A new study at The University of Texas at Arlington could help substantially reduce the amount of radiation heart patients receive during imaging procedures without sacrificing diagnostic accuracy.
The study, led by Mingwu Jin, professor of physics, will focus on computed tomography angiography (CTA), an advanced imaging test that noninvasively assesses coronary artery disease, the world's leading cause of death. CTA combines a CT scan with iodine contrast dye injected into a patient's vein to create 3D images of blood vessels and surrounding tissues throughout the body. Doctors can use CTA to detect aneurysms, narrowed or damaged blood vessels, blood clots, tumors and other conditions.
The project is funded by a three-year, $579,745 grant from the National Heart, Lung, and Blood Institute, part of the National Institutes of Health. The project is titled "Deep leaRning-bAsed reConstruction (DRAC) for Multi-Phase Cardiac CT Angiography."
Multi-phase coronary CTA (MP-CCTA) captures images at multiple points during the heartbeat, providing doctors with more information and helping reduce blurring caused by the heart's movement. The drawback is that collecting images across multiple phases exposes patients to more radiation than a single-phase scan. Dr. Jin's research aims to substantially reduce that radiation exposure while preserving the quality and diagnostic value of the images.
"Lowering the radiation dose in medical imaging has been vigorously pursued due to imaging prevalence and potential cancer risk of ionizing radiation," Jin said. "Although notable progress has been made by developing advanced reconstruction methods, the dose reduction potential is far from its limit."
CT scans use reconstruction methods to turn raw data collected during a scan into images doctors can interpret. One advanced approach is model-based iterative reconstruction (MBIR), an algorithm designed to generate high-quality cross-sectional images while potentially using less radiation. Another is 4D reconstruction, which adds the dimension of time to standard 3D imaging. In cardiac imaging, this technique can capture clear views of fast-moving heart valves and coronary arteries while reducing motion artifacts.
Jin said that recent breakthroughs in deep learning provide an unprecedented opportunity to substantially lower radiation doses by combining MBIR with information learned from a large database of high-quality MP-CCTA images. The resulting 4D DRAC method would reconstruct a patient's images using not only data from that individual scan, but also information learned from a much larger collection of images.
Jin said the project could substantially lower the radiation dose needed for MP-CCTA, potentially improving its use in detecting the narrowing of coronary arteries and monitoring the progression of high-risk plaque. He said the 4D DRAC methods could also be applied to other dynamic CT scans, which use a series of images to track changes over time.
To test the hypothesis, Jin and his team will:
- Build a synthetic patient library using computational human models based on retrospectively collected MP-CCTA data from real patients.
- Develop 3D and 4D deep-learning methods that combine CT imaging physics with features learned from the high-quality patient data.
- Determine the lowest radiation dose that can be used without compromising diagnostic accuracy. The team will evaluate diagnostic accuracy using a computer model designed to mimic how a radiologist assesses an image, then validate the results through a small-scale study involving human observers.
The Mayo Clinic in Rochester, Minnesota, will provide MP-CCTA patient data for the study.
"This award reflects the high quality of Dr. Jin's research and the strength of his proposed ideas," said Alex Weiss, professor of physics and former department chair. "I know a great deal of work goes into preparing a successful proposal of this kind. This is a significant achievement for Dr. Jin and a major benefit to our department."
Graduate students Noor Rasel and Darnisha Langlais will work with Jin on the project. The grant will also allow Jin to hire undergraduate students in the summers, providing them with research opportunities to apply AI techniques to medical imaging.
