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Posts Tagged ‘OCT’


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.

Abstract

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.

Abbreviations

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

Keywords

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.

Acknowledgments

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

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Virtual Biopsy – is it possible?

Author – Writer: 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.

[START ORIGINAL PAPER]

ABSTRACT

Background

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.

Methods

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.

 Results

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).

Conclusions

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.

DISCUSSION

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.

REFERENCES

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.
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3     Gabrielli A, Avvedimento EV, Krieg T. Scleroderma. N Engl J Med
2009;360:1989–2003.

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
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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.

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

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diffuse cutaneou

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New development in measuring mechanical properties of tissue

Author – Writer: Dror Nir, PhD

Measuring the effects induced onto imaging by the mechanical properties of tissue is a common approach to differentiate tissue abnormalities. In previous posts I discussed the applicability of imaging applications that visualize variations in tissue stiffness; e.g. ultrasound-elastography and MRI-elastography as aid in the diagnosis workflow of cancer. Today, I would like to report on a recent publication made in SPIE Newsroom describing an optical-imaging system to measure tissue stiffness at high resolution. I think that such emerging technologies should be followed up as they bear promise to bridge deficiencies of the traditional modalities currently in use.

Reporting on: Optical elastography probes mechanical properties of tissue at high resolution

By: David Sampson, Kelsey Kennedy, Robert McLaughlin and Brendan Kennedy

Information published at: SPIE Newsroom – Biomedical Optics & Medical Imaging

Probing the micro-mechanical properties of tissue using optical imaging might offer new surgical tools that enable improved differentiation of tissue pathologies, such as cancer or atherosclerosis.

11 January 2013, SPIE Newsroom. DOI: 10.1117/2.1201212.004605

Elastography is an emerging branch of medical imaging that uses mechanical contrast to better characterize tissue pathology than can be achieved with structural imaging alone. It achieves this by imaging a tissue’s response to mechanical loading. Although commercial products based on ultrasonography and magnetic resonance imaging (MRI) have been available for several years, these new modalities offer superior tissue differentiation deep in the human body. However, elastography is limited by its low resolution compared with the length scales relevant to many diseases. Increasing the resolution with optical techniques might offer new opportunities for elastography in medical imaging and surgical guidance.

An elastography system requires a means of loading the tissue to cause deformation and an imaging system with sufficient sensitivity and range to capture this deformation. Implicit in these requirements is access to the tissue of interest. Optical elastography has previously been largely based on schemes that suit small tissue samples rather than intact tissue in living humans. Additionally, such schemes have not had the sensitivity or range to produce high-fidelity images of mechanical properties. We have addressed both these issues in our recent work, developing the means to access tissues in vivo and improve the sensitivity and range of optical elastography using phase-sensitive optical coherence tomography as the underlying modality. The use of optical coherence tomography to perform elastography has come to be referred to as optical coherence elastography.1

To make optical coherence elastography on human subjects feasible, we designed an annular piezoelectric loading transducer (see Figure 1), through which we could simultaneously image, enabling the first in vivo dynamic optical coherence elastography on human subjects.2 We were subsequently able to extend this to three dimensions (see Figure 2), in collaboration with Stephen Boppart’s group at the University of Illinois at Urbana-Champaign.3 This extension took advantage of the high speed of spectral-domain optical coherence tomography, and the maturity of phase-sensitive detection techniques originally developed for Doppler flowmetry and microangiography.

Figure 1. Schematic (left) and photograph (right) of the annular load transducer and imaging optics for in vivo optical coherence elastography.

 

Figure 2. 2D images of in vivo human skin selected from 3D stacks. (a) Optical coherence tomography image and (b) the same image overlaid by the 2D dynamic elastogram recorded at 125Hz load frequency, highlighting the greater strain in the epidermis. Reprinted in modified form with permission.3

For general access to tissues in the body, optical coherence elastography faces two basic limitations. The free-space probe requires miniaturization for versatile access to tissue in confined or convoluted geometries. We addressed this in studies of the elastic properties of human airways using catheter-based anatomical optical coherence tomography.4

 

Figure 3. (a) Schematic diagram of needle optical coherence elastography. The phase difference Δφ=φ1– φ2 determines the displacement, d, when scaled by the wavelength, λ, and refractive index, n. (b) Needle and pig trachea. (c) Local displacement versus distance, with tissue boundaries indicated by red stars. (d) Representative histology. Reprinted in modified form with permission.6

More fundamentally, optical coherence tomography can only penetrate, at best, 1–2mm into most tissues, limiting it to superficial applications. To address this issue, we combined optical coherence elastography with needle probes, an active research area in our group (see Figure 3).5 We conveniently use the needle probe itself to deform the tissue during insertion.6 The deformation ahead of the needle tip depends on the mechanical properties of the tissue encountered, as well as on the nearby tissue environment, particularly on any interfaces ahead of it. We measure the local sub-micrometer displacement of the tissue between two positions of the moving needle probe. We plot this displacement versus distance ahead of the probe: see Figure 3(c). The slope of the displacement at location z is a measure of the local strain. A change in slope signifies a change in tissue stiffness; the steeper the slope, the softer the tissue (other things being equal). Figure 3 highlights this effect in a layered sample of pig trachea. The positions of the changes in slope correlate well with the tissue interfaces shown in the accompanying histology: see Figure 3(d).

The other key area of improvement we have focused on is lowering the optical coherence elastography noise floor by increasing the detection sensitivity, which is vital to make clinical imaging practical. We firstly showed that Gaussian-smoothed, weighted-least squares strain estimation improved the sensitivity of estimates by up to 12dB over conventional finite-difference methods.7 Next, we showed that performance could be further improved at low optical coherence tomo- graphy signal-to-noise ratios (and, therefore, at greater depths in tissue) by employing a 2D Fourier transform technique.8Combined with other system refinements, these improvements have enabled us to reach a displacement sensitivity of 300pm for typical optical coherence tomography signal-to-noise ratios in tissue, with room for improvement.

The Young’s modulus of soft tissue varies from kPa to tens of MPa, whereas the scattering coefficient of such tissues—which is largely responsible for determining the contrast of optical coherence tomography—is typically in the range 2–20mm−1. This apparent native advantage in mechanical over optical contrast (see the example in Figure 4), combined with the maturation of optical coherence elastography methods, bodes well for the future. In our group, we are pursuing tumor-margin identification using elastography; others have begun to consider corneal elastography,9, 10 and still others are examining shear wave schemes with the aim of probing Young’s modulus much deeper in tissues.11,12

 

Figure 4. Optical coherence tomography (a) and optical coherence elastography (b) images of the same phantom with two inclusions visible, showing enhanced mechanical over scattering contrast.

Optical elastography currently sits at a similar stage of development as ultrasound elastography did in 1999. Based on a similar trajectory, this field will rapidly expand over the next decade. Our recent results point to the first convincing applications of optical elastography being just around the corner.

We acknowledge funding for this work from Perpetual Trustees, the Raine Medical Research Foundation, the Cancer Council of Western Australia, the Australian Research Council, the National Health and Medical Research Council (Australia), and the National Breast Cancer Foundation (Australia).


David Sampson

Optical+Biomedical Engineering Laboratory
School of Electrical, Electronic and Computer Engineering

and
Centre for Microscopy, Characterisation and Analysis
The University of Western Australia

Perth, Australia
Kelsey Kennedy, Robert McLaughlin, Brendan Kennedy

Optical+Biomedical Engineering Laboratory
School of Electrical, Electronic and Computer Engineering
The University of Western Australia

Perth, Australia

References:
1. J. Schmitt, OCT elastography: imaging microscopic deformation and strain of tissue, Opt. Express 3(6), p. 199-211, 1998.doi:10.1364/OE.3.000199
2. B. F. Kennedy, T. R. Hillman, R. A. McLaughlin, B. C. Quirk, D. D. Sampson, In vivo dynamic optical coherence elastography using a ring actuator, Opt. Express 17(24), p. 21762-21772, 2009.doi:10.1364/OE.17.021762
3. B. F. Kennedy, X. Liang, S. G. Adie, D. K. Gerstmann, B. C. Quirk, S. A. Boppart, D. D. Sampson, In vivo three-dimensional optical coherence elastography, Opt. Express 19(7), p. 6623-6634, 2011.doi:10.1364/OE.19.006623
4. J. P. Williamson, R. A. McLaughlin, W. J. Noffsingerl, A. L. James, V. A. Baker, A. Curatolo, J. J. Armstrong, Elastic properties of the central airways in obstructive lung diseases measured using anatomical optical coherence tomography, Am. J. Resp. Crit. Care 183(5), p. 612-619, 2011.doi:10.1164/rccm.201002-0178OC
5. R. A. McLaughlin, B. C. Quirk, A. Curatolo, R. W. Kirk, L. Scolaro, D. Lorenser, P. D. Robbins, B. A. Wood, C. M. Saunders, D. D. Sampson, Imaging of breast cancer with optical coherence tomography needle probes: Feasibility and initial results, IEEE J. Sel. Topics Quantum Electron. 18(3), p. 1184-1191, 2012. doi:10.1109/JSTQE.2011.2166757
6. K. M. Kennedy, B. F. Kennedy, R. A. McLaughlin, D. D. Sampson, Needle optical coherence elastography for tissue boundary detection, Opt. Lett. 37(12), p. 2310-2312, 2012. doi:10.1364/OL.37.002310
7. B. F. Kennedy, S. H. Koh, R. A. McLaughlin, K. M. Kennedy, P. R. T. Munro, D. D. Sampson, Strain estimation in phase-sensitive optical coherence elastography, Biomed. Opt. Express 3(8), p. 1865-1879, 2012.doi:10.1364/BOE.3.001865
8. B. F. Kennedy, M. Wojtkowski, M. Szkulmowski, K. M. Kennedy, K. Karnowski, D. D. Sampson, Improved measurement of vibration amplitude in dynamic optical coherence elastography, Biomed. Opt. Express 3(12), p. 3138-3152, 2012. doi:10.1364/BOE.3.003138
9. R. K. Manapuram, S. R. Aglyamov, F. M. Monediado, M. Mashiatulla, J. Li, S. Y. Emelianov, K. V. Larin, In vivo estimation of elastic wave parameters using phase-stabilized swept source optical coherence elastography, J. Biomed. Opt. 17(10), p. 100501, 2012.doi:10.1117/1.JBO.17.10.100501
10. W. Qi, R. Chen, L. Chou, G. Liu, J. Zhang, Q. Zhou, Z. Chen, Phase-resolved acoustic radiation force optical coherence elastography, J. Biomed. Opt. 17(11), p. 110505, 2012. doi:10.1117/1.JBO.17.11.110505
11. C. Li, G. Guan, S. Li, Z. Huang, R. K. Wang, Evaluating elastic properties of heterogeneous soft tissue by surface acoustic waves detected by phase-sensitive optical coherence tomography, J. Biomed. Opt. 17(5), p. 057002, 2012. doi:10.1117/1.JBO.17.5.057002
12. M. Razani, A. Mariampillai, C. Sun, T. W. H. Luk, V. X. D. Yang, M. C. Kolios, Feasibility of optical coherence elastography measurements of shear wave propagation in homogeneous tissue equivalent phantoms,Biomed. Opt. Express 3(5), p. 972-980, 2012. doi:10.1364/BOE.3.00097

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