RUN-UP: Accelerated multishot diffusion-weighted MRI reconstruction using an unrolled network with U-Net as priors.

ABSTRACT:

Purpose

To accelerate and improve multishot diffusion-weighted MRI reconstruction using deep learning.

Methods

An unrolled pipeline containing recurrences of model-based gradient updates and neural networks was introduced for accelerating multishot DWI reconstruction with shot-to-shot phase correction. The network was trained to predict results of jointly reconstructed multidirection data using single-direction data as input. In vivo brain and breast experiments were performed for evaluation.

Results

The proposed method achieves a reconstruction time of 0.1 second per image, over 100-fold faster than a shot locally low-rank reconstruction. The resultant image quality is comparable to the target from the joint reconstruction with a peak signal-to-noise ratio of 35.3 dB

SUBMITTER: Hu Y 

PROVIDER: S-EPMC8095163 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature