Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data.

ABSTRACT:

Purpose

To develop a strategy for training a physics-guided MRI reconstruction neural network without a database of fully sampled data sets.

Methods

Self-supervised learning via data undersampling (SSDU) for physics-guided deep learning reconstruction partitions available measurements into two disjoint sets, one of which is used in the data consistency (DC) units in the unrolled network and the other is used to define the loss for training. The proposed training without fully sampled data is compared with fully supervised training with ground-truth data, as well as conventional compressed-sensing and parallel imaging methods using the publicly available fastMRI knee database. The same physics-guided neural network is used for both proposed SSDU and supervised training. The S

SUBMITTER: Yaman B 

PROVIDER: S-EPMC7811359 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature