VS-Net: variable splitting network for accelerated parallel MRI reconstruction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

  • Jo Schlemper
  • Chen Qin
  • Cheng Ouyang
  • Wenjia Bai
  • Carlo Biffi
  • Ghalib Bello
  • Ben Statton
  • Declan P. O’regan
  • Daniel Rueckert

Colleges, School and Institutes

Abstract

In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data. We formulate the generalized parallel compressed sensing reconstruction as an energy minimization problem, for which a variable splitting optimization method is derived. Based on this formulation we propose a novel, end-to-end trainable deep neural network architecture by unrolling the resulting iterative process of such variable splitting scheme. VS-Net is evaluated on complex valued multi-coil knee images for 4-fold and 6-fold acceleration factors. We show that VS-Net outperforms state-of-the-art deep learning reconstruction algorithms, in terms of reconstruction accuracy and perceptual quality. Our code is publicly available at https://github.com/j-duan/VS-Net.

Details

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019
Subtitle of host publication22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV
EditorsDinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
Publication statusPublished - 10 Oct 2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention - Shenzhen, China
Duration: 13 Oct 201917 Oct 2019

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11767
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention
Abbreviated titleMICCAI 2019
CountryChina
CityShenzhen
Period13/10/1917/10/19