Jeffrey Fessler : Joint Optimization and Learning for Image Reconstruction in MRI скачать в хорошем качестве

Jeffrey Fessler : Joint Optimization and Learning for Image Reconstruction in MRI

Recording of Jeffrey Fessler’s (University of Michigan) talk on April 21, 2022, at the EPFL Seminar Series in Imaging. Abstract. Machine learning approaches to medical image reconstruction are of considerable recent interest, especially supervised approaches that use a corpus of training data. Accelerated MRI scans, where fewer k-space points than image voxels are acquired, is a natural setting for such reconstruction methods. Recently, machine learning methods for optimizing the k-space sampling have also had growing interest. This talk will summarize recent work where we jointly optimize non-Cartesian k-space sampling, heeding physical constraints like gradient slew rate, and a learning-based image reconstruction method. Joint work with Guanhua Wang, Tianrui Luo, Jon Nielsen, and Doug Noll, based on http://arxiv.org/abs/2101.11369 See upcoming talks on: imagingseminars.org The EPFL Seminar Series in Imaging is run by EPFL Center for Imaging: imaging.epfl.ch