Feature | Cardiac Imaging | March 11, 2019 | By Greg Freiherr

How AI Can Unlock Data in CT and MRI Scans

Acquiring these data could change patient management

Collage provided by Albert Hsiao

Collage depicts broad applications in machine learning or deep learning (DL) that can be applied to advanced medical imaging technologies. Size of the liver and its fat fraction — 22 percent — (top middle in collage) can be quantified automatically using an algorithm developed by Dr. Albert Hsiao and his team at the University of California San Diego. This and other information that might be mined by DL algorithms from CT and MR images could help personalize patients’ treatment. Collage provided by Albert Hsiao

Greg Freiherr

Greg Freiherr

Computed tomography (CT) and magnetic resonance imaging (MRI) scans are chock full of information that might be used to personalize treatment. Unfortunately, this information is not readily available. The latest type of artificial intelligence (AI) algorithms, however, might change that, according to Albert Hsiao, M.D., assistant professor of radiology at the University of California San Diego.

During a scientific session March 16 of this year’s annual meeting (#ACC19) of the American College of Cardiology, Hsiao said he plans to discuss how smart algorithms might extract this information from CT and MRI scans and how its availability could change patient management.

A key takeaway of his presentation, to be given as part of a scientific session titled “Man vs. Machine: Current and Future Applications of Machine Learning in Cardiovascular Imaging,” will be that the “explosive growth” of machine learning has raised “opportunities for tying (this information) directly to patient management,” he said. “This is in large part because of the amazing democratization of machine learning that this new area of ‘deep learning’ has enabled.”

Physicians might use this information to create “imaging phenotypes” of patients. These phenotypes, in turn, might be used to personalize the treatment of patients.

“Information like how much body fat you have, how fat is your liver, how much is your muscle mass — those are rich pieces of information that if quantified could really help us guide and make more specific patient recommendations,” said Hsiao, who seven years ago co-founded Arterys, a modern day provider of cloud-based AI software for medical imaging.

Imaging phenotypes would characterize the physical aspects of a patient, seen in medical images. For the time being the information remains embedded in these images, he said. But smart algorithms might extract and quantify it. Doing so could give clinicians insights into exactly what their patients need, allowing an unprecedented degree of personalization.

 

DL Growth To Be Cited at #ACC19

Deep learning (DL), also called machine learning or unsupervised learning, allows algorithms to discover rules for distinguishing certain features of images. In the early days of AI, DL allowed some algorithms to distinguish pictures of cats from those of dogs. Rather than coding the rules for distinguishing these pictures, the algorithms “learned” them.

Today’s DL algorithms might similarly learn how to pick out and extract phenotypic information from CT and MR images. They would do so by leveraging convolutional neural networks (CNNS), which are typically used to analyze images.

 

Deep learning — and CNNs — might be applied in any medical imaging modalities, including echocardiography and X-ray angiography. But the three-dimensional nature of CT and MRI make these two modalities attractive, especially when it comes to AI efforts aimed at quantitation, Hsiao said, for example, determining the fat fraction of a liver.

Over the past eight years, Hsiao has co-authored articles primarily on smart algorithms in cardiac MRI. But there is also “quite a bit of open terrain” regarding CT, Hsiao said: “There's some interesting work for coronary CT and estimating CT-FFR (fractional flow reserve) with machine learning.”

 

How To Generalize Algorithms

The challenge facing AI developers is to validate these algorithms across multiple patient populations. Algorithms typically are developed and tested in a single population type. This may be exemplified by a cohort of 200 to 300 patients. Achieving “generalizability” may be especially important when trying to dig information out of CT and MR images — information needed to classify patients according to “imaging phenotypes” — because different types of patients may express different types of imaging phenotypes.

The challenge is not, however, as difficult as might be imagined, Hsiao said: “We just basically need the data from these other demographics.”

The data are required, he said, not only to refine the algorithms but to prove that they work on different types of patients. This proof is needed if the algorithms are to have wide clinical utility — and, consequently, to establish the value of unused data now buried in CT and MR images.

 

Greg Freiherr is a contributing editor to Imaging Technology News (ITN). Over the past three decades, Freiherr has served as business and technology editor for publications in medical imaging, as well as consulted for vendors, professional organizations, academia, and financial institutions.

 

Related content:

Applications for Artificial Intelligence in Cardiovascular Imaging

Machine Learning Approaches in Cardiovascular Imaging

Technology Report: Artificial Intelligence (Video report published January 2019) 

PODCAST: Shortcomings of CTA in Cardiology

PODCAST: How Technology Is Changing Cardiology

ACC.19 Future Hub Hosts “Shark Tank” of Emerging Technologies In Cardiology

 

Related Content

Ebit and DiA Imaging Analysis Partner on AI-based Cardiac Ultrasound Analysis
News | Cardiovascular Ultrasound | April 16, 2019
DiA Imaging Analysis has partnered with the Italian healthcare IT company Ebit (Esaote Group), to offer DiA’s LVivo...
DiA Imaging Analysis Introduces LVivo SAX Ultrasound Analysis Tool
Technology | Cardiovascular Ultrasound | April 09, 2019
DiA Imaging Analysis announced the launch of LVivo SAX, a cardiac analysis tool that helps clinicians quickly and...
Videos | RSNA | April 03, 2019
ITN Editor Dave Fornell takes a tour of some of the most interesting new medical imaging technologies displa
Four of the top pieces of content in March included news on proton therapy, including a 360 image and videos from ITN's recent visit to the Northwestern Medicine Proton Center in the Chicago suburbs. This image shows the main proton treatment room gantry at the proton center in Warrenville, Ill. Interview with Mark Pankuch, Ph.D.

Four of the top pieces of content in March included news on proton therapy, including a 360 image and videos from ITN's recent visit to the Northwestern Medicine Proton Center in the Chicago suburbs. This image shows the main proton treatment room gantry at the proton center in Warrenville, Ill.
 

Feature | April 02, 2019 | Dave Fornell, Editor and A.J. Connell
April 2, 2019 — Here is the list of the most popular content on the Imaging Technology News (ITN) magazine w
At #ACC.19, Siemens unveiled a version of its go.Top platform optimized for cardiovascular imaging. The newly packaged scanner can generate the data needed to do CT-based FFR (fractional flow reserve).

At #ACC.19, Siemens unveiled a version of its go.Top platform optimized for cardiovascular imaging. The newly packaged scanner can generate the data needed to do CT-based FFR (fractional flow reserve). Photo by Greg Freiherr

Feature | Cardiac Imaging | March 22, 2019 | By Greg Freiherr
Reflecting a trend toward the increased use of...
HeartFlow Analysis Successfully Stratifies Heart Disease Patients at One Year
News | CT Angiography (CTA) | March 19, 2019
Late-breaking results confirm the HeartFlow FFRct (fractional flow reserve computed tomography) Analysis enables...