University of Wisconsin–Madison

Year: 2020

New Paper Published on CT Synthesis from MRI using Deep Learning

MIMRTL team members Haley Massa and Dr. Alan McMillan published a new paper in Physics in Medicine and Biology entitled ‘Comparison of deep learning synthesis of synthetic CTs using clinical MRI inputs’ that studies the use different types of MRI input images to synthesize CT images. Link: https://doi.org/10.1088/1361-6560/abc5cb

New Paper Published on Lightweight MRI Coils for Improved PET Quality in Simultaneous PET/MR

MIMRTL team members Nick Mathew, Dr. Samuel Hurley, and Dr. Alan McMillan published a new paper in Radiology entitled ‘PET Image Quality Improvement for Simultaneous PET/MRI with a Lightweight MRI Surface Coil’ that demonstrates improved PET image quality utilizing lightweight AIR coils in comparison to conventional anterior array coils. Link: https://doi.org/10.1148/radiol.2020200967

New Review Paper Published on the Application and Construction of Deep Learning Networks

MIMRTL team members Maribel Torres-Velazquez, Wei-Jie Chen, Xue Li, and Dr. Alan McMillan published a new paper in IEEE Transactions on Radiation and Plasma Medical Sciences entitled ‘Application and construction of deep learning networks in medical imaging’ that outlines the steps necessary for the application of deep learning to medical imaging data. Link: https://doi.org/10.1109/TRPMS.2020.3030611

New Paper Published on Rapid MRI Coil Development

MIMRTL team members Dr. Bruce Collick, Dr. Bahareh Bezhadnezhad, Dr. Samuel Hurley, and Dr. Alan McMillan published a new paper in Physics in Medicine and Biology entitled ‘Rapid development of application-specific flexible MRI receive coils’ that describes rapid prototyping of MRI coils utilizing lightweight and flexible AIR coil technology. Link: https://doi.org/10.1088/1361-6560/abaffb

Dr. McMillan writes editorial for the journal Radiology on continuous learning in artificial intelligence for medical imaging

Dr. McMillan from MIMRTL recently wrote an editorial for the journal Radiology, entitled “Making Your AI Smarter: Continuous Learning Artificial Intelligence for Radiology.” Continuous learning is strategy that allows models to evolve and improve over time. It will have substantial implications as more and more deep learning applications are clinically approved for medical imaging. Read …

Dr. McMillan Receives NSF Grant

Dr. McMillan is co-PI on a new NSF award, from the division Electrical, Communications and Cyber Systems, with Professor Nader Behdad, PhD (PI,  Department of Electrical and Computer Engineering). The title of the award is “Traveling Wave Approaches for Improved Sensitivity and Spatial Coverage in Continuous-Wave (CW) Electron Paramagnetic Resonance Imaging.”

New NIH R01 for Robust Deep Learning in Medical Imaging!

Dr. McMillan, along with Associate Professor Po-Ling Loh, PhD (Statistics) and Assistant Professor  Varun Jog, PhD (Electrical and Computer Engineering)  were awarded an R01 from the National Library of Medicine. The title of the project is “Can Machines Be Trusted? Robustification of Deep Learning for Medical Imaging.”  

Congrats Alex Kaeck, MS!

Congratulations to MIMRTL graduate student Alex Kaeck for successfully defending his Masters’ thesis in Medical Physics. Alex’s thesis is entitled “Quantitative Whole-Body Dynamic PET Imaging and Applications.” After graduation, Alex is beginning a medical physics residency at Naval Medical Center Portsmouth. Congrats, Alex!

Congrats, 2020 Grads!

Congrats to our undergraduate MIMRTL researchers who graduated recently. This includes: Grace Herbeck (Dec 2019), Murad Jaber (May 2020), Nick Mathew (May 2020), Tyler Walters (May 2020).