An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data.

ABSTRACT:

Purpose

To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data.

Methods

The ConvDecoder (CD) neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle data. Fully-sampled variable-flip angle k-space data were retrospectively accelerated by factors of R = {8, 12, 18, 36} and reconstructed with CD, CD with the proposed regularization (CD + r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1). Final images from CD + r training were evaluated at the "argmin" of the regularization loss; whereas the CD, LR, and L1 reconstructions were cho

SUBMITTER: Slavkova KP 

PROVIDER: S-EPMC9892348 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature