Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
Abstract
Compressed sensing in MRI enables high subsampling factors while maintaining diagnostic image quality. This technique enables shortened scan durations and/or improved image resolution. Further, compressed sensing can increase the diagnostic information and value from each scan performed. Overall, compressed sensing has significant clinical impact in improving the diagnostic quality and patient experience for imaging exams. However, a number of challenges exist when moving compressed sensing from research to the clinic. These challenges include hand-crafted image priors, sensitive tuning parameters, and long reconstruction times. Data-driven learning provides a solution to address these challenges. As a result, compressed sensing can have greater clinical impact. In this tutorial, we will review the compressed sensing formulation and outline steps needed to transform this formulation to a deep learning framework. Supplementary open source code in python will be used to demonstrate this approach with open databases. Further, we will discuss considerations in applying data-driven compressed sensing in the clinical setting.
Keywords
Cite
@article{arxiv.1903.07824,
title = {Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning},
author = {Joseph Y. Cheng and Feiyu Chen and Christopher Sandino and Morteza Mardani and John M. Pauly and Shreyas S. Vasanawala},
journal= {arXiv preprint arXiv:1903.07824},
year = {2019}
}
Comments
Submitted to the Special Issue on Computational MRI: Compressed Sensing and Beyond in the IEEE Signal Processing Magazine