Drs. Maryam Vareth and Akram Bayat offer this project (#1) through UC Berkeley's Undergraduate Research Apprentice Program (URAP).
One of the fastest growing fields of research in medical imaging during the last several years is the use of machine learning methods for image reconstruction.
This project aims to use deep learning approaches in image reconstruction to accelerate Magnetic Resonance Imaging (MRI) acquisition and in result reduce MRI examination times for patients. Two of the most influential development in this area during the last two decades have been parallel imaging and compressed sensing. Both of these rapid imaging techniques are based on the principle of reducing the number of lines that are acquired in k-space, which reduces the scan time and then exploiting the redundancy in measured data during the image reconstruction. In Parallel imaging, the redundancy arises from the simultaneous acquisition of MR signal with multiple receive coils; in compressed sensing, it derives from the observation that images are generally compressible. Machine learning approaches have adopted similar strategies for the acceleration of MRI, which set the main design criteria for this project. The majority of existing work have focused on designing better reconstruction models given a pre-determined acquisition trajectory, ignoring the question of trajectory optimization. In this project, we also focus on learning acquisition trajectories given a fixed image reconstruction model.
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