Deep Learning Single-Frame and Multiframe Super-Resolution for Cardiac MRI.

ABSTRACT: Background Cardiac MRI is limited by long acquisition times, yet faster acquisition of smaller-matrix images reduces spatial detail. Deep learning (DL) might enable both faster acquisition and higher spatial detail via super-resolution. Purpose To explore the feasibility of using DL to enhance spatial detail from small-matrix MRI acquisitions and evaluate its performance against that of conventional image upscaling methods. Materials and Methods Short-axis cine cardiac MRI examinations performed between January 2012 and December 2018 at one institution were retrospectively collected for algorithm development and testing. Convolutional neural networks (CNNs), a form of DL, were trained to perform super resolution in image space by using synthetically generated low-resolution data. There wer

SUBMITTER: Masutani EM 

PROVIDER: S-EPMC7263289 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature