Posts Tagged ‘CT scans’

Al is on the way to lead critical ED decisions on CT

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

Artificial intelligence (AI) has infiltrated many organizational processes, raising concerns that robotic systems will eventually replace many humans in decision-making. The advent of AI as a tool for improving health care provides new prospects to improve patient and clinical team’s performance, reduce costs, and impact public health. Examples include, but are not limited to, automation; information synthesis for patients, “fRamily” (friends and family unpaid caregivers), and health care professionals; and suggestions and visualization of information for collaborative decision making.

In the emergency department (ED), patients with Crohn’s disease (CD) are routinely subjected to Abdomino-Pelvic Computed Tomography (APCT). It is necessary to diagnose clinically actionable findings (CAF) since they may require immediate intervention, which is typically surgical. Repeated APCTs, on the other hand, results in higher ionizing radiation exposure. The majority of APCT performance guidance is clinical and empiric. Emergency surgeons struggle to identify Crohn’s disease patients who actually require a CT scan to determine the source of acute abdominal distress.

Image Courtesy: Jim Coote via Pixabay https://www.aiin.healthcare/media/49446

Aid seems to be on the way. Researchers employed machine learning to accurately distinguish these sufferers from Crohn’s patients who appear with the same complaint but may safely avoid the recurrent exposure to contrast materials and ionizing radiation that CT would otherwise wreak on them.

The study entitled “Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department” was published on July 9 in Digestive and Liver Disease by gastroenterologists and radiologists at Tel Aviv University in Israel.

Retrospectively, Jacob Ollech and his fellow researcher have analyzed 101 emergency treatments of patients with Crohn’s who underwent abdominopelvic CT.

They were looking for examples where a scan revealed clinically actionable results. These were classified as intestinal blockage, perforation, intra-abdominal abscess, or complex fistula by the researchers.

On CT, 44 (43.5 %) of the 101 cases reviewed had such findings.

Ollech and colleagues utilized a machine-learning technique to design a decision-support tool that required only four basic clinical factors to test an AI approach for making the call.

The approach was successful in categorizing patients into low- and high-risk groupings. The researchers were able to risk-stratify patients based on the likelihood of clinically actionable findings on abdominopelvic CT as a result of their success.

Ollech and co-authors admit that their limited sample size, retrospective strategy, and lack of external validation are shortcomings.

Moreover, several patients fell into an intermediate risk category, implying that a standard workup would have been required to guide CT decision-making in a real-world situation anyhow.

Consequently, they generate the following conclusion:

We believe this study shows that a machine learning-based tool is a sound approach for better-selecting patients with Crohn’s disease admitted to the ED with acute gastrointestinal complaints about abdominopelvic CT: reducing the number of CTs performed while ensuring that patients with high risk for clinically actionable findings undergo abdominopelvic CT appropriately.

Main Source:

Konikoff, Tom, Idan Goren, Marianna Yalon, Shlomit Tamir, Irit Avni-Biron, Henit Yanai, Iris Dotan, and Jacob E. Ollech. “Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department.” Digestive and Liver Disease (2021). https://www.sciencedirect.com/science/article/abs/pii/S1590865821003340

Other Related Articles published in this Open Access Online Scientific Journal include the following:

Al App for People with Digestive Disorders

Reporter: Irina Robu, Ph.D.


Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc


Al System Used to Detect Lung Cancer

Reporter: Irina Robu, Ph.D.


Artificial Intelligence: Genomics & Cancer


Yet another Success Story: Machine Learning to predict immunotherapy response

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc


Systemic Inflammatory Diseases as Crohn’s disease, Rheumatoid Arthritis and Longer Psoriasis Duration May Mean Higher CVD Risk

Reporter: Aviva Lev-Ari, PhD, RN


Autoimmune Inflammatory Bowel Diseases: Crohn’s Disease & Ulcerative Colitis: Potential Roles for Modulation of Interleukins 17 and 23 Signaling for Therapeutics

Curators: Larry H Bernstein, MD FCAP and Aviva Lev-Ari, PhD, RN https://pharmaceuticalintelligence.com/2016/01/23/autoimmune-inflammtory-bowl-diseases-crohns-disease-ulcerative-colitis-potential-roles-for-modulation-of-interleukins-17-and-23-signaling-for-therapeutics/

Inflammatory Disorders: Inflammatory Bowel Diseases (IBD) – Crohn’s and Ulcerative Colitis (UC) and Others

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN


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Artificial Intelligence and Cardiovascular Disease

Reporter and Curator: Dr. Sudipta Saha, Ph.D.


3.3.18   Artificial Intelligence and Cardiovascular Disease, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

Cardiology is a vast field that focuses on a large number of diseases specifically dealing with the heart, the circulatory system, and its functions. As such, similar symptomatologies and diagnostic features may be present in an individual, making it difficult for a doctor to easily isolate the actual heart-related problem. Consequently, the use of artificial intelligence aims to relieve doctors from this hurdle and extend better quality to patients. Results of screening tests such as echocardiograms, MRIs, or CT scans have long been proposed to be analyzed using more advanced techniques in the field of technology. As such, while artificial intelligence is not yet widely-used in clinical practice, it is seen as the future of healthcare.

The continuous development of the technological sector has enabled the industry to merge with medicine in order to create new integrated, reliable, and efficient methods of providing quality health care. One of the ongoing trends in cardiology at present is the proposed utilization of artificial intelligence (AI) in augmenting and extending the effectiveness of the cardiologist. This is because AI or machine-learning would allow for an accurate measure of patient functioning and diagnosis from the beginning up to the end of the therapeutic process. In particular, the use of artificial intelligence in cardiology aims to focus on research and development, clinical practice, and population health. Created to be an all-in-one mechanism in cardiac healthcare, AI technologies incorporate complex algorithms in determining relevant steps needed for a successful diagnosis and treatment. The role of artificial intelligence specifically extends to the identification of novel drug therapies, disease stratification or statistics, continuous remote monitoring and diagnostics, integration of multi-omic data, and extension of physician effectivity and efficiency.

Artificial intelligence – specifically a branch of it called machine learning – is being used in medicine to help with diagnosis. Computers might, for example, be better at interpreting heart scans. Computers can be ‘trained’ to make these predictions. This is done by feeding the computer information from hundreds or thousands of patients, plus instructions (an algorithm) on how to use that information. This information is heart scans, genetic and other test results, and how long each patient survived. These scans are in exquisite detail and the computer may be able to spot differences that are beyond human perception. It can also combine information from many different tests to give as accurate a picture as possible. The computer starts to work out which factors affected the patients’ outlook, so it can make predictions about other patients.

In current medical practice, doctors will use risk scores to make treatment decisions for their cardiac patients. These are based on a series of variables like weight, age and lifestyle. However, they do not always have the desired levels of accuracy. A particular example of the use of artificial examination in cardiology is the experimental study on heart disease patients, published in 2017. The researchers utilized cardiac MRI-based algorithms coupled with a 3D systolic cardiac motion pattern to accurately predict the health outcomes of patients with pulmonary hypertension. The experiment proved to be successful, with the technology being able to pick-up 30,000 points within the heart activity of 250 patients. With the success of the aforementioned study, as well as the promise of other researches on artificial intelligence, cardiology is seemingly moving towards a more technological practice.

One study was conducted in Finland where researchers enrolled 950 patients complaining of chest pain, who underwent the centre’s usual scanning protocol to check for coronary artery disease. Their outcomes were tracked for six years following their initial scans, over the course of which 24 of the patients had heart attacks and 49 died from all causes. The patients first underwent a coronary computed tomography angiography (CCTA) scan, which yielded 58 pieces of data on the presence of coronary plaque, vessel narrowing and calcification. Patients whose scans were suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were also obtained from medical records including sex, age, smoking status and diabetes. These 85 variables were then entered into an artificial intelligence (AI) programme called LogitBoost. The AI repeatedly analysed the imaging variables, and was able to learn how the imaging data interacted and identify the patterns which preceded death and heart attack with over 90% accuracy. The predictive performance using the ten clinical variables alone was modest, with an accuracy of 90%. When PET scan data was added, accuracy increased to 92.5%. The predictive performance increased significantly when CCTA scan data was added to clinical and PET data, with accuracy of 95.4%.

Another study findings showed that applying artificial intelligence (AI) to the electrocardiogram (ECG) enables early detection of left ventricular dysfunction and can identify individuals at increased risk for its development in the future. Asymptomatic left ventricular dysfunction (ALVD) is characterised by the presence of a weak heart pump with a risk of overt heart failure. It is present in three to six percent of the general population and is associated with reduced quality of life and longevity. However, it is treatable when found. Currently, there is no inexpensive, noninvasive, painless screening tool for ALVD available for diagnostic use. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3 percent, 85.7 percent, and 85.7 percent, respectively. Furthermore, in patients without ventricular dysfunction, those with a positive AI screen were at four times the risk of developing future ventricular dysfunction compared with those with a negative screen.

In recent years, the analysis of big data database combined with computer deep learning has gradually played an important role in biomedical technology. For a large number of medical record data analysis, image analysis, single nucleotide polymorphism difference analysis, etc., all relevant research on the development and application of artificial intelligence can be observed extensively. For clinical indication, patients may receive a variety of cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical coherence tomography, electrical physiology, etc. By using artificial intelligence deep learning system, the investigators hope to not only improve the diagnostic rate and also gain more accurately predict the patient’s recovery, improve medical quality in the near future.

The primary issue about using artificial intelligence in cardiology, or in any field of medicine for that matter, is the ethical issues that it brings about. Physicians and healthcare professionals prior to their practice swear to the Hippocratic Oath—a promise to do their best for the welfare and betterment of their patients. Many physicians have argued that the use of artificial intelligence in medicine breaks the Hippocratic Oath since patients are technically left under the care of machines than of doctors. Furthermore, as machines may also malfunction, the safety of patients is also on the line at all times. As such, while medical practitioners see the promise of artificial technology, they are also heavily constricted about its use, safety, and appropriateness in medical practice.

Issues and challenges faced by technological innovations in cardiology are overpowered by current researches aiming to make artificial intelligence easily accessible and available for all. With that in mind, various projects are currently under study. For example, the use of wearable AI technology aims to develop a mechanism by which patients and doctors could easily access and monitor cardiac activity remotely. An ideal instrument for monitoring, wearable AI technology ensures real-time updates, monitoring, and evaluation. Another direction of cardiology in AI technology is the use of technology to record and validate empirical data to further analyze symptomatology, biomarkers, and treatment effectiveness. With AI technology, researchers in cardiology are aiming to simplify and expand the scope of knowledge on the field for better patient care and treatment outcomes.
















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AI System Used to Detect Lung Cancer

Reporter: Irina Robu, PhD


3.3.13   AI System Used to Detect Lung Cancer, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

Lung cancer is characterized by uncontrolled cell growth in tissues of the lung. The growth spreads beyond the lung by metastasis into nearby tissues. The most common symptoms are coughing (including coughing up blood), weight loss, shortness of breath, and chest pains. The two main types of lung cancer are small-cell lung carcinoma(SCLC) and non-small-cell lung carcinoma (NSCLC). Lung cancer may be seen on chest radiographs and computed tomography(CT) scans. However, computers seem to be as good or better than regular doctors at detecting tiny lung cancers on CT scans according to scientists from Google.

The AI designed by Google was able to interpret images using the same skills as humans to read microscope slides, X-rays, M.R.I.s and other medical scans by feeding huge amounts of data from medical imaging into the systems. It seems that the researchers were able to train computers to recognize patterns linked to a specific condition.

In a new Google study, the scientists applied artificial intelligence to CT scans used to screen people for lung cancer. Current studies have shown that screening can reduce the risk of dying from lung cancer and can also identify spots that might later become malignant.

The researchers created a neural network with multiple layers of processing and trained the AI by giving it many CT scans from patients whose diagnoses were known. This allows radiologists to sort patients into risk groups and decide whether biopsies are needed or follow up to keep track of the suspected regions. Even though the technology seems promising, but it can have pitfalls such as missing tumors, mistaken benign spots for malignancies and push patients into risky procedures.

Yet, the ability to process vast amounts of data may make it imaginable for artificial intelligence to recognize subtle patterns that humans simply cannot see. It is well understood that the systems should be studied extensively before using them for general public use. The lung-screening neural network is not ready for the clinic yet.


A.I. Took Test To Detect Lung Cancer And Smashed It

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Detecting Multiple Types of Cancer With a Single Blood Test

Reporter and Curator: Irina Robu, PhD

Monitoring cancer patients and evaluating their response to treatment can sometimes involve invasive procedures, including surgery.

The liquid biopsies have become something of a Holy Grail in cancer treatment among physicians, researchers and companies gambling big on the technology. Liquid biopsies, unlike traditional biopsies involving invasive surgery — rely on an ordinary blood draw. Developments in sequencing the human genome, permitting researchers to detect genetic mutations of cancers, have made the tests conceivable. Some 38 companies in the US alone are working on liquid biopsies by trying to analyze blood for fragments of DNA shed by dying tumor cells.

Premature research on the liquid biopsy has concentrated profoundly on patients with later-stage cancers who have suffered treatments, including chemotherapy, radiation, surgery, immunotherapy or drugs that target molecules involved in the growth, progression and spread of cancer. For cancer patients undergoing treatment, liquid biopsies could spare them some of the painful, expensive and risky tissue tumor biopsies and reduce reliance on CT scans. The tests can rapidly evaluate the efficacy of surgery or other treatment, while old-style biopsies and CT scans can still remain inconclusive as a result of scar tissue near the tumor site.

As recently as a few years ago, the liquid biopsies were hardly used except in research. At the moment, thousands of the tests are being used in clinical practices in the United States and abroad, including at the M.D. Anderson Cancer Center in Houston; the University of California, San Diego; the University of California, San Francisco; the Duke Cancer Institute and several other cancer centers.

With patients for whom physicians cannot get a tissue biopsy, the liquid biopsy could prove a safe and effective alternative that could help determine whether treatment is helping eradicate the cancer. A startup, Miroculus developed a cheap, open source device that can test blood for several types of cancer at once. The platform, called Miriam finds cancer by extracting RNA from blood and spreading it across plates that look at specific type of mRNA. The technology is then hooked up at a smartphone which sends the information to an online database and compares the microRNA found in the patient’s blood to known patterns indicating different type of cancers in the early stage and can reduce unnecessary cancer screenings.

Nevertheless, experts warn that more studies are essential to regulate the accuracy of the test, exactly which cancers it can detect, at what stages and whether it improves care or survival rates.




Other related articles published in this Open Access Online Scientific Publishing Journal include the following:

Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood – R&D @Worcester Polytechnic Institute, Micro and Nanotechnology Lab

Reporters: Tilda Barliya, PhD and Aviva Lev-Ari, PhD, RN


Liquid Biopsy Assay May Predict Drug Resistance

Curator: Larry H. Bernstein, MD, FCAP


One blood sample can be tested for a comprehensive array of cancer cell biomarkers: R&D at WPI

Curator: Marzan Khan, B.Sc




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The unfortunate ending of the Tower of Babel construction project and its effect on modern imaging-based cancer patients’ management

The story of the city of Babel is recorded in the book of Genesis 11 1-9. At that time, everyone on earth spoke the same language.

Picture: Pieter Bruegel the Elder: The Tower of Babel_(Vienna)

It is probably safe to assume that medical practitioners at that time were reporting the status of their patients in a standard manner. Although not mentioned, one might imagine that, at that time, ultrasound or MRI scans were also reported in a standard and transferrable manner. The people of Babel noticed the potential in uniform communication and tried to build a tower so high that it would  reach the gods. Unfortunately, God did not like that, so he went down (in person) and confounded people’s speech, so that they could not understand each another. Genesis 11:7–8.

This must be the explanation for our inability to come to a consensus on reporting of patients’ imaging-outcome. Progress in development of efficient imaging protocols and in clinical management of patients is withheld due to high variability and subjectivity of clinicians’ approach to this issue.

Clearly, a justification could be found for not reaching a consensus on imaging protocols: since the way imaging is performed affects the outcome, (i.e. the image and its interpretation) it takes a long process of trial-and-error to come up with the best protocol.  But, one might wonder, wouldn’t the search for the ultimate protocol converge faster if all practitioners around the world, who are conducting hundreds of clinical studies related to imaging-based management of cancer patients, report their results in a standardized and comparable manner?

Is there a reason for not reaching a consensus on imaging reporting? And I’m not referring only to intra-modality consensus, e.g. standardizing all MRI reports. I’m referring also to inter-modality consensus to enable comparison and matching of reports generated from scans of the same organ by different modalities, e.g. MRI, CT and ultrasound.

As developer of new imaging-based technologies, my personal contribution to promoting standardized and objective reporting was the implementation of preset reporting as part of the prostate-HistoScanning product design. For use-cases, as demonstrated below, in which prostate cancer patients were also scanned by MRI a dedicated reporting scheme enabled matching of the HistoScanning scan results with the prostate’s MRI results.

The MRI reporting scheme used as a reference is one of the schemes offered in a report by Miss Louise Dickinson on the following European consensus meeting : Magnetic Resonance Imaging for the Detection, Localisation, and Characterisation of Prostate Cancer: Recommendations from a European Consensus Meeting, Louise Dickinson a,b,c,*, Hashim U. Ahmed a,b, Clare Allen d, Jelle O. Barentsz e, Brendan Careyf, Jurgen J. Futterer e, Stijn W. Heijmink e, Peter J. Hoskin g, Alex Kirkham d, Anwar R. Padhani h, Raj Persad i, Philippe Puech j, Shonit Punwani d, Aslam S. Sohaib k, Bertrand Tomball,Arnauld Villers m, Jan van der Meulen c,n, Mark Emberton a,b,c,


Image of MRI reporting scheme taken from the report by Miss Louise Dickinson

The corresponding HistoScanning report is following the same prostate segmentation and the same analysis plans:

Preset reporting enabling matching of HistoScanning and MRI reporting of the same case.

It is my wish that already in the near-future, the main radiology societies (RSNA, ESR, etc..) will join together to build the clinical Imaging’s “Tower of Babel” to effectively address the issue of standardizing reporting of imaging procedures. This time it will not be destroyed…:-)

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Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention?

Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention?

Author: Dror Nir, PhD

Advances in techniques for cancer lesions’ detection and localisation [1-6] opened the road to methods of localised (“focused”) cancer treatment [7-10].  An obvious challenge on the road is reassuring that the imaging-guided treatment device indeed treats the region of interest and preferably, only it.

A step in that direction was taken by a group of investigators from Sunnybrook Health Sciences Centre, University of Toronto, Ontario, Canada who evaluate the feasibility and safety of magnetic resonance (MR) imaging–controlled transurethral ultrasound therapy for prostate cancer in humans [7]. Their study’s objective was to prove that using real-time MRI guidance of HIFU treatment is possible and it guarantees that the location of ablated tissue indeed corresponds to the locations planned for treatment. Eight eligible patients were recruited.


The setup


Treatment protocol


The result


“There was excellent agreement between the zone targeted for treatment and the zone of thermal injury, with a targeting accuracy of ±2.6 mm. In addition, the temporal evolution of heating was very consistent across all patients, in part because of the ability of the system to adapt to changes in perfusion or absorption properties according to the temperature measurements along the target boundary.”


Technological problems to be resolved in the future:

“Future device designs could incorporate urinary drainage during the procedure, given the accumulation of urine in the bladder during treatment.”

“Sufficient temperature resolution could be achieved only by using 10-mm-thick sections. Our numeric studies suggest that 5-mm-thick sections are necessary for optimal three-dimensional conformal heating and are achievable by using endorectal imaging coils or by performing the treatment with a 3.0-T platform.”

Major limitation: “One of the limitations of the study was the inability to evaluate the efficacy of this treatment; however, because this represents, to our knowledge, the first use of this technology in human prostate, feasibility and safety were emphasized. In addition, the ability to target the entire prostate gland was not assessed, again for safety considerations. We have not attempted to evaluate the effectiveness of this treatment for eradicating cancer or achieving durable biochemical non-evidence of disease status.”


  1. SIMMONS (L.A.M.), AUTIER (P.), ZATURA (F.), BRAECKMAN (J.G.), PELTIER (A.), ROMICS (I.), STENZL (A.), TREURNICHT (K.), WALKER (T.), NIR (D.), MOORE (C.M.), EMBERTON (M.). Detection, localisation and characterisation of prostate cancer by Prostate HistoScanning.. British Journal of Urology International (BJUI). Issue 1 (July). Vol. 110, Page(s): 28-35
  2. WILKINSON (L.S.), COLEMAN (C.), SKIPPAGE (P.), GIVEN-WILSON (R.), THOMAS (V.). Breast HistoScanning: The development of a novel technique to improve tissue characterization during breast ultrasound. European Congress of Radiology (ECR), A.4030, C-0596, 03-07/03/2011.
  3. Hebert Alberto Vargas, MD, Tobias Franiel, MD,Yousef Mazaheri, PhD, Junting Zheng, MS, Chaya Moskowitz, PhD, Kazuma Udo, MD, James Eastham, MD and Hedvig Hricak, MD, PhD, Dr(hc) Diffusion-weighted Endorectal MR Imaging at 3 T for Prostate Cancer: Tumor Detection and Assessment of Aggressiveness. June 2011 Radiology, 259,775-784.
  4. Wendie A. Berg, Kathleen S. Madsen, Kathy Schilling, Marie Tartar, Etta D. Pisano, Linda Hovanessian Larsen, Deepa Narayanan, Al Ozonoff, Joel P. Miller, and Judith E. Kalinyak Breast Cancer: Comparative Effectiveness of Positron Emission Mammography and MR Imaging in Presurgical Planning for the Ipsilateral Breast Radiology January 2011 258:1 59-72.
  5. Anwar R. Padhani, Dow-Mu Koh, and David J. Collins Reviews and Commentary – State of the Art: Whole-Body Diffusion-weighted MR Imaging in Cancer: Current Status and Research Directions Radiology December 2011 261:3 700-718
  6. Eggener S, Salomon G, Scardino PT, De la Rosette J, Polascik TJ, Brewster S. Focal therapy for prostate cancer: possibilities and limitations. Eur Urol 2010;58(1):57–64).
  7. Rajiv Chopra, PhD, Alexandra Colquhoun, MD, Mathieu Burtnyk, PhD, William A. N’djin, PhD, Ilya Kobelevskiy, MSc, Aaron Boyes, BSc, Kashif Siddiqui, MD, Harry Foster, MD, Linda Sugar, MD, Masoom A. Haider, MD, Michael Bronskill, PhD and Laurence Klotz, MD. MR Imaging–controlled Transurethral Ultrasound Therapy for Conformal Treatment of Prostate Tissue: Initial Feasibility in Humans. October 2012 Radiology, 265,303-313.
  8. Black, Peter McL. M.D., Ph.D.; Alexander, Eben III M.D.; Martin, Claudia M.D.; Moriarty, Thomas M.D., Ph.D.; Nabavi, Arya M.D.; Wong, Terence Z. M.D., Ph.D.; Schwartz, Richard B. M.D., Ph.D.; Jolesz, Ferenc M.D.  Craniotomy for Tumor Treatment in an Intraoperative Magnetic Resonance Imaging Unit. Neurosurgery: September 1999 – Volume 45 – Issue 3 – p 423
  9. Medel, Ricky MD,  Monteith, Stephen J. MD, Elias, W. Jeffrey MD, Eames, Matthew PhD, Snell, John PhD, Sheehan, Jason P. MD, PhD, Wintermark, Max MD, MAS, Jolesz, Ferenc A. MD, Kassell, Neal F. MD. Neurosurgery: Magnetic Resonance–Guided Focused Ultrasound Surgery: Part 2: A Review of Current and Future Applications. October 2012 – Volume 71 – Issue 4 – p 755–763
  10. Bruno Quesson PhD, Jacco A. de Zwart PhD, Chrit T.W. Moonen PhD. Magnetic resonance temperature imaging for guidance of thermotherapy. Journal of Magnetic Resonance Imaging, Special Issue: Interventional MRI, Part 1, Volume 12, Issue 4, pages 525–533, October 2000

Writer: Dror Nir, PhD

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Introducing smart-imaging into radiologists’ daily practice.

Author and Curator: Dror Nir, PhD

Radiology congresses are all about imaging in medicine. Interestingly, radiology originates from radiation. It was the discovery of X-ray radiation at the beginning of the 20th century that opened the road to “seeing” the inside of the human body without harming it (at that time that meant cutting into the body).

Radiology meetings are about sharing experience and knowhow on imaging-based management patients. The main topic is always image-interpretation: the bottom line of clinical radiology! This year’s European Congress of Radiology (ECR) dedicated few of its sessions to recent developments in image-interpretation tools. I chose to discuss the one that I consider contributing the most to the future of cancer patients’ management.

In the refresher course dedicated to computer application the discussion was aimed at understanding the question “How do image processing and CAD impact radiological daily practice?” Experts’ reviews gave the audience some background information on the following subjects:

  1. A.     The link between image reconstruction and image analysis.
  2. B.     Semantic web technologies for sharing and reusing imaging-related information
  3. C.     Image processing and CAD: workflow in clinical practice.

I find item A to be a fundamental education item. Not once did I hear a radiologist saying: “I know this is the lesion because it’s different on the image”.  Being aware of the computational concepts behind image rendering, even if it is at a very high level and lacking deep understanding of the computational processes,  will contribute to more balanced interpretations.

Item B is addressing the dream of investigators worldwide. Imagine that we could perform a web search and find educating, curated materials linking visuals and related clinical information, including standardized pathology reporting. We would only need to remember that search engines used certain search methods and agree, worldwide, on the method and language to be used when describing things. Having such tools is a pre-requisite to successful pharmaceutical and bio-tech development.

I find item C strongly linked to A, as all methods for better image interpretation must fit into a workflow. This is a design goal that is not trivial to achieve. To understand what I mean by that, try to think about how you could integrate the following examples in your daily workflow: i.e. what kind of expertise is needed for execution, how much time it will take, do you have the infrastructure?

In the rest of this post, I would like to highlight, through examples that were discussed during ECR 2012, the aspect of improving cancer patients’ clinical assessment by using information fusion to support better image interpretation.

  • Adding up quantitative information from MR spectroscopy (quantifies biochemical property of a target lesion) and Dynamic Contrast Enhanced MR imaging (highlights lesion vasculature).

Image provided by: Dr. Pascal Baltzer, director of mammography at the centre for radiology at Friedrich Schiller University in Jena, Germany

  • Registration of images generated by different imaging modalities (Multi-modal imaging registration).

The following examples: Fig 2 demonstrates registration of a mammography image of a breast lesion to an MRI image of this lesion. Fig3 demonstrates registration of an ultrasound image of a breast lesion scanned by an Automatic Breast Ultrasound (ABUS) system and an MRI image of the same lesion.

Images provided by members of the HAMAM project (an EU, FP7 funded research project: Highly Accurate Breast Cancer Diagnosis through Integration of Biological Knowledge, Novel Imaging Modalities, and Modelling): http://www.hamam-project.org


 Multi-modality image registration is usually based on the alignment of image-features apparent in the scanned regions. For ABUS-MRI matching these were: the location of the nipple and the breast thickness; the posterior of the nipple in both modalities; the medial-lateral distance of the nipple to the breast edge on ultrasound; and an approximation of the rib­cage using a cylinder on the MRI. A mean accuracy of 14mm was achieved.

Also from the HAMAM project, registration of ABUS image to a mammography image:

registration of ABUS image to a mammography image, Image provided by members of the HAMAM project (an EU, FP7 funded research project: Highly Accurate Breast Cancer Diagnosis through Integration of Biological Knowledge, Novel Imaging Modalities, and Modelling): http://www.hamam-project.org

  • Automatic segmentation of suspicious regions of interest seen in breast MRI images

Segmentation of suspicious the lesions on the image is the preliminary step in tumor evaluation; e.g. finding its size and location. Since lesions have different signal/image character­istics to the rest of the breast tissue, it gives hope for the development of computerized segmentation techniques. If successful, such techniques bear the promise of enhancing standardization in the reporting of lesions size and location: Very important information for the success of the treatment step.

Roberta Fusco of the National Cancer Institute of Naples Pascal Foundation, Naples/IT suggested the following automatic method for suspi­cious ROI selection within the breast using dynamic-derived information from DCE-MRI data.


Automatic segmentation of suspicious ROI in breast MRI images, image provided by Roberta Fusco of the National Cancer Institute of Naples Pascal Foundation, Naples/IT


 Her algorithm includes three steps (Figure 2): (i) breast mask extraction by means of automatic intensity threshold estimation (Otsu Thresh-holding) on the par­ametric map obtained through the sum of intensity differences (SOD) calculated pixel by pixel; (ii) hole-filling and leakage repair by means of morphological operators: closing is required to fill the holes on the boundaries of breast mask, filling is required to fill the holes within the breasts, erosion is required to reduce the dilation obtained by the closing operation; (iii) suspicious ROIs extraction: a pixel is assigned to a suspicious ROI if it satisfies two conditions: the maximum of its normalized time-intensity curve should be greater than 0.3 and the maximum signal intensity should be reached before the end of the scan time. The first condition assures that the pixels within the ROI have a significant contrast agent uptake (thus excluding type I and type II curves) and the second condition is required for the time-intensity pattern to be of type IV or V (thus excluding type III curves).

Written by: Dror Nir, PhD

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Imaging: seeing or imagining? (Part 2)

Author and Curator: Dror Nir, PhD

This post is a continuation of

Imaging: seeing or imagining? (Part 1)


That is the question…

Anyone who follows healthcare news, as I do , cannot help being impressed with the number of scientific and non-scientific items that mention the applicability of Magnetic Resonance Imaging (‘MRI’) to medical procedures.

A very important aspect that is worthwhile noting is that the promise MRI bears to improve patients’ screening – pre-clinical diagnosis, better treatment choice, treatment guidance and outcome follow-up – is based on new techniques that enables MRI-based tissue characterisation.

Magnetic resonance imaging (MRI) is an imaging device that relies on the well-known physical phenomena named “Nuclear Magnetic Resonance”. It so happens that, due to its short relaxation time, the 1H isotope (spin ½ nucleus) has a very distinctive response to changes in the surrounding magnetic field. This serves MRI imaging of the human body well as, basically, we are 90% water. The MRI device makes use of strong magnetic fields changing at radio frequency to produce cross-sectional images of organs and internal structures in the body. Because the signal detected by an MRI machine varies depending on the water content and local magnetic properties of a particular area of the body, different tissues or substances can be distinguished from one another in the scan’s resulting image.

MRI scan of a breast lesion (Source Radiology.com)

The main advantages of MRI in comparison to X-ray-based devices such as CT scanners and mammography systems are that the energy it uses is non-ionizing and it can differentiate soft tissues very well based on differences in their water content.

In the last decade, the basic imaging capabilities of MRI have been augmented for the purpose of cancer patient management, by using magnetically active materials (called contrast agents) and adding functional measurements such as tissue temperature to show internal structures or abnormalities more clearly.

In order to increase the specificity and sensitivity of MRI imaging in cancer detection, various imaging strategies have been developed. The most discussed in MRI related literature are:

  • T2 weighted imaging: The measured response of the 1H isotope in a resolution cell of a T2-weighted image is related to the extent of random tumbling and the rotational motion of the water molecules within that resolution cell. The faster the rotation of the water molecule, the higher the measured value of the T2 weighted response in that resolution cell. For example, prostate cancer is characterized by a low T2 response relative to the values typical to normal prostatic tissue [5].

T2 MRI pelvis with Endo Rectal Coil ( DATA of Dr. Lance Mynders, MAYO Clinic)

  • Dynamic Contrast Enhanced (DCE) MRI involves a series of rapid MRI scans in the presence of a contrast agent. In the case of scanning the prostate, the most commonly used material is gadolinium [4].

Axial MRI Lava DCE with Endo Rectal ( DATA of Dr. Lance Mynders, MAYO Clinic)

  • Diffusion weighted (DW) imaging: Provides an image intensity that is related to the microscopic motion of water molecules [5].

DW image of the left parietal glioblastoma multiforme (WHO grade IV) in a 59-year-old woman, Al-Okaili R N et al. Radiographics 2006;26:S173-S189

  • Multifunctional MRI: MRI image overlaid with combined information from T2-weighted scans, dynamic contrast-enhancement (DCE), and diffusion weighting (DW) [5].
  • Blood oxygen level-dependent (BOLD) MRI: Assessing tissue oxygenation. Tumors are characterized by a higher density of micro blood vessels. The images that are acquired follow changes in the concentration of paramagnetic deoxyhaemoglobin [5].

In the last couple of years, medical opinion leaders are offering to use MRI to solve almost every weakness of the cancer patients’ pathway. Such proposals are not always supported by any evidence of feasibility. For example, a couple of weeks ago, the British Medical Journal published a study [1] concluding that women carrying a mutation in the BRCA1 or BRCA2 genes who have undergone a mammogram or chest x-ray before the age of 30 are more likely to develop breast cancer than those who carry the gene mutation but who have not been exposed to mammography. What is published over the internet and media to patients and lay medical practitioners is: “The results of this study support the use of non-ionising radiation imaging techniques (such as magnetic resonance imaging) as the main tool for surveillance in young women with BRCA1/2 mutations.”.

Why is ultrasound not mentioned as a potential “non-ionising radiation imaging technique”?

Another illustration is the following advert:

Advert in favour of MRI termal imaging of breast

An MRI scan takes between 30 to 45 minutes to perform (not including the time of waiting for the interpretation by the radiologist). It requires the support of around 4 well-trained team members. It costs between $400 and $3500 (depending on the scan).

The important question, therefore, is: Are there, in the USA, enough MRI  systems to meet the demand of 40 million scans a year addressing women with radiographically dense  breasts? Toda there are approximately 10,000 MRI systems in the USA. Only a small percentage (~2%) of the examinations are related to breast cancer. A

A rough calculation reveals that around 10,000 additional MRI centers would need to be financed and operated to meet that demand alone.


  1. Exposure to diagnostic radiation and risk of breast cancer among carriers of BRCA1/2 mutations: retrospective cohort study (GENE-RAD-RISK), BMJ 2012; 345 doi: 10.1136/bmj.e5660 (Published 6 September 2012), Cite this as: BMJ 2012;345:e5660 – http://www.bmj.com/content/345/bmj.e5660
  1. http://www.auntminnieeurope.com/index.aspx?sec=sup&sub=wom&pag=dis&itemId=607075
  1. Ahmed HU, Kirkham A, Arya M, Illing R, Freeman A, Allen C, Emberton M. Is it time to consider a role for MRI before prostate biopsy? Nat Rev Clin Oncol. 2009;6(4):197-206.
  1. Puech P, Potiron E, Lemaitre L, Leroy X, Haber GP, Crouzet S, Kamoi K, Villers A. Dynamic contrast-enhanced-magnetic resonance imaging evaluation of intraprostatic prostate cancer: correlation with radical prostatectomy specimens. Urology. 2009;74(5):1094-9.
  1. Advanced MR Imaging Techniques in the Diagnosis of Intraaxial Brain Tumors in Adults, Al-Okaili R N et al. Radiographics 2006;26:S173-S189 ,


  1. Ahmed HU. The Index Lesion and the Origin of Prostate Cancer. N Engl J Med. 2009 Oct; 361(17): 1704-6

Writer: Dror Nir, PhD.

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