Deep, deep learning with BART.

ABSTRACT:

Purpose

To develop a deep-learning-based image reconstruction framework for reproducible research in MRI.

Methods

The BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing. In this work, BART was extended by a nonlinear operator framework that provides automatic differentiation to allow computation of gradients. Existing MRI-specific operators of BART, such as the nonuniform fast Fourier transform, are directly integrated into this framework and are complemented by common building blocks used in neural networks. To evaluate the use of the framework for advanced deep-learning-based reconstruction, two state-of-the-art unrolled reconstruction networks, namely the Variational Network and MoDL,

SUBMITTER: Blumenthal M 

PROVIDER: S-EPMC10898647 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature