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Pixel Club: Sparsity-based Methods for Solving Inverse Problems in Medical Imaging
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Efrat Shimron (UC Berkeley)
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Tuesday, 4.8.2020, 11:30
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Zoom Lecture:
Although Magnetic Resonance Imaging (MRI) is a superb medical imaging modality, its clinical use is limited by its long acquisition time. The acquisition time can be shortened by sampling data with a sub-Nyquist rate; however, this requires suitable methods for solving the ill-posed inverse problem of accurate image reconstruction from highly subsampled data.

In this seminar, four novel methods for solving the MRI reconstruction problem will be presented. These methods build upon the well-established Compressed Sensing (CS) framework and utilize a-priori knowledge about the sparsity of the MRI data in the Wavelet Transform domain. The first two methods accelerate static MRI scans by introducing the Convolution-based Reconstruction (CORE) framework, which offers a parameter-free non-iterative reconstruction. Experiments with in-vivo 7T brain data demonstrated that these methods perform comparably to the well-established GRAPPA and l1-SPIRiT methods, with the advantage of shorter computation times and reduced need for parameter calibration. The next two developed methods accelerate dynamic MRI scans for providing accurate temperature monitoring in High Intensity Focused Ultrasound (MRgHIFU) thermal ablation treatments. The developed methods enable rapid MR monitoring by reconstructing temperature changes from sub-Nyquist sampled data. Validation experiments performed with phantoms and in-vivo data from clinical human treatments showed that the proposed methods significantly outperform two state-of-the-art methods in the field.

Short Bio:
Efrat Shimron obtained two bachelor’s degrees from the Technion, in Physics and in Electrical Engineering, both with honors. She later obtained an M.Sc. in Medical Sciences and a Ph.D. in Biomedical Engineering, also from the Technion. Efrat has received the Katzir Fellowship for Excelling Young Israeli Scientists and the Summa Cum Laude Award of the International Society for Magnetic Resonance in Medicine (ISMRM). Since June 2020 she is a post-doctoral fellow in the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, working with Prof. Miki Lustig from UC Berkeley and Prof. Shreyas Vasanawala from Stanford on developing rapid medical imaging methods.
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