Cited by 254Advanced MRI Techniques and ApplicationsGenerative Adversarial Networks and Image SynthesisArtificial Intelligence in Healthcare and Education

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Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network

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Sharma, Anmol, Hamarneh, Ghassan·2020·IEEE Transactions on Medical Imaging

DOI 10.1109/tmi.2019.2945521

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Abstract

Magnetic resonance imaging (MRI) is being increasingly utilized to assess, diagnose, and plan treatment for a variety of diseases. The ability to visualize tissue in varied contrasts in the form of MR pulse sequences in a single scan provides valuable insights to physicians, as well as enabling automated systems performing downstream analysis. However, many issues like prohibitive scan time, image corruption, different acquisition protocols, or allergies to certain contrast materials may hinder the process of acquiring multiple sequences for a patient. This poses challenges to both physicians and automated systems since complementary information provided by the missing sequences is lost. In this paper, we propose a variant of generative adversarial network (GAN) capable of leveraging redundant information contained within multiple available sequences in order to generate one or more missing sequences for a patient scan. The proposed network is designed as a multi-input, multi-output network which combines information from all the available pulse sequences and synthesizes the missing ones in a single forward pass. We demonstrate and validate our method on two brain MRI datasets each with four sequences, and show the applicability of the proposed method in simultaneously synthesizing all missing sequences in any possible scenario where either one, two, or three of the four sequences may be missing. We compare our approach with competing unimodal and multi-modal methods, and show that we outperform both quantitatively and qualitatively.

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Authors: Sharma, A., & Hamarneh, G.
Affiliations: Simon Fraser University (CA)
ORCID: Sharma, Anmol
Hamarneh, Ghassan
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-04
Volume: 39
Issue: 4
Pages: 1170-1183
ISSN: 0278-0062 (ISSN-L)
1558-254X
DOI: 10.1109/tmi.2019.2945521
OpenAlex: W2941736865
Cited by: 254
References: 51
Citations by year: 2026: 37 · 2025: 66 · 2024: 50 · 2023: 32 · 2022: 31 · 2021: 20 · 2020: 17
FWCI: 11.39
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Medical Imaging (publishedVersion)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
Summit (Simon Fraser University) (submittedVersion)
arXiv (Cornell University)
Funders: Nvidia
Compute Canada
Western Canada Research Grid
Natural Sciences and Engineering Research Council of Canada
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: brain MRI, scan time reduction, multi-contrast MRI
MeSH terms: Deep Learning, Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging

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Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network

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