose rosuvastatin causes lipid reduction in plaques. Theoretically it may be due to reduced LDL, increased HDL, other mechanisms or a combination of these effects. Potentially, other agents that are already available such as bile acid sequestrants, ezetimibe, and fibrates may have a weak lipid core reducing effect. However, we would underscore the fact that at the present time the utility of these agents is speculative, and no other agent (apart from high-dose rosuvastatin in the YELLOW study) has been shown to reduce lipid content in vivo in human plaques. Furthermore, given the fact that these other agents are far less potent in their overall effect than rosuvastatin 40 mg/day, it may be clinically challenging to determine if they have efficacy for lipid core reduction beyond that of statins.

    In addition to pharmacotherapy, it must be remembered that we have several non-pharmacological treatments in our armamentarium that may impact lipid core reduction. For example, exercise is known to be associated with reduced plaque lipid content,14 and proper adherence to current guidelines with respect to lifestyle and diet are of paramount importance in any patient in whom it is considered desirable to reduce plaque lipid content.

    Looking ahead, there are several emerging and investigational agents that may hold promise for lipid core reduction. Microsomal triglyceride transfer protein (MTP) is expressed in the liver, intestine, and the heart and is required for the proper assembly of VLDL and chylomicrons. In animals, treatment with an MTP inhibitor leads to a rapid reduction in plasma lipid levels, with a significant decrease in lipid content and monocyte-derived (CD68+) cells in atherosclerotic plaques.15 On December 21, 2012, the first of the MTP inhibitors was approved for clinical use. Lomitapide (marketed as Juxtapid) was approved by the FDA as an adjunct to a low fat diet and other lipid-lowering treatments for patients with homozygous familial hypercholesterolemia. However, concerns have been raised due to hepatic side effects and liver toxicity. As a result, lomitapide will carry a boxed warning and will only be available through a restricted program.16 Another new drug that was recently given restricted approval in the US for homozygous familial hypercholesterolemia is mipomersen. This agent is an antisense therapeutic that targets messenger RNA for apolipoprotein B, leading to reduced apoB protein and LDL levels. While showing efficacy for lowering LDL,17 safety concerns have thus far prohibited this agent from gaining approval for use in Europe. PCSK9 inhibitors are yet another novel class of agents that may hold promise for reducing lipid core plaque. PCSK9 is involved in the degradation of the LDL receptor (LDLR), and by inhibiting PCSK9 it is believed that this permits more LDL receptors to remain active and participate in LDL removal from the blood, thereby reducing plasma LDL and cholesterol levels. Denis et al18 recently demonstrated that gene inactivation of PCSK9 in mice reduced aortic cholesterol accumulation and atherosclerotic lesion development in atherosclerosis-prone mice. Based on their powerful LDL lowering effect, intense efforts are currently underway to develop clinically efficacious PCSK9 inhibitors with several agents already moving to phase II/III human studies.19 While all of these new and emerging therapies are cause for optimism, the recent experience with CETP-inhibitors and the overall failure of this class so far to stand up to rigorous testing as HDL raising agents in phase III studies20,21 serves to remind us that not all “promising future therapies” survive through the arduous clinical testing pipeline.

    In conclusion, there is renewed interest in the concept of “plaque regression” and pharmacological therapy for “lipid core reduction.” This has been driven by our increasing ability to image and quantify these phenomena, and more recently by the provocative findings that high-dose statin therapy may achieve both of these clinical endpoints. Further studies are now required to evaluate novel agents, define mechanisms of action and, most importantly, to confirm that atherosclerotic lipid core reduction is associated with plaque stabilization and fewer clinical endpoints.

    References, pp. 27A-28A in the Supplement

    1. Kristanto W, van Ooijen PM, Greuter MJ, et al. Non-calcified coronary atherosclerotic plaque visualization on CT: effects of contrast-enhancement and lipid-content fractions. Int J Cardiovasc Imaging. 2013; online ahead of print.

    2. Cassis LA, Lodder RA. Near-IR imaging of atheromas in living arterial tissue. Anal Chem. 1993;65:1247-1256.

    3. Jaross W, Neumeister V, Lattke P, et al. Determination of cholesterol in atherosclerotic plaques using near infrared diffuse reflection spectroscopy. Atherosclerosis. 1999;147:327-337.

    4. Moreno PR, Lodder RA, Purushothaman KR, et al. Detection of lipid pool, thin fibrous cap, and inflammatory cells in human aortic atherosclerotic plaques by near-infrared spectroscopy. Circulation. 2002;105:923-927.

    5. Wang J, Geng YJ, Guo B, et al. Near-infrared spectroscopic characterization of human advanced atherosclerotic plaques. J Am Coll Cardiol. 2002;39:1305-1313.

    6. Waxman S, Dixon SR, L’Allier P, et al. In vivo validation of a catheter-based near-infrared spectroscopy system for detection of lipid core coronary plaques: initial results of the SPECTACL study. JACC Cardiovasc Imaging. 2009;2:858-868.

    7. Goldstein JA, Maini B, Dixon SR, et al. Detection of lipid-core plaques by intracoronary near-infrared spectroscopy identifies high risk of periprocedural myocardial infarction. Circ Cardiovasc Interv. 2011;4:429-437.

    8. Nissen SE, Tuzcu EM, Schoenhagen P, et al. Statin therapy, LDL cholesterol, C-reactive protein, and coronary artery disease. N Engl J Med. 2005;352:29-38.

    9. Nissen SE, Nicholls SJ, Sipahi I, et al. Effect of very high-intensity statin therapy on regression of coronary atherosclerosis: the ASTEROID trial. JAMA. 2006;295:1556-1565.

    10. Nicholls SJ, Ballantyne CM, Barter PJ, et al. Effect of two intensive statin regimens on progression of coronary disease. N Engl J Med. 2011;365:2078-2087.

    11. Varnava AM, Mills PG, Davies MJ. Relationship between coronary artery remodeling and plaque vulnerability. Circulation. 2002;105:939-943.

    12. Kini AS, Baber U, Kovacic JC, et al. Changes in plaque lipid content after short-term, intensive versus standard statin therapy: The YELLOW Trial. J Am Coll Cardiol. 2013;62:21-29.

    13. Hulten E, Jackson JL, Douglas K, et al. The effect of early, intensive statin therapy on acute coronary syndrome: a meta-analysis of randomized controlled trials. Arch Intern Med. 2006;166:1814-1821.

    14. Yoshikawa D, Ishii H, Kurebayashi N, et al. Association of cardiorespiratory fitness with characteristics of coronary plaque: assessment using integrated backscatter intravascular ultrasound and optical coherence tomography. Int J Cardiol. 2013;162:123-128.

    15. Hewing B, Parathath S, Mai CK, et al. Rapid regression of atherosclerosis with MTP inhibitor treatment. Atherosclerosis. 2013;227:125-129.

    16. Cuchel M, Bloedon LT, Szapary PO, et al. Inhibition of microsomal triglyceride transfer protein in familial hypercholesterolemia. N Engl J Med. 2007;356:148-156.

    17. Raal FJ, Santos RD, Blom DJ, et al. Mipomersen, an apolipoprotein B synthesis inhibitor, for lowering of LDL cholesterol concentrations in patients with homozygous familial hypercholesterolaemia: a randomised, double-blind, placebo-controlled trial. Lancet. 2010;375:998-1006.

    18. Denis M, Marcinkiewicz J, Zaid A, et al. Gene inactivation of proprotein convertase subtilisin/kexin type 9 reduces atherosclerosis in mice. Circulation. 2012;125:894-901.

    19. Roth EM, McKenney JM, Hanotin C, et al. Atorvastatin with or without an antibody to PCSK9 in primary hypercholesterolemia. N Engl J Med. 2012;367:1891-1900.

    20. Schwartz GG, Olsson AG, Abt M, et al. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N Engl J Med. 2012;367:2089-2099.

    21. Kovacic JC, Fuster V. From Treating Complex Coronary Artery Disease to Promoting Cardiovascular Health: Therapeutic Transitions and Challenges, 2010-2020. Clin Pharmacol Ther. 2011;90:509-518.



    The Journal of Invasive Cardiology®

    KEY SOURCE for this Article

    Journal of Invasive Cardiology, August 2013, Vol 25/Supplement A

    Print ISSN 1042-3931 / Electronic ISSN 1557-2501


    NIRS-IVUS Imaging To Characterize the Composition and Structure of Coronary Plaques

    D. RIZIK AND J.A. GOLDSTEIN……………………………………..2A


    Imaging of Plaque Composition and Structure with the TVC Imaging System™ and TVC Insight™ Catheter

    B. SHYDO, ET AL…………………………………………………………5A

    Comparative Intravascular Imaging for Lipid Core Plaque: NIRS vs VH-IVUS vs OCT

    E. FUH AND E.S. BRILAKIS……………………………………………9A

    Plaque Characterization and PCI Procedural Outcomes

    NIRS-IVUS Imaging Identifies Lesions at High Risk of

    Peri-Procedural Myocardial Infarction

    J.A. GOLDSTEIN, ET AL……………………………………………..14A

    Case Vignettes:

    Multiple Plaque Ruptures in a Patient with ST-Segment Elevation Myocardial Infarction: Does Infrared Spectroscopy Evidence Explain a Significant Change in the Angiogram?

    M.J. LIM AND J.M. STOLKER……………………………………….16A

    Missing the Culprit Yellow Plaque

    D. ERLINGE…………………………………………………………….18A

    The Use of Near-Infrared Spectroscopy to Optimize Stent Length

    G.A. STOUFFER ………………………………………………………19A

    Employing NIRS-IVUS to Guide Optimal Lesion Coverage—Avoidance of Geographic Miss

    I. HANSON, ET AL……………………………………………………..20A

    Peri-Procedural Myocardial Injury Unraveled: Combined

    Assessment by Optical Coherence Tomography, Near-Infrared

    Spectroscopy, and IVUS

    A. KARANASOS, ET AL………………………………………………..22A

    Plaque Characterization and Long-Term 

    Clinical Outcomes

    Long-term Consequences of a Lipid Core Plaque

    C.V. BOURANTAS, ET AL…………………………………………….24A

    Pharmacological Therapy of Lipid Core Plaque

    J.C. KOVACIC AND A. KINI………………………………………….27A

    The Search for Vulnerable Plaque — The Pace Quickens

    R.D. MADDER, ET AL…………………………………………………29A

    Case Vignettes:

    Observations from Intracoronary Near-Infrared Spectroscopy in Patients with ST-Segment Elevation Myocardial Infarction

    R.D. MADDER…………………………………………………………34A

    NIRS Imaging of Cardiac Allograft Vasculopathy

    G. WEISZ ……………………………………………………………….35A

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Imaging of Non-tumorous and Tumorous Human Brain Tissues

Reporter and Curator: Dror Nir, PhD

The point of interest in the article I feature below is that it represents a potential building block in a future system that will use full-field optical coherence tomography during brain surgery to improve the accuracy of cancer lesions resection. The article is featuring promising results for differentiating tumor from normal brain tissue in large samples (order of 1–3 cm2) by offering images with spatial resolution comparable to histological analysis, sufficient to distinguish microstructures of the human brain parenchyma.  Easy to say, and hard to make…:) –> Intraoperative apparatus to guide the surgeon in real time during resection of brain tumors.


Imaging of non-tumorous and tumorous human brain tissues with full-field optical coherence tomography 

Open Access Article

Osnath Assayaga1Kate Grievea1Bertrand DevauxbcFabrice HarmsaJohan Palludbc,Fabrice ChretienbcClaude BoccaraaPascale Varletbc;  a Inserm U979 “Wave Physics For Medicine” ESPCI -ParisTech – Institut Langevin, 1 rue Jussieu, 75005, b France, Centre Hospitalier Sainte-Anne, 1 rue Cabanis 75014 Paris, France

c University Paris Descartes, France.


A prospective study was performed on neurosurgical samples from 18 patients to evaluate the use of full-field optical coherence tomography (FF-OCT) in brain tumor diagnosis.

FF-OCT captures en face slices of tissue samples at 1 μm resolution in 3D to a penetration depth of around 200 μm. A 1 cm2 specimen is scanned at a single depth and processed in about 5 min. This rapid imaging process is non-invasive and requires neither contrast agent injection nor tissue preparation, which makes it particularly well suited to medical imaging applications.

Temporal chronic epileptic parenchyma and brain tumors such as meningiomas, low-grade and high-grade gliomas, and choroid plexus papilloma were imaged. A subpopulation of neurons, myelin fibers and CNS vasculature were clearly identified. Cortex could be discriminated from white matter, but individual glial cells such as astrocytes (normal or reactive) or oligodendrocytes were not observable.

This study reports for the first time on the feasibility of using FF-OCT in a real-time manner as a label-free non-invasive imaging technique in an intraoperative neurosurgical clinical setting to assess tumorous glial and epileptic margins.


  • FF-OCT, full field optical coherence tomography;
  • OCT, optical coherence tomography


Optical imaging; Digital pathology; Brain imaging; Brain tumor; Glioma

1. Introduction

1.1. Primary CNS tumors

Primary central nervous system (CNS) tumors represent a heterogeneous group of tumors with benign, malignant and slow-growing evolution. In France, 5000 new cases of primary CNS tumors are detected annually (Rigau et al., 2011). Despite considerable progress in diagnosis and treatment, the survival rate following a malignant brain tumor remains low and 3000 deaths are reported annually from CNS tumors in France (INCa, 2011). Overall survival from brain tumors depends on the complete resection of the tumor mass, as identified through postoperative imaging, associated with updated adjuvant radiation therapy and chemotherapy regimen for malignant tumors (Soffietti et al., 2010). Therefore, there is a need to evaluate the completeness of the tumor resection at the end of the surgical procedure, as well as to identify the different components of the tumor interoperatively, i.e. tumor tissue, necrosis, infiltrated parenchyma (Kelly et al., 1987). In particular, the persistence of non-visible tumorous tissue or isolated tumor cells infiltrating brain parenchyma may lead to additional resection.

For low-grade tumors located close to eloquent brain areas, a maximally safe resection that spares functional tissue warrants the current use of intraoperative techniques that guide a more complete tumor resection. During awake surgery, speech or fine motor skills are monitored, while cortical and subcortical stimulations are performed to identify functional areas (Sanai et al., 2008). Intraoperative MRI provides images of the surgical site as well as tomographic images of the whole brain that are sufficient for an approximate evaluation of the abnormal excised tissue, but offers low resolution (typically 1 to 1.5 mm) and produces artifacts at the air-tissue boundary of the surgical site.

Histological and immunohistochemical analyses of neurosurgical samples remain the current gold standard method used to analyze tumorous tissue due to advantages of sub-cellular level resolution and high contrast. However, these methods require lengthy (12 to 72 h), complex multiple steps, and use of carcinogenic chemical products that would not be technically possible intra-operatively. In addition, the number of histological slides that can be reviewed and analyzed by a pathologist is limited, and it defines the number and size of sampled locations on the tumor, or the surrounding tissue.

To obtain histology-like information in a short time period, intraoperative cytological smear tests are performed. However tissue architecture information is thereby lost and the analysis is carried out on only a limited area of the sample (1 mm × 1 mm).

Intraoperative optical imaging techniques are recently developed high resolution imaging modalities that may help the surgeon to identify the persistence of tumor tissue at the resection boundaries. Using a conventional operating microscope with Xenon lamp illumination gives an overall view of the surgical site, but performance is limited by the poor discriminative capacity of the white light illumination at the surgical site interface. Better discrimination between normal and tumorous tissues has been obtained using fluorescence properties of tumor cells labeled with preoperatively administered 5-ALA. Tumor tissue shows a strong ALA-induced PPIX fluorescence at 635 nm and 704 nm when the operative field is illuminated with a 440 nm-filtered lamp. More complete resections of high-grade gliomas have been demonstrated using 5-ALA fluorescence guidance (Stummer et al., 2000), however brain parenchyma infiltrated by isolated tumor cells is not fluorescent, reducing the interest of this technique when resecting low-grade gliomas.

Refinement of this induced fluorescence technique has been achieved using a confocal microscope and intraoperative injection of sodium fluorescein. A 488 nm laser illuminates the operative field and tissue contact analysis is performed using a handheld surgical probe (field of view less than 0.5 × 0.5 mm) which scans the fluorescence of the surgical interface at the 505–585 nm band. Fluorescent isolated tumor cells are clearly identified at depths from 0 to 500 μm from the resection border (Sanai et al., 2011), demonstrating the potential of this technique in low-grade glioma resection.

Reviewing the state-of-the-art, a need is identified for a quick and reliable method of providing the neurosurgeon with architectural and cellular information without the need for injection or oral intake of exogenous markers in order to guide the neurosurgeon and optimize surgical resections.

1.2. Full-field optical coherence tomography

Introduced in the early 1990s (Huang et al., 1991), optical coherence tomography (OCT) uses interference to precisely locate light deep inside tissue. The photons coming from the small volume of interest are distinguished from light scattered by the other parts of the sample by the use of an interferometer and a light source with short coherence length. Only the portion of light with the same path length as the reference arm of the interferometer, to within the coherence length of the source (typically a few μm), will produce interference. A two-dimensional B-scan image is captured by scanning. Recently, the technique has been improved, mainly in terms of speed and sensitivity, through spectral encoding (De Boer et al., 2003Leitgeb et al., 2003 and Wojtkowski et al., 2002).

A recent OCT technique called full-field optical coherence tomography (FF-OCT) enables both a large field of view and high resolution over the full field of observation (Dubois et al., 2002 and Dubois et al., 2004). This allows navigation across the wide field image to follow the morphology at different scales and different positions. FF-OCT uses a simple halogen or light-emitting diode (LED) light source for full field illumination, rather than lasers and point-by-point scanning components required for conventional OCT. The illumination level is low enough to maintain the sample integrity: the power incident on the sample is less than 1 mW/mm2 using deep red and near infrared light. FF-OCT provides the highest OCT 3D resolution of 1.5 × 1.5 × 1 μm3 (X × Y × Z) on unprepared label-free tissue samples down to depths of approximately 200 μm–300 μm (tissue-dependent) over a wide field of view that allows digital zooming down to the cellular level. Interestingly, it produces en face images in the native field view (rather than the cross-sectional images of conventional OCT), which mimic the histology process, thereby facilitating the reading of images by pathologists. Moreover, as for conventional OCT, it does not require tissue slicing or modification of any kind (i.e. no tissue fixation, coloration, freezing or paraffin embedding). FF-OCT image acquisition and processing time is less than 5 min for a typical 1 cm2 sample (Assayag et al., in press) and the imaging performance has been shown to be equivalent in fresh or fixed tissue (Assayag et al., in press and Dalimier and Salomon, 2012). In addition, FF-OCT intrinsically provides digital images suitable for telemedicine.

Numerous studies have been published over the past two decades demonstrating the suitability of OCT for in vivo or ex vivo diagnosis. OCT imaging has been previously applied in a variety of tissues such as the eye (Grieve et al., 2004 and Swanson et al., 1993), upper aerodigestive tract (Betz et al., 2008Chen et al., 2007 and Ozawa et al., 2009), gastrointestinal tract (Tearney et al., 1998), and breast tissue and lymph nodes (Adie and Boppart, 2009Boppart et al., 2004Hsiung et al., 2007Luo et al., 2005Nguyen et al., 2009Zhou et al., 2010 and Zysk and Boppart, 2006).

In the CNS, published studies that evaluate OCT (Bizheva et al., 2005Böhringer et al., 2006Böhringer et al., 2009Boppart, 2003 and Boppart et al., 1998) using time-domain (TD) or spectral domain (SD) OCT systems had insufficient resolution (10 to 15 μm axial) for visualization of fine morphological details. A study of 9 patients with gliomas carried out using a TD-OCT system led to classification of the samples as malignant versus benign (Böhringer et al., 2009). However, the differentiation of tissues was achieved by considering the relative attenuation of the signal returning from the tumorous zones in relation to that returning from healthy zones. The classification was not possible by real recognition of CNS microscopic structures. Another study showed images of brain microstructures obtained with an OCT system equipped with an ultra-fast laser that offered axial and lateral resolution of 1.3 μm and 3 μm respectively (Bizheva et al., 2005). In this way, it was possible to differentiate malignant from healthy tissue by the presence of blood vessels, microcalcifications and cysts in the tumorous tissue. However the images obtained were small (2 mm × 1 mm), captured on fixed tissue only and required use of an expensive large laser thereby limiting the possibility for clinical implementation.

Other studies have focused on animal brain. In rat brain in vivo, it has been shown that optical coherence microscopy (OCM) can reveal neuronal cell bodies and myelin fibers (Srinivasan et al., 2012), while FF-OCT can also reveal myelin fibers (Ben Arous et al., 2011), and movement of red blood cells in vessels (Binding et al., 2011).

En face images captured with confocal reflectance microscopy can closely resemble FF-OCT images. For example, a prototype system used by Wirth et al. (2012) achieves lateral and axial resolution of 0.9 μm and 3 μm respectively. However small field size prevents viewing of wide-field architecture and slow acquisition speed prohibits the implementation of mosaicking. In addition, the poorer axial resolution and lower penetration depth of confocal imaging in comparison to FF-OCT limit the ability to reconstruct cross-sections from the confocal image stack.

This study is the first to analyze non-tumorous and tumorous human brain tissue samples using FF-OCT.

2. Materials and methods

2.1. Instrument

The experimental arrangement of FF-OCT (Fig. 1A) is based on a configuration that is referred to as a Linnik interferometer (Dubois et al., 2002). A halogen lamp is used as a spatially incoherent source to illuminate the full field of an immersion microscope objective at a central wavelength of 700 nm, with spectral width of 125 nm. The signal is extracted from the background of incoherent backscattered light using a phase-shifting method implemented in custom-designed software. This study was performed on a commercial FF-OCT system (LightCT, LLTech, France).


Fig 1

Capturing “en face” images allows easy comparison with histological sections. The resolution, pixel number and sampling requirements result in a native field of view that is limited to about 1 mm2. The sample is moved on a high precision mechanical platform and a number of fields are stitched together (Beck et al., 2000) to display a significant field of view. The FF-OCT microscope is housed in a compact setup (Fig. 1B) that is about the size of a standard optical microscope (310 × 310 × 800 mm L × W × H).

2.2. Imaging protocol

All images presented in this study were captured on fresh brain tissue samples from patients operated on at the Neurosurgery Department of Sainte-Anne Hospital, Paris. Informed and written consent was obtained in all cases following the standard procedure at Sainte-Anne Hospital from patients who were undergoing surgical intervention. Fresh samples were collected from the operating theater immediately after resection and sent to the pathology department. A pathologist dissected each sample to obtain a 1–2 cm2 piece and made a macroscopic observation to orientate the specimen in order to decide which side to image. The sample was immersed in physiological serum, placed in a cassette, numbered, and brought to the FF-OCT imaging facility in a nearby laboratory (15 min distant) where the FF-OCT images were captured. The sample was placed in a custom holder with a coverslip on top (Fig. 1C, D). The sample was raised on a piston to rest gently against the coverslip in order to flatten the surface and so optimize the image capture. The sample is automatically scanned under a 10 × 0.3 numerical aperture (NA) immersion microscope objective. The immersion medium is a silicone oil of refractive index close to that of water, chosen to optimize index matching and slow evaporation. The entire area of each sample was imaged at a depth of 20 μm beneath the sample surface. This depth has been reported to be optimal for comparison of FF-OCT images to histology images in a previous study on breast tissue (Assayag et al., in press). There are several reasons for the choice of imaging depth: firstly, histology was also performed at approximately 20 μm from the edge of the block, i.e. the depth at which typically the whole tissue surface begins to be revealed. Secondly, FF-OCT signal is attenuated with depth due to multiple scattering in the tissue, and resolution is degraded with depth due to aberrations. The best FF-OCT images are therefore captured close to the surface, and the best matching is achieved by attempting to image at a similar depth as the slice in the paraffin block. It was also possible to capture image stacks down to several hundred μm in depth (where penetration depth is dependent on tissue type), for the purpose of reconstructing a 3D volume and imaging layers of neurons and myelin fibers. An example of such a stack in the cerebellum is shown as a video (Video 2) in supplementary material. Once FF-OCT imaging was done, each sample was immediately fixed in formaldehyde and returned to the pathology department where it underwent standard processing in order to compare the FF-OCT images to histology slides.

2.3. Matching FF-OCT to histology

The intention in all cases was to match as closely as possible to histology. FF-OCT images were captured 20 μm below the surface. Histology slices were captured 20 μm from the edge of the block. However the angle of the inclusion is hard to control and so some difference in the angle of the plane always exists when attempting matching. Various other factors that can cause differences stem from the histology process — fixing, dehydrating, paraffin inclusion etc. all alter the tissue and so precise correspondence can be challenging. Such difficulties are common in attempting to match histology to other imaging modalities (e.g. FF-OCT Assayag et al., in press; OCT Bizheva et al., 2005; confocal microscopy Wirth et al., 2012).

An additional parameter in the matching process is the slice thickness. Histology slides were 4 μm in thickness while FF-OCT optical slices have a 1 μm thickness. The finer slice of the FF-OCT image meant that lower cell densities were perceived on the FF-OCT images (in those cases where individual cells were seen, e.g. neurons in the cortex). This difference in slice thickness affects the accuracy of the FF-OCT to histology match. In order to improve matching, it would have been possible to capture four FF-OCT slices in 1 μm steps and sum the images to mimic the histology thickness. However, this would effectively degrade the resolution, which was deemed undesirable in evaluating the capacities of the FF-OCT method.

3. Results

18 samples from 18 adult patients (4 males, 14 females) of age range 19–81 years have been included in the study: 1 mesial temporal lobe epilepsy and 1 cerebellum adjacent to a pulmonary adenocarcinoma metastasis (serving as the non-tumor brain samples), 7 diffuse supratentorial gliomas (4 WHO grade II, 3 WHO grade III), 5 meningiomas, 1 hemangiopericytoma, and 1 choroid plexus papilloma. Patient characteristics are detailed in Table 1.


Table 1

3.1. FF-OCT imaging identifies myelinated axon fibers, neuronal cell bodies and vasculature in the human epileptic brain and cerebellum

The cortex and the white matter are clearly distinguished from one another (Fig. 2). Indeed, a subpopulation of neuronal cell bodies (Fig. 2B, C) as well as myelinated axon bundles leading to the white matter could be recognized (Fig. 2D, E). Neuronal cell bodies appear as dark triangles (Fig. 2C) in relation to the bright surrounding myelinated environment. The FF-OCT signal is produced by backscattered photons from tissues of differing refractive indices. The number of photons backscattered from the nuclei in neurons appears to be too few to produce a signal that allows their differentiation from the cytoplasm, and therefore the whole of the cell body (nucleus plus cytoplasm) appears dark.

Fig 2


Myelinated axons are numerous, well discernible as small fascicles and appear as bright white lines (Fig. 2E). As the cortex does not contain many myelinated axons, it appears dark gray. Brain vasculature is visible (Fig. 2F and G), and small vessels are distinguished by a thin collagen membrane that appears light gray. Video 1 in supplementary material shows a movie composed of a series of en face 1 μm thick optical slices captured over 100 μm into the depth of the cortex tissue. The myelin fibers and neuronal cell bodies are seen in successive layers.

The different regions of the human hippocampal formation are easily recognizable (Fig. 3). Indeed, CA1 field and its stratum radiatum, CA4 field, the hippocampal fissure, the dentate gyrus, and the alveus are easily distinguishable. Other structures become visible by zooming in digitally on the FF-OCT image. The large pyramidal neurons of the CA4 field (Fig. 3B) and the granule cells that constitute the stratum granulosum of the dentate gyrus are visible, as black triangles and as small round dots, respectively (Fig. 3D).


Fig 3

In the normal cerebellum, the lamellar or foliar pattern of alternating cortex and central white matter is easily observed (Fig. 4A). By digital zooming, Purkinje and granular neurons also appear as black triangles or dots, respectively (Fig. 4C), and myelinated axons are visible as bright white lines (Fig. 4E). Video 2 in supplementary material shows a fly-through movie in the reconstructed axial slice orientation of a cortex region in cerebellum. The Purkinje and granular neurons are visible down to depths of 200 μm in the tissue.


Fig 4

3.2. FF-OCT images distinguish meningiomas from hemangiopericytoma in meningeal tumors

The classic morphological features of a meningioma are visible on the FF-OCT image: large lobules of tumorous cells appear in light gray (Fig. 5A), demarcated by collagen-rich bundles (Fig. 5B) which are highly scattering and appear a brilliant white in the FF-OCT images. The classic concentric tumorous cell clusters (whorls) are very clearly distinguished on the FF-OCT image (Fig. 5D). In addition the presence of numerous cell whorls with central calcifications (psammoma bodies) is revealed (Fig. 5F). Collagen balls appear bright white on the FF-OCT image (Fig. 5H). As the collagen balls progressively calcify, they are consumed by the black of the calcified area, generating a target-like image (Fig. 5H). Calcifications appear black in FF-OCT as they are crystalline and so allow no penetration of photons to their interior.

Fig 5

Mesenchymal non-meningothelial tumors such as hemangiopericytomas represent a classic differential diagnosis of meningiomas. In FF-OCT, the hemangiopericytoma is more monotonous in appearance than the meningiomas, with a highly vascular branching component with staghorn-type vessels (Fig. 6A, C).

Fig 6

3.3. FF-OCT images identify choroid plexus papilloma

The choroid plexus papilloma appears as an irregular coalescence of multiple papillas composed of elongated fibrovascular axes covered by a single layer of choroid glial cells (Fig. 7). By zooming in on an edematous papilla, the axis appears as a black structure covered by a regular light gray line (Fig. 7B). If the papilla central axis is hemorrhagic, the fine regular single layer is not distinguishable (Fig. 7C). Additional digital zooming in on the image reveals cellular level information, and some nuclei of plexus choroid cells can be recognized. However, cellular atypia and mitosis are not visible. These represent key diagnosis criteria used to differentiate choroid plexus papilloma (grade I) from atypical plexus papilloma (grade II).

Fig 7

3.4. FF-OCT images detect the brain tissue architecture modifications generated by diffusely infiltrative gliomas

Contrary to the choroid plexus papillomas which have a very distinctive architecture in histology (cauliflower-like aspect), very easily recognized in the FF-OCT images (Fig. 7A to G), diffusely infiltrating glioma does not present a specific tumor architecture (Fig. 8) as they diffusely permeate the normal brain architecture. Hence, the tumorous glial cells are largely dispersed through a nearly normal brain parenchyma (Fig. 8E). The presence of infiltrating tumorous glial cells attested by high magnification histological observation (irregular atypical cell nuclei compared to normal oligodendrocytes) is not detectable with the current generation of FF-OCT devices, as FF-OCT cannot reliably distinguish the individual cell nuclei due to lack of contrast (as opposed to lack of resolution). In our experience, diffuse low-grade gliomas (less than 20% of tumor cell density) are mistaken for normal brain tissue on FF-OCT images. However, in high-grade gliomas (Fig. 8G–K), the infiltration of the tumor has occurred to such an extent that the normal parenchyma architecture is lost. This architectural change is easily observed in FF-OCT and is successfully identified as high-grade glioma, even though the individual glial cell nuclei are not distinguished.

Fig 8

4. Discussion

We present here the first large size images (i.e. on the order of 1–3 cm2) acquired using an OCT system that offer spatial resolution comparable to histological analysis, sufficient to distinguish microstructures of the human brain parenchyma.

Firstly, the FF-OCT technique and the images presented here combine several practical advantages. The imaging system is compact, it can be placed in the operating room, the tissue sample does not require preparation and image acquisition is rapid. This technique thus appears promising as an intraoperative tool to help neurosurgeons and pathologists.

Secondly, resolution is sufficient (on the order of 1 μm axial and lateral) to distinguish brain tissue microstructures. Indeed, it was possible to distinguish neuron cell bodies in the cortex and axon bundles going towards white matter. Individual myelin fibers of 1 μm in diameter are visible on the FF-OCT images. Thus FF-OCT may serve as a real-time anatomical locator.

Histological architectural characteristics of meningothelial, fibrous, transitional and psammomatous meningiomas were easily recognizable on the FF-OCT images (lobules and whorl formation, collagenous-septae, calcified psammoma bodies, thick vessels). Psammomatous and transitional meningiomas presented distinct architectural characteristics in FF-OCT images in comparison to those observed in hemangiopericytoma. Thus, FF-OCT may serve as an intraoperative tool, in addition to extemporaneous examination, to refine differential diagnosis between pathological entities with different prognoses and surgical managements.

Diffuse glioma was essentially recognized by the loss of normal parenchyma architecture. However, glioma could be detected on FF-OCT images only if the glial cell density is greater than around 20% (i.e. the point at which the effect on the architecture becomes noticeable). The FF-OCT technique is therefore not currently suitable for the evaluation of low tumorous infiltration or tumorous margins. Evaluation at the individual tumor cell level is only possible by IDH1R132 immunostaining in IDH1 mutated gliomas in adults (Preusser et al., 2011). One of the current limitations of the FF-OCT technique for use in diagnosis is the difficulty in estimating the nuclear/cytoplasmic boundaries and the size and form of nuclei as well as the nuclear-cytoplasmic ratio of cells. This prevents precise classification into tumor subtypes and grades.

To increase the accuracy of diagnosis of tumors where cell density measurement is necessary for grading, perspectives for the technique include development of a multimodal system (Harms et al., 2012) to allow simultaneous co-localized acquisition of FF-OCT and fluorescence images. The fluorescence channel images in this multimodal system show cell nuclei, which increase the possibility of diagnosis and tumor grading direct from optical images. However, the use of contrast agents for the fluorescence channel means that the multimodal imaging technique is no longer non-invasive, and this may be undesirable if the tissue is to progress to histology following optical imaging. This is a similar concern in confocal microscopy where use of dyes is necessary for fluorescence detection (Wirth et al., 2012).

In its current form therefore, FF-OCT is not intended to serve as a diagnostic tool, but should rather be considered as an additional intraoperative aid in order to determine in a short time whether or not there is suspicious tissue present in a sample. It does not aim to replace histological analyses but rather to complement them, by offering a tool at the intermediary stage of intraoperative tissue selection. In a few minutes, an image is produced that allows the surgeon or the pathologist to assess the content of the tissue sample. The selected tissue, once imaged with FF-OCT, may then proceed to conventional histology processing in order to obtain the full diagnosis (Assayag et al., in press and Dalimier and Salomon, 2012).

Development of FF-OCT to allow in vivo imaging is underway, and first steps include increasing camera acquisition speed. First results of in vivo rat brain imaging have been achieved with an FF-OCT prototype setup, and show real-time visualization of myelin fibers (Ben Arous et al., 2011) and movement of red blood cells in vessels (Binding et al., 2011). To respond more precisely to surgical needs, it would be preferable to integrate the FF-OCT system into a surgical probe. Work in this direction is currently underway and preliminary images of skin and breast tissue have been captured with a rigid probe FF-OCT prototype (Latrive and Boccara, 2011).

In conclusion, we have demonstrated the capacity of FF-OCT for imaging of human brain samples. This technique has potential as an intraoperative tool for determining tissue architecture and content in a few minutes. The 1 μm3 resolution and wide-field down to cellular-level views offered by the technique allowed identification of features of non-tumorous and tumorous tissues such as myelin fibers, neurons, microcalcifications, tumor cells, microcysts, and blood vessels. Correspondence with histological slides was good, indicating suitability of the technique for use in clinical practice for tissue selection for biobanking for example. Future work to extend the technique to in vivo imaging by rigid probe endoscopy is underway.

The following are the supplementary data related to this article.

Video 1.  Shows a movie composed of a series of en face 1 μm thick optical slices captured over 100 μm into the depth of the cortex tissue. The myelin fibers and neuronal cell bodies are seen in successive layers. Field size is 800 μm × 800 μm.

Video 2.  Shows a fly-through movie in the reconstructed cross-sectional orientation showing 1 μm steps through a 3D stack down to 200 μm depth in cerebellum cortical tissue. Purkinje and granular neurons are visible as dark spaces. Field size is 800 μm × 200 μm.


The authors wish to thank LLTech SAS for use of the LightCT Scanner.



Adie and Boppart, 2009

Adie, Boppart

Optical Coherence Tomography for Cancer Detection

SpringerLink (2009), pp. 209–250

Assayag et al., in press

Assayag et al.

Large field, high resolution full field optical coherence tomography: a pre-clinical study of human breast tissue and cancer assessment

Technology in Cancer Research & Treatment TCRT Express, 1 (1) (2013), p. e600254http://dx.doi.org/10.7785/tcrtexpress.2013.600254

Beck et al., 2000

Beck et al.

Computer-assisted visualizations of neural networks: expanding the field of view using seamless confocal montaging

Journal of Neuroscience Methods, 98 (2) (2000), pp. 155–163

Ben Arous et al., 2011

Ben Arous et al.

Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy

Journal of Biomedical Optics, 16 (11) (2011), p. 116012

Full Text via CrossRef

Betz et al., 2008

C.S. Betz et al.

A set of optical techniques for improving the diagnosis of early upper aerodigestive tract cancer

Medical Laser Application, 23 (2008), pp. 175–185

Binding et al., 2011

Binding et al.

Brain refractive index measured in vivo with high-NA defocus-corrected full-field OCT and consequences for two-photon microscopy

Optics Express, 19 (6) (2011), pp. 4833–4847

Bizheva et al., 2005

Bizheva et al.

Imaging ex vivo healthy and pathological human brain tissue with ultra-high-resolution optical coherence tomography

Journal of Biomedical Optics, 10 (2005), p. 011006 http://dx.doi.org/10.1117/1.1851513

Böhringer et al., 2006

Böhringer et al.

Time domain and spectral domain optical coherence tomography in the analysis of brain tumor tissue

Lasers in Surgery and Medicine, 38 (2006), pp. 588–597 http://dx.doi.org/10.1002/lsm.20353

Böhringer et al., 2009

Böhringer et al.

Imaging of human brain tumor tissue by near-infrared laser coherence tomography

Acta Neurochirurgica, 151 (2009), pp. 507–517 http://dx.doi.org/10.1007/s00701-009-0248-y

Boppart, 2003


Optical coherence tomography: technology and applications for neuroimaging

Psychophysiology, 40 (2003), pp. 529–541 http://dx.doi.org/10.1111/1469-8986.00055

Boppart et al., 1998

Boppart et al.

Optical coherence tomography for neurosurgical imaging of human intracortical melanoma

Neurosurgery, 43 (1998), pp. 834–841 http://dx.doi.org/10.1097/00006123-199810000-00068

Boppart et al., 2004

Boppart et al.

Optical coherence tomography: feasibility for basic research and image-guided surgery of breast cancer

Breast Cancer Research and Treatment, 84 (2004), pp. 85–97

Chen et al., 2007

Chen et al.

Ultrahigh resolution optical coherence tomography of Barrett’s esophagus: preliminary descriptive clinical study correlating images with histology

Endoscopy, 39 (2007), pp. 599–605

Dalimier and Salomon, 2012

Dalimier, Salomon

Full-field optical coherence tomography: a new technology for 3D high-resolution skin imaging

Dermatology, 224 (2012), pp. 84–92 http://dx.doi.org/10.1159/000337423

De Boer et al., 2003

De Boer et al.

Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography

Optics Letters, 28 (2003), pp. 2067–2069

Dubois et al., 2002

Dubois et al.

High-resolution full-field optical coherence tomography with a Linnik microscope

Applied Optics, 41 (4) (2002), p. 805

Dubois et al., 2004

Dubois et al.

Ultrahigh-resolution full-field optical coherence tomography

Applied Optics, 43 (14) (2004), p. 2874

Grieve et al., 2004

Grieve et al.

Ocular tissue imaging using ultrahigh-resolution, full-field optical coherence tomography

Investigative Ophthalmology & Visual Science, 45 (2004), pp. 4126-3–4131

Harms et al., 2012

Harms et al.

Multimodal full-field optical coherence tomography on biological tissue: toward all optical digital pathology

Proc. SPIE 2011, Multimodal Biomedical Imaging VII, 8216 (2012)

Hsiung et al., 2007

Hsiung et al.

Benign and malignant lesions in the human breast depicted with ultrahigh resolution and three-dimensional optical coherence tomography

Radiology, 244 (2007), pp. 865–874

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Virtual Biopsy – is it possible?

Author and Curator: Dror Nir, PhD

In a remark made to my last post: New envelopment in measuring mechanical properties of tissue, Dr. Aviva Lev-Ari, PhD, RN, Director and Founder of our Open Access Online Scientific Journal:  Leaders of Pharmaceutical Business Intelligence, asked whether OCT can be used for the purpose of performing biopsy. My answer to her question was “YES”. I thought that it will be worthwhile explaining why I am so “optimistic” about this:

A conventional biopsy is a process where a tissue sample is being cut out of the body and after being subjected to all kind of chemical processes a thin-film of tissue is trimmed and read under the microscope by a trained pathologist. Can imaging provide histological assessment of “thin-film” of tissue without cutting it out of the body? The answer would be positive if the imaging will result with high resolution reconstruction of a tissue sample identical in quality to a “live-sample” that is put under the microscope.

I was happy to find support to my optimism regarding the feasibility of constructing such device in the following article: Virtual skin biopsy by optical coherence tomography: the first quantitative imaging biomarker for scleroderma published on February 20th 2013 in Ann Rheum Dis doi:10.1136/annrheumdis-2012-202682

 This article reports an original, first study to perform histological comparison and explore Optical coherence tomography (“OCT”) as a potential imaging technique for the clinical assessment of patients presenting with systemic sclerosis (“SSc”). In their study the investigators used a device emitting low-intensity infrared laser beam, capable of producing high-contrast images of skin up to 2 mm deep with resolutions of 4–10 μm.




Skin involvement is of major prognostic value in systemic sclerosis (SSc) and often the primary outcome in clinical trials. Nevertheless, an objective, validated biomarker of skin fibrosis is lacking. Optical coherence tomography (OCT) is an imaging technology providing high-contrast images with 4 μm resolution, comparable with microscopy (‘virtual biopsy’). The present study evaluated OCT to detect and quantify skin fibrosis in SSc.


We performed 458 OCT scans of hands and forearms on 21 SSc patients and 22 healthy controls. We compared the findings with histology from three skin biopsies and by correlation with clinical assessment of the skin. We calculated the optical density (OD) of the OCT images employing Matlab software and performed statistical analysis of the results, including intraobserver/ interobserver reliability, employing SPSS software.


Comparison of OCT images with skin histology indicated a progressive loss of visualisation of the dermal–epidermal junction associated with dermal fibrosis. Furthermore, SSc affected skin showed a consistent decrease of OD in the papillary dermis, progressively worse in patients with worse modified Rodnan skin score (p<0.0001). Additionally, clinically unaffected skin was also distinguishable from healthy skin for its specific pattern of OD decrease in the reticular dermis (p<0.001). The technique showed an excellent intraobserver and interobserver reliability (intraclass correlation coefficient >0.8).


OCT of the skin could offer a feasible and reliable quantitative outcome measure in SSc. Studies determining OCT sensitivity to change over time and its role in defining skin vasculopathy may pave the way to defining OCT as a valuable imaging biomarker in SSc.

Virtual skin biopsy by OCT

The OCT images acquisition allowed the reconstruction of a virtual skin biopsy measuring 4×0.4×2 mm. The main structure of the healthy skin was easily recognisable by OCT (figure 1).

Virtual biopsy of forearm skin by optical coherence tomography. Representative 3D reconstruction from the tomography of healthy and systemic sclerosis (SSc) (site modified Rodnan skin score=3) skin scans. The keratin of the skin appears as a white line on the surface (k). The epidermis (ED) is quite visible in the healthy skin by the contrast with the increased optical density of the papillary dermis (PD). The dermal– epidermal junction (DEJ) is quite visible in the healthy skin between the ED and PD. On the contrary, neither clear distinction of ED and PD or DEJ is appreciable in the SSc skin. The vessels (*) are numerous and very well recognisable in healthy skin, whereas they appear less numerous and less distinct in the OCT image of SSc skin. Total depth of 3D reconstruction=1.2 mm. Scale bars are calculated by ImageJ.

Virtual biopsy of forearm skin by optical coherence tomography. Representative 3D reconstruction from the tomography of healthy and systemic sclerosis (SSc) (site modified Rodnan skin score=3) skin scans. The keratin of the skin appears as a white line on the surface (k). The epidermis (ED) is quite visible in the healthy skin by the contrast with the increased optical density of the papillary dermis (PD). The dermal– epidermal junction (DEJ) is quite visible in the healthy skin between the ED and PD. On the contrary, neither clear distinction of ED and PD or DEJ is appreciable in the SSc skin. The vessels (*) are numerous and very well recognisable in healthy skin, whereas they appear less numerous and less distinct in the OCT image of SSc skin. Total depth of 3D reconstruction=1.2 mm. Scale bars are calculated by ImageJ.

Some quantitative results  – in images:

Validation of optical coherence tomography (OCT) images by histology. (A and B) H&E staining (A) and corresponding OCT scan (B) from a healthy control (HC). The green line is the mean A-scan of the entire OCT image (100 scans) overlaid by matching the scale bars of OCT and histology. The green arrow indicates the nadir of the valley in the mean A-scan, which corresponds to the dermal–epidermal junction clearly visible on both images. The green arrowhead indicates the second peak of the mean OCT A-Scan which corresponds by the overlay to the most superficial region of the papillary dermis. (C and D) H&E staining (C) and corresponding OCT scan (D) from a systemic sclerosis (SSc) patient (site modified Rodnan skin score =3). The red line is the mean A-scan of the OCT image, overlaid by matching the scale bars in the two panels. The red arrow indicates the nadir in the valley of the mean A-scan, which in this case does not correspond to the dermal–epidermal junction. The red arrowhead corresponds to the second peak in mean A-Scan. (E) Overlay of HC and SSc. Scale bar=240 μm.

Validation of optical coherence tomography (OCT) images by histology. (A and B) H&E staining (A) and corresponding OCT scan (B) from a healthy control (HC). The green line is the mean A-scan of the entire OCT image (100 scans) overlaid by matching the scale bars of OCT and histology. The green arrow indicates the nadir of the valley in the mean A-scan, which corresponds to the dermal–epidermal junction clearly visible on both images. The green arrowhead indicates the second peak of the mean OCT A-Scan which corresponds by the overlay to the most superficial region of the papillary dermis. (C and D) H&E staining (C) and corresponding OCT scan (D) from a systemic sclerosis (SSc) patient (site modified Rodnan skin score =3). The red line is the mean A-scan of the OCT image, overlaid by matching the scale bars in the two panels. The red arrow indicates the nadir in the valley of the mean A-scan, which in this case does not correspond to the dermal–epidermal junction. The red arrowhead corresponds to the second peak in mean A-Scan. (E) Overlay of HC and SSc. Scale bar=240 μm.

Optical coherence tomography (OCT) of affected and not affected skin in plaque morphea. (A) OCT of not affected skin. Vertical scale represents depth in micrometre from the surface. The dermal–epidermal junction (DEJ) level is indicated by the white dotted line. Mean A-scan curve is overlaid and displayed in green. (B) OCT of affected skin in morphea patient. Mean A-scan curve is overlaid and displayed in red. Note the poorly visible DEJ and the valley of the curve below the DEJ (arrowhead). (C) Overlay of mean A-scan curves from the analysis of affected and unaffected skin in a morphea patient. Note that in the curves overlay graph both the difference depth of the first valley is clearly appreciable (arrowheads). Similarly the second mean A-scan peak (arrow) is subtle in the affected skin, similar to scleroderma affected skin.

Optical coherence tomography (OCT) of affected and not affected skin in plaque morphea. (A) OCT of not affected skin. Vertical scale represents depth in micrometre from the surface. The dermal–epidermal junction (DEJ) level is indicated by the white dotted line. Mean A-scan curve is overlaid and displayed in green. (B) OCT of affected skin in morphea patient. Mean A-scan curve is overlaid and displayed in red. Note the poorly visible DEJ and the valley of the curve below the DEJ (arrowhead). (C) Overlay of mean A-scan curves from the analysis of affected and unaffected skin in a morphea patient. Note that in the curves overlay graph both the difference depth of the first valley is clearly appreciable (arrowheads). Similarly the second mean A-scan peak (arrow) is subtle in the affected skin, similar to scleroderma affected skin.


The current gold standard for semiquantitative assessment of skin fibrosis, the mRSS, suffers from several shortcomings ranging from the subjectivity of skin palpation assessments and the high level of skill required from the clinical investigator. Even more importantly, a meta-analysis of three independent studies determined an overall within patient interobserver SD of five units independently of the mean skin score,[6 21] which represents an SE ranging from 20% to 26%. A primary outcome measure with 25% of SE entails the recruitment of a large number of patients to attain statistical validity in minimally significant changes, a task often difficult to accomplish given the comparatively low incidence of SSc.

A robust imaging biomarker for the assessment of skin fibrosis in SSc has not previously been reported. Herein we report the first study aimed to validate OCT for the quantitative assessment of skin involvement in SSc.

To date, the limited data on surrogate outcome measures for skin involvement are largely composed of histopathological or molecular changes in affected skin.[22 23] Despite conceptually very valuable, these studies, involving skin biopsies, are invasive and limited because of a site bias, referring to only one precise body area. Moreover, they are difficult to repeat in longitudinal manner and showed no sensitivity to change over time.[24] In this study, we evaluated OCT skin scanning as a reliable and quanti­tative tool that could be used as a surrogate marker of skin fibrosis. The technique requires minimal operator training, less than 10s per site examined, and offers the great advantage of saving image files for further or centralised operator independ­ent analysis. This latter is a particularly useful tool limiting the ‘hands on’ time in the clinic office and allowing a centralised, blinded assessment of results in clinical trials.

We observed an excellent correlation of OCT mean A-Scan curves and mRSS score at the site of analysis. More importantly, the corroboration of our OCT findings with pathological changes at the DEJ provides a robust construct validity for the technique. Of interest, we found that the changes of the OD of the dermis in SSc are similar to the ones observed in a case of plaque morphea, corroborating even further the potential value of OCT in measuring skin fibrosis.

Additional Comment

HFUS (High Frequensy Ultrasound) has been recently suggested to offer a quantitative assessment of skin thickness in SSc by several studies.8–10 In contrast with ultrasound, OCT does not require any use of gels, is able to give a higher resolution images and the analysis algo­rithm is automatic, not involving any operator interpretation. Nevertheless, since the penetration of OCT is limited to the first millimetre of skin, OCT and HFUS may be explored as comple­mentary imaging biomarkers in SSc.


1     Jimenez SA, Derk CT. Following the molecular pathways toward an understanding
of the pathogenesis of systemic sclerosis. Ann Intern Med 2004;140:37–50.

2     Varga J, Abraham D. Systemic sclerosis: a prototypic multisystem fibrotic disorder.
J Clin Invest 2007;117:557–67.

3     Gabrielli A, Avvedimento EV, Krieg T. Scleroderma. N Engl J Med

4     Clements PJ, Hurwitz EL, Wong WK, et al. Skin thickness score as a predictor and
correlate of outcome in systemic sclerosis: high-dose versus low-dose penicillamine trial. Arthritis Rheum 2000;43:2445–54.

5     Steen VD, Medsger TA Jr. Improvement in skin thickening in systemic sclerosis
associated with improved survival. Arthritis Rheum 2001;44:2828–35.

6     Pope JE, Baron M, Bellamy N, et al. Variability of skin scores and clinical
measurements in scleroderma. J Rheumatol 1995;22:1271–6.

Clements PJ, Lachenbruch PA, Seibold JR, et al. Skin thickness score in systemic

sclerosis: an assessment of interobserver variability in 3 independent studies. J Rheumatol 1993;20:1892–6.

   8     Akesson A, Hesselstrand R, Scheja A, et al. Longitudinal development of skin
involvement and reliability of high frequency ultrasound in systemic sclerosis. Ann Rheum Dis 2004;63:791–6.

   9     Moore TL, Lunt M, McManus B, et al. L. Seventeen-point dermal ultrasound scoring
system—a reliable measure of skin thickness in patients with systemic sclerosis. Rheumatology (Oxford) 2003;42:1559–63.

10     Kaloudi O, Bandinelli F, Filippucci E, et al. High frequency ultrasound

measurement of digital dermal thickness in systemic sclerosis. Ann Rheum Dis 2010;69:1140–3.

11     Aden N, Shiwen X, Aden D, et al. Proteomic analysis of scleroderma lesional skin
reveals activated wound healing phenotype of epidermal cell layer. Rheumatology (Oxford) 2008;47:1754–60.

12     Aden N, Nuttall A, Shiwen X, et al. Epithelial Cells Promote Fibroblast Activation via
IL-1alpha in Systemic Sclerosis. J Invest Dermatol 2010;130:2191–200.

13     Gambichler T, Jaedicke V, Terras S. Optical coherence tomography in dermatology:
technical and clinical aspects. Arch Dermatol Res 2011;303:457–73.

14     Marschall S, Sander B, Mogensen M, et al. Optical coherence tomography-current
technology and applications in clinical and biomedical research. Anal Bioanal Chem 2011;400:2699–720.

15     Coleman AJ, Richardson TJ, Orchard G, et al. Histological correlates of optical
coherence tomography in non-melanoma skin cancer. Skin Res Technol 2013;19: e10–9.

16     Preliminary criteria for the classification of systemic sclerosis (scleroderma).
Subcommittee for scleroderma criteria of the American Rheumatism Association Diagnostic and Therapeutic Criteria Committee. Arthritis Rheum 1980;23:581–90.

17     Collins TJ. ImageJ for microscopy. Biotechniques 2007;43:25–30.

18     Clendenon JL, Phillips CL, Sandoval RM, et al. Voxx: a PC-based, near real-time

volume rendering system for biological microscopy. Am J Physiol Cell Physiol 2002;282:C213–18.

19     Bland JM, Altman DG. Statistical methods for assessing agreement between two
methods of clinical measurement. Lancet 1986;1:307–10.

20     LeRoy EC, Black C, Fleischmajer R, et al. Scleroderma (systemic sclerosis):
classification, subsets and pathogenesis. J Rheumatol 1988;15:202–5.

21     Merkel PA, Silliman NP, Clements PJ, et al. Patterns and predictors of change in
outcome measures in clinical trials in scleroderma: an individual patient meta-analysis of 629 subjects with diffuse cutaneous systemic sclerosis. Arthritis Rheum 2012;64:3420–9.

22     Farina G, Lafyatis D, Lemaire R, et al. A four-gene biomarker predicts skin disease
in patients with diffuse cutaneous systemic sclerosis. Arthritis Rheum 2010;62:580–8.

23     Milano A, Pendergrass SA, Sargent JL, et al. Molecular subsets in the gene
expression signatures of scleroderma skin. PLoS One 2008;3:e2696.

  24   Pendergrass SA, Lemaire R, Francis IP, et al. Intrinsic gene expression subsets of

diffuse cutaneou

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Optical Coherent Tomography – emerging technology in cancer patient management

Author: Dror Nir, PhD

Optical Coherence Tomography (‘OCT’), is a technique for obtaining 3D optical-image from 1-2 mm thick tissue samples. The key benefits of using OCT for medical applications is that it is not ionizing, therefore harmless to the patient and the image resolution ~10µm, almost the size of a human cell.

Commercially available OCT systems have been used in ophthalmology and in interventional cardiology for quite a while. Recently, new applications attempting to provide intraoperative histopathology appear as alternative solution to frozen-sections based histopathology.

I found this nice diagram explaining why OCT can be used for identifying presence of cancer intra-operatively in an article [1] I downloaded from the website of the American association for cancer research.

Figure 1. Diagnostic scattering spectroscopy. In this cartoon illustration of the underlying principles of the method described in the work of Laughney and colleagues (1), an excised tissue sample sits on a glass plate, with normal cells depicted schematically on the left and tumor cells on the right. In contrast with the normal cells, the tumor cells are represented as having enlarged nuclei, with increased granularity of the chromatin, and, perhaps, a disrupted cytoskeleton resulting in cellular disorder. Light, from a broadband source, and after manipulation through an optical system, is collimated (parallel rays) and impinges on the tissue at normal incidence. While most light is scattered in the near-forward direction, some of the light is scattered directly backward after one (in most cases) or a few scattering events and is collected to be analyzed by a spectrometer (not depicted). The spectrometer provides a backscattering spectrum that is representative of the tissue properties. The wavelength dependence of the probability for backscattering varies with changes in the sizes and densities of the dominant scattering centers, such that the backscattered light spectrum changes with pathology, as manifested in the mean cellular micromorphology. In this way, the recorded spectrum relates directly to some of the micromorphology features that a pathologist recognizes as indicative of pathology when looking at histology slides, but in a quantitative manner, without the need to generate an actual microscopic image and without requiring subjective interpretation. (Of course, this physicist’s simplification ignores many tissue components that also affect scattering, such as extracellular matrix and vasculature.

In that article, Laughney and colleagues reported on real-time assessment of resection margins during breast- conserving surgery using OCT technique.

At the Thirty-Fifth Annual CTRC-AACR Breast Cancer Symposium held on Dec 4-8, 2012 in San Antonio, TX, BC Wilson, MK Akens, and CJ Niu (University Health Network, Ontario Cancer Institute, Toronto, ON, Canada; Tornado Medical System, Toronto, ON, Canada) discussed the unmet clinical need and the cost saving impact related to the potential use of Optical Coherence Tomography for intraoperative breast tumour margin width estimation in a poster session:

“Total mastectomy and lumpectomy with radiation have been shown to have equivalent patient outcomes, which has likely contributed to the more widespread adoption of breast conserving surgery (BCS) procedures. Assessment of breast lumpectomy margin widths in both an accurate and timely manner is essential to successful breast conservation procedures. Current BCS methodologies have been reported to result in reoperation rates of up to 20–60%, which represents a significant and unmet need for improved margin assessment. High reoperation rates present both increased treatment risk to patients and increased burden on healthcare systems. In the USA alone, over 150,000 lumpectomies are performed per year at an average cost between $11,000 and $19,000 USD per procedure. Assuming a relatively modest average repeat operation rate of 25%, potentially preventable repeat surgeries represent an approximate cost to the US healthcare system of $500M (USD) annually.”

From the same meeting: An abstract presenting the design of an ongoing multi-center, randomized, blinded clinical study aimed at Intraoperative assessment of tumor margins with a new optical imaging technology using a specific implementation of an OCT based device.

At last RSNA I have visited the French pavilion were one of the companies, LLTech, presented an OCT based system designed facilitate the work of pathologists and save time and money by allowing fresh tissue processing and pathology examination instead of the traditionally frozen sections (intraoperative) and fixated specimens (at lab)


That could be used by a pathologist to perform in-situ histology in the OR.


To summarize; OCT based systems seem to be something to look for in the future of cancer patients’ management.


1. Laughney AM, Krishnaswamy V, Rizzo EJ, Schwab EM, Barth RJ, et al. Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment. Clin Cancer Res. 2012;18:6315–25.

Writen by: Dror Nir, PhD

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