The Board of Directors of the Society of Computed Body Tomography and Magnetic Resonance are proud to announce the recipients of the SCBT-MR Gold Medal for 2017.

Gold medals are awarded in recognition of leadership to the society and extraordinary contributions to the art and science of body imaging.

Michael P. Federle, MD, FSCBTMR
Dr. Federle graduated Summa cum Laude with a bachelor’s degree in Biology from Marquette University and his MD from Georgetown University. He trained in Internal Medicine and Radiology at the University of Cincinnati, after which he had a fellowship in Abdominal Imaging and 10 years on faculty at the University of California, San Francisco (UCSF), in radiology where he also served as Chief of Radiology at San Francisco General Hospital.

In 1989 Dr Federle accepted the Chair of Radiology position at the University of Pittsburgh medical center, where he also subsequently held the position of Chief of Abdominal Imaging. Read More


W. Dennis Foley, MD, FSCBTMR
W. Dennis Foley, MD is a graduate of the University of Sydney, New South Wales, Australia.  He trained in Radiology at St. Vincent’s Hospital, Sydney, Australia.  Dr. Foley did a 1-year Instructorship in Vascular Radiology at the University of Michigan Medical Center, Ann Arbor, Michigan.     

Dr. Foley has been practicing diagnostic radiology since 1974.  For the last 40 years he has been at the Medical College of Wisconsin in Milwaukee, Wisconsin.  Prior to 1974 Dr. Foley had worked as an assistant professor at the University of Michigan Medical Center in Ann Arbor, Michigan and just prior to starting at the Medical College of Wisconsin in 1977, Dr. Foley spent two years as a Specialist in Diagnostic Radiology at Sir Charles Gairdner Hospital, Perth Medical Centre, Shenton Park, West Australia. 
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Alec J. Megibow, MD, FSCBTMR
Dr. Megibow received HIS bachelor’s degree from Case Western Reserve University in 1968. Following a gap year during which time he taught in the Cleveland Public Schools,  went on to SUNY Upstate Medical Center and received my MD in 1974. From there he directly began my diagnostic Radiology residency at NYU-Bellevue, completing training that included a year of abdominal imaging fellowship in 1978. Dr. Megibow remain at NYU-Bellevue to this day.

Dr. Megibow was extremely fortunate in career timing; his first whole body scanner was installed as he began my fellowship year in 1977-8. Dr. Megibow was granted the privilege of working side-by-side with Dr. Morton A. Bosniak a mentor/partner relationship that endured throughout my career. In 1980, joined by Drs. David Naidich and Emil Balthazar our core of innovative imagers was established  who, through strong ties with clinicians at NYU-Bellevue, were able to consistently publish substantive contributions to the literature and medical practice. Read More

2017 registration fee reduced $200 - the lowest price in more than 10 years- with the same packed meeting! The 2017 meeting will bring; over 25 Hours of Lecture Time, AMA PRA Category 1 Credits™‚Äč,opportunity to learn about new techniques in body imaging, and face-to-face time with leadership-level faculty.
Online recording of the full course available to full meeting registrants for an opportunity to earn SA-CME. View Full Program

Save an additional $100! Register now to receive the early bird rate - ends July 10, 2017



Dr.Dreyer will discuss Big Data, Machine Learning, and AI in Radiology. Vice Chairman of Radiology, Massachusetts General Hospital and Associate Professor of Radiology, Harvard Medical School, Dr. Dreyer is also the Director of the Center for Clinical Data Science at Massachusetts General HospitalLearn More

3D Printing;Scott Reeder, MD, PhD,FSCBTMR
Managing the Incidental Renal Mass; Brian Herts, MD,FSCBTMR
Small Bowel Obstructions: When to Worry? Erik Paulson, MD, FSCBTMR

View Full Program


This workshop will take place during the the first half of the day, September 9th, before the start of SCBT-MR’s 2017 Annual Course.
For just $200 ($250: non-members) you will be able to partake in three individual sessions that will provide:
• Practical, case based lectures with emphasis on the workflow and clinical applications
• Workstation based discussion of three commercial DECT technologies by faculty with clinical and research expertise.
• A friendly and interactive environment with ample opportunity for discussion
Register Today


The 40th Annual Meeting will be held at the Omni Nashville Hotel in Nashville, TN.
Make your hotel reservation with the SCBT-MR discount
A limited number of rooms have been blocked at the special SCBT-MR group rate. We anticipate that rooms will sell quickly and advise you to make reservations early, well before the cut-off date of Wednesday, August 9, 2017.


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It is with deep sadness we inform the SCBT-MR community that Richard L. Baron, MD, FSCBTMR passed away suddenly on May 4, 2017. Dr Baron was one of our most distinguished colleagues for his impact on the field of Radiology and on his many grateful mentees. As a highly respected abdominal imager, Dr Baron was president of SCBT-MR from 1998 to 1999. He also served as a very impactful, innovative President of both RSNA and Society of Gastrointestinal Radiologists, as well as on the ACR Board of Chancellors.

Rich was acum laude1972 graduate of Yale University. He earned his medical degree in 1976 at the Washington University School of Medicine in St. Louis, Mo., where he was elected to the Alpha Omega Alpha Honor Medical Society.

He did his internship in internal medicine at Yale University, followed by a radiology residency and an abdominal-radiology fellowship at the Mallinckrodt Institute of Radiology at Washington University. He taught at the University of Pennsylvania and at the University of Washington before being recruited to Pittsburgh by Mike Federle. There, Rich rose to Department Chairman and then Director of the University Physicians Practice group, the largest in the country.

In 2002, Dr Baron moved on to become Chairman of Radiology at the University of Chicago. He served ably in that role from 2002 to 2011, when he became Dean of their Clinical Practice Group, from 2011 to 2013.

Rich’s academic impact is validated by 118 peer-reviewed scientific articles, one book, 49 book chapters, and numerous review articles, plus scientific and educational exhibits. Always in demand, he has presented hundreds of invited lectures. His true love and greatest pioneering research impact was in imaging of the liver in patients with advanced fibrosis.

Dr. Baron is survived by his wife, Shirley, son Tim and daughter Christine.

The entire field of Radiology pauses to mourn his passing is testament to his kind wisdom, his warm friendship with so many, and his fatherly guidance of hundreds of trainees.

Our hearts are with the Baron family



The Society of Computed Body Tomography & Magnetic Resonance is now accepting nominations for 2017 Fellow Members.  Please read the requirements carefully before submission.

All materials must be received by August 10, 2017.  Nominations will be not accepted after that date.

To be considered for Fellow Membership, a physician or related scientist must meet the following MINIMUM requirements as determined by the Fellow Membership Committee and outlined in the bylaws of SCBT-MR. Candidates who meet these criteria will be presented to the Society for a vote by the fellows, which will occur at the Annual Meeting. Please note that while some candidates may meet the minimum requirements for nomination, that does not guarantee the achievement of Fellowship status.

Below is a list of the minimum requirements to achieve Fellowship status:

1. In order for a candidate to be considered for Fellow Membership in 2017, all nomination materials must be completed and received by August 10, 2017.  Nominations will not be accepted after that date.  The presentation of candidates and voting will occur at the annual Fellow’s meeting on September 11, 2017. 

2. The candidate will have been a general member in good standing for at least two years before applying and must attend a minimum of one meeting. 

3. The candidate must have contributed to the society in a meaningful manner within 2 years prior to consideration.  This can be accomplished by either having served on a committee, having provided educational material to the website, having contributed to an ad hoc initiative (e.g. white paper or ACR consultation) or having been a previous presenter at a recent annual meeting (e.g. scientific session, poster, workshop, round table, or case presentation).

4. Candidates for Fellow Membership must be nominated in writing by TWO Fellow Members. Nominating letters should be emailed to Katie Robbins at   

5. Physicians shall have practiced radiology for at least three years after attainment of Radiology Board Certification and scientists will have held a faculty position for at least three years prior to being considered for Fellow Membership. 

6. At least 50% of the physician’s or scientist's time shall be devoted to work in body (chest, cardiovascular, abdominal, pelvic, musculoskeletal) computed tomography and/or magnetic resonance imaging, including, but not limited to, clinical or laboratory work or teaching. Time occupied by administrative or other academic activities may be excluded. 

7. The candidate should be employed by, teach in, or maintain an affiliation with an academic department of radiology with a radiology residency program. This requirement may be waived for Fellow Members who work in academic institutions (such as NIH or AFIP) that do not have a residency program. 

8. The physician or related scientist must have had a significant role on 10 original (non-review article) peer-reviewed publications on body (as defined in #6 above) computed tomography and/or magnetic resonance imaging or related topics (minimum of 5 of these publications in the last 10 years).

9. Additionally, of these 10 publications, the physician or related scientist must have at least five publications where he/she met the following criteria:  the first author, or the second author with a trainee as first author, or the last author, where the latter designation was clearly in a senior author role. These scientific (clinical or laboratory) publications will be published in major refereed journals. Abstracts, case reports, and technical notes are not acceptable to fulfill this requirement.  

10. The candidate’s record of scholarly and service contributions should demonstrate leadership (e.g. book chapters, awards, invited lectureships, mentoring, national/international committee chairs, etc.). 

11. A copy of the candidate's curriculum vitae must be uploaded with the application.

The following items must be included for the nomination to be considered:

1. Fellow Nomination Application

2. Two letters of support from current SCBT-MR Fellows emailed to Katie Robbins at
They must describe in detail the length and nature of the relationship of the candidate to the Fellow Member and need to contain a statement of the candidate's past, present, and possible future contributions (focusing particular attention on whether the candidate will make a distinct contribution) to the Society by virtue of his/her national or international reputation, scientific accomplishments, excellence as a teacher, lecturer, researcher, or other scholarly activities. 

3. A copy of the candidate’s curriculum vitae 

4. A recent photograph of one's self

In order for a candidate to be considered for Fellow Membership in 2017, all nomination materials must be completed and received by August 10, 2017. Nominations will not be accepted after that date. Information regarding nominees will be circulated to fellows of the society approximately two weeks prior to the SCBT-MR Annual Meeting.

If you have any questions or require additional information, please contact Katie Robbins at or by telephone at (703) 476-1149.

ML 101: The Radiologist's Basic Guider

Reprinted with permission from the ACR Bulletin

From IBM's Watson to CAD, most radiologists have heard of machine learning. But
do you know how this technique is already used in the field? Plus, what does the future hold? The ACR Bulletin brings you FAQs so you can be sure to have the basics down pat.

ML 101

What is Machine Learning?

A researcher named Arthur Samuel coined the term “machine learning” (ML) in 1959. Samuel defined ML as the “field of study that gives computers the ability to learn without being explicitly programmed.” In other words, ML is the notion that machines can, over time, learn to do what we as humans do. How? Computers can capture datasets, aggregate the information in those data, and then create predictions, explains Keith J. Dreyer, DO, PhD, vice chair of radiology at Massachusetts General Hospital in Boston and assistant professor of radiology at Harvard Medical School in Cambridge, Mass.

How Does It Work?

The idea that a machine can understand how to perform a new process without specific instruction once seemed impossible. What inner workings have made ML a reality? The answer is a series of algorithms, according to J. Raymond Geis, MD, FACR, assistant professor of radiology at University of Colorado School of Medicine in Aurora, Colo., and vice chair of the ACR’s Commission on Informatics. “Instead of being programmed to do a specific task, machines rely on these algorithms to incorporate statistical methods and learn to perform the task without being specifically shown how,” Geis explains. “While these algorithms have been known for decades, the huge increase in computer-accessible data and faster computing power now make them affordable to use in daily life.”

How Is It Being Applied?

One of the most famous examples of ML is IBM’s Watson, whom you may remember from the TV game show Jeopardy! Watson won a first place prize of $1 million against human competitors. More recently, you may have also seen a commercial about Watson sorting through hundreds of medical images to make a diagnosis. But ML is already used in medicine and radiology: the most common use being computer-aided detection, or CAD. CAD uses datasets to process an image and detect conspicuous sections where disease might be present. However, even with CAD, radiologists still typically interpret images and review and contextualize the information from CAD: “It needs to be an interactive process between human and machine,” says Dreyer.

What Does It Mean for Radiology?

There’s a large initiative by academic and commercial groups to develop more valuable ML products, so “you will see the use cases boom in the future,” says Geis. He adds that ML provides an opportunity to find new and useful patterns in datasets, such as electronic health records, radiologic images, or even genomic information. Within ML, radiology seems to have become a hot topic because of the vast quantities of already labeled images, such as those on social media platforms like Facebook, which allow us to test applicability. Even though Facebook images may be of friends or scenery, rather than radiologic images, they can serve as use cases to experiment with ML recognition — for instance, the way that Facebook can now “suggest” who to tag in an image.

Dreyer explains that ML can be used for two different tasks: detection and diagnosis. Detection simply refers to identifying the presence of a finding in a radiologic image. Diagnosis could take this a step further, analyzing possible clinical classifications to identify the object, similar to how a radiologist would read an image to determine what’s going on with a patient.

What’s the Difference Between Machine Learning vs. Artificial Intelligence vs. Deep Learning?

Machine learning refers to a computer’s ability to train itself without being programmed. Artificial intelligence is what results when machine learning is put into practice and is a more mainstream, popularized term. Another common term that’s been confused with ML is deep learning, which is actually a subset of ML. Deep learning involves multiple, complex levels of computation, explains Geis. “In a simple network, you have one input layer, a single ‘hidden’ or computational layer, and one output layer. In a deep network, you have multiple hidden layers.”

What Challenges Exist?

A recent article in the New England Journal of Medicine warned that ML could “displace much of the work of radiologists and anatomical pathologists” while improving diagnostic accuracy. Despite the use of CAD and the development of even better algorithms, many remain concerned about the use of ML in image interpretation. Dreyer explains, “There’s a lot of context and information that needs to be conveyed beyond a single simple interpretation based on one finding in a complex imaging exam. Even if an algorithm finds tuberculosis on a chest CT, that doesn’t mean other diseases aren’t present. There still needs to be a comprehensive interpretation.” Geis also adds that while many are optimistic about the benefits of ML, many roadblocks exist, including the need for more research and datasets. Additionally, ML brings up many issues that need to be thought through, such as legal risk, ethical issues, and an undefined regulatory framework.

What Does the Future Hold?

Could ML actually replace radiologists? “Not anytime soon,” according to Geis. “Machine learning will, however, arrive in the next few years in the form of advanced CAD, personalized exam protocols, and tools to help with specific clinical questions.” So what’s the key for radiologists to avoid being left behind? “To see the potential of ML in radiology, we need to watch the consumer markets to see how technologies are being applied,” says Dreyer. “Companies like Google, Facebook, and IBM are driving a lot of innovation, and it will be translated into the clinical domain.”

By Alyssa Martino, freelance writer for the ACR Bulletin

Seismic Shifts

Reprinted with permission from the ACR Bulletin

Evaluating the impending impacts of the machine-learning economy


Karl Benz is credited with inventing the automobile in Germany, but Henry Ford introduced the concept of mass-produced vehicles that were economical for the everyday consumer.

Many consider machine learning as revolutionary to medicine as the automobile was to transportation, but what will it take to enter the mainstream? What are the economic shifts we can expect? And how can radiologists prepare for a machine-learning future?

For answers, let’s consider some basic lessons from Economics 101.

Lesson #1. When the cost of something falls, more people demand it. Machine-learning algorithms take available information and use it to fill in — or predict — missing information or something that is unknown (such as the presence of a disease). In this way, machine learning is a prediction technology, explains Avi Goldfarb, PhD, the Ellison Professor of Marketing at the Rotman School of Management at the University of Toronto. “As artificial intelligence improves, it will lower the cost of machine prediction,” he says. “And as the cost of prediction drops, we’ll begin using machine learning for many more tasks — including those that were never before framed as prediction problems, like medical diagnosis.”

Lesson #2. When the cost of something falls, the value of substitutes drops along with it. The substitute for machine prediction is human prediction. As the cost of machine learning falls and it starts getting used more often, the value of human prediction skills will also fall. So, if diagnostic radiology were considered only as a prediction problem, the value of radiologists would fall.

Lesson #3. When the cost of something falls, the value of complements rises. “When coffee becomes cheaper, people buy more sugar and milk, and their value rises,” says Goldfarb. “In the machine-learning economy, we believe human judgment is going to become increasingly valuable as a complement to diagnostic prediction.”

Implications for Radiology

These days, everybody is talking about IBM’s Watson, but machine learning isn’t new. In fact, Arthur Samuel, a pioneer in artificial intelligence at IBM, built a checkers-playing program as far back as 1959. What is new is the ability to apply machine learning to radiology, says Adam C. Powell, PhD, president of Payer+Provider Syndicate, a management advisory and operational consulting firm focused on the managed care and health care delivery industries.

“For machine learning to work, you need digital inputs and outputs,” says Powell. “In radiology, we are now capturing images and information digitally and thus have a digital input. Thanks to electronic medical records, we now have a digital output from the diagnostic process. We can develop algorithms to map inputs to outputs, using archives of digital images and recorded diagnoses as training data.”

So can a computer learn to do a better job of diagnosing medical problems than the radiologists who programmed it? And will the laws of economics put radiologists out of their jobs?

Not according to Howard B. Fleishon, MD, MMM, FACR, secretarytreasurer of the ACR’s Board of Chancellors. “Right now, there’s paranoia about radiologists being displaced by machine learning,” he says. “But radiology is much more than just routine image interpretation. There are complexities that require human judgment. There’s also the important role of patient and physician interaction. Clearly, our profession will change as machine learning becomes better and cheaper. But radiologists will also become more effective as a result, and the value of our judgment will continue to rise.”seismic shifts in text

New Roles for Radiology

Powell predicts that the advent of machine learning in medicine will create new roles for radiologists. “Bread-and-butter services may become more automated, but there may need to be more radiologists who have specialized knowledge in particular niches. There may also need to be ‘algorithm curators’ that help decide which algorithms best achieve specific objectives. And we may need more medical ethicists to help make judgments,” he says.

The need for patient interaction and shared decision-making may also expand. “The patient will still need someone to explain the implications of the findings,” says Powell. “How can they visualize the issue? How can they make intelligent decisions about the care pathway they wish to follow based upon the probabilities that are presented by the data? Machine learning may drive the need for more clinical radiologists who take a holistic approach.”

Mainstream Acceptance

What will it take for machine learning to become mainstream in medicine? The answer, says Goldfarb, is surprisingly simple: “It just has to be better than a person. Are we there yet in radiology? Not quite. But as more data accumulates, as the research progresses, there will be an increasing number of situations in which machines are able to automatically make diagnostic predictions with a greater degree of accuracy than a person would.”

Powell agrees. “It’s not going to be all or nothing, and there are many different types of problems to solve in radiology,” he says. “It may be easier for a machine to perform better than a person at evaluating some very constrained, frequently seen clinical situations. It will probably be much harder for a machine to be better at irregular, complex, or rare problems.”

Key Takeaways

What do radiologists need to know now to prepare for the machine-learning future? The most important takeaway, says Goldfarb, is that these are prediction technologies, and so the parts of the job that involve prediction will increasingly be done by machine. “But don’t panic,” he advises. “While machines may consume some aspects of your job, we’ll start to see the rise of a whole lot of complementary things that still require human judgment. As a profession, radiologists should identify and invest in the skills that are complementary to better interpretation of images.”

Fleishon believes that machine learning will pave the way for incredible opportunities for radiologists. “Not only will it supplement our interpretive skills, but applying algorithms to personalized medicine and using massive data sets to drive new research initiatives could dramatically improve the future of patient care and our profession.”

By Linda G. Sowers, freelance writer for the ACR Bulletin

SCBT-MR is trying to increase the society's out reach of and involve more members in society information. The communications committee hopes to use social media to create a space where members can stay up to date, and connect on society news. As well as learn from recent research and  members can share and discuss relevant materials.  Follow us, and interact with the various posts. The more interaction on the social media pages, the boarder viewership.


Recent Posts include:

June 19: 2017 Gold Medal Recipients  
June 15: 7 steps to better AI algorithms
June 14: MRI as accurate as CT for Crohn’s disease detection, management
June 12: The 2017 Meeting program is now online.
June 10: Multi-Energy Workshop is back for #SCBTMR2017! Three individual & unique sessions.





Alec Megibow MD, MPH, FSCBTMR, Brian Herts, MD, FSCBTMR  and Mark Baker, MD, FSCBTMR traveled to Singapore. As part of the trip they visited National University Hospital in Singapore to present a mini-symposium honoring Joseph K. T. Lee, M.D., FACR, FSCBTMR - the 2016 SCBT-MR Gold Medal winner.


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SCBT-MR members are invited to share news and update their fellow members or themselves. This is a great opportunity to share awards, achievements, promotions, or praise a fellow member. Member News will be published in the SCBT-MR quar-terly newsletter. Please send the information you wish to share to with the subject line "Member News".




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