SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction.

ABSTRACT:

Purpose

To develop and evaluate a novel deep learning-based reconstruction framework called SANTIS (Sampling-Augmented Neural neTwork with Incoherent Structure) for efficient MR image reconstruction with improved robustness against sampling pattern discrepancy.

Methods

With a combination of data cycle-consistent adversarial network, end-to-end convolutional neural network mapping, and data fidelity enforcement for reconstructing undersampled MR data, SANTIS additionally utilizes a sampling-augmented training strategy by extensively varying undersampling patterns during training, so that the network is capable of learning various aliasing structures and thereby removing undersampling artifacts more effectively and robustly. The performance of SANTIS was demonstrated for accel

SUBMITTER: Liu F 

PROVIDER: S-EPMC6660404 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature