An Artificial Intelligence Method for Auto-Compressed Sensing and Blind Deconvolution in Magnetic Resonance Imaging Data of Shoulder Muscle Metabolism

Johnson, Talon, Dallas TX

Investigators

Abstract

This award is made as part of the FY 2021 Mathematical and Physical Sciences Ascending Postdoctoral Research Fellowships, MPS-Ascend Program. Talon Johnson is awarded this fellowship to conduct a program of research and education in the mathematical sciences, including applications to other disciplines, at University of Texas Southwestern Medical Center under the mentorship of the sponsoring scientists Jimin Ren and Anke Henning. This is a research project aimed at development of computational image reconstruction methods for Magnetic Resonance Imaging (MRI). Johnson plans to design compressed sensing methods that are capable of reconstructing image data from MRI measurements that have been affected by the subject's motion, such as respiratory-related motion in MRI imaging of shoulder muscle metabolism. Along with this research, Johnson will be involved in activities to broaden participation of underrepresented minorities in mathematical sciences through the recruitment of students from the University of Texas at Arlington and a collaboration with the Atlanta University Center Data Science Initiative. This research is aimed at improving image precision and quality by completing three research objectives: 1) Develop and refine the methodology for reconstruction of compressed sensing data for blurred MRI imaging using l1-l2 minimization scheme, based on l1-magic for compressed sensing and l2 minimization for deconvolution of a fixed kernel function, 2) develop an AI method for auto-recognition of any unknown kernel functions from blurred compressed MRI data sets to achieve simultaneous compressed sensing - blind deblurring of any simulated MRI data, 3) refine the AI method for auto-compressed sensing and blind deblurring of experimental data in MRI imaging of shoulder muscle metabolism and improve imaging precision. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.