Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction

Jo Schlemper, Seyed Sadegh Mohseni Salehi, Prantik Kundu, Carole Lazarus, Hadrien Dyvorne, Daniel Rueckert, Michal Sofka

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

Deep learning for accelerated magnetic resonance (MR) image reconstruction is a fast growing field, which has so far shown promising results. However, most works are limited in the sense that they assume equidistant rectilinear (Cartesian) data acquisition in 2D or 3D. In practice, a reconstruction from nonuniform samplings such as radial and spiral is an attractive choice for more efficient acquisitions. Nevertheless, it has less been explored as the reconstruction process is complicated by the necessity to handle non-Cartesian samples. In this work, we present a novel approach for reconstructing from nonuniform undersampled MR data. The proposed approach, termed nonuniform variational network (NVN), is a convolutional neural network architecture based on the unrolling of a traditional iterative nonlinear reconstruction, where the knowledge of the nonuniform forward and adjoint sampling operators are efficiently incorporated. Our extensive evaluation shows that the proposed method outperforms existing state-of-the-art deep learning methods, hence offering a method that is widely applicable to different imaging protocols for both research and clinical deployments.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer Science and Business Media Deutschland GmbH
Pages57-64
Number of pages8
ISBN (Print)9783030322472
DOIs
StatePublished - 2019
Externally publishedYes
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 13 Oct 201917 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/TerritoryChina
CityShenzhen
Period13/10/1917/10/19

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