Deep learning reconstruction for cardiac magnetic resonance fingerprinting T₁ and T₂ mapping.

ABSTRACT:

Purpose

To develop a deep learning method for rapidly reconstructing T₁ and T₂ maps from undersampled electrocardiogram (ECG) triggered cardiac magnetic resonance fingerprinting (cMRF) images.

Methods

A neural network was developed that outputs T₁ and T₂ values when given a measured cMRF signal time course and cardiac RR interval times recorded by an ECG. Over 8 million cMRF signals, corresponding to 4000 random cardiac rhythms, were simulated for training. The training signals were corrupted by simulated k-space undersampling artifacts and random phase shifts to promote robust learning. The deep learning reconstruction was evaluated in Monte Carlo simulations for a variety of cardiac rhythms and compared with dictionary-based patt

SUBMITTER: Hamilton JI 

PROVIDER: S-EPMC10250104 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature