Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement

Yuan Xue, Blake E. Dewey, Lianrui Zuo, Shuo Han, Aaron Carass, Peiyu Duan, Samuel W. Remedios, Dzung L. Pham, Shiv Saidha, Peter A. Calabresi, Jerry L. Prince

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

2 Scopus citations

Abstract

Magnetic resonance imaging (MRI) with gadolinium contrast is widely used for tissue enhancement and better identification of active lesions and tumors. Recent studies have shown that gadolinium deposition can accumulate in tissues including the brain, which raises safety concerns. Prior works have tried to synthesize post-contrast T1-weighted MRIs from pre-contrast MRIs to avoid the use of gadolinium. However, contrast and image representations are often entangled during the synthesis process, resulting in synthetic post-contrast MRIs with undesirable contrast enhancements. Moreover, the synthesis of pre-contrast MRIs from post-contrast MRIs which can be useful for volumetric analysis is rarely investigated in the literature. To tackle pre- and post- contrast MRI synthesis, we propose a BI-directional Contrast Enhancement Prediction and Synthesis (BICEPS) network that enables disentanglement of contrast and image representations via a bi-directional image-to-image translation (I2I) model. Our proposed model can perform both pre-to-post and post-to-pre contrast synthesis, and provides an interpretable synthesis process by predicting contrast enhancement maps from the learned contrast embedding. Extensive experiments on a multiple sclerosis dataset demonstrate the feasibility of applying our bidirectional synthesis and show that BICEPS outperforms current methods.

Original languageEnglish (US)
Title of host publicationSimulation and Synthesis in Medical Imaging - 7th International Workshop, SASHIMI 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsCan Zhao, David Svoboda, Jelmer M. Wolterink, Maria Escobar
PublisherSpringer Science and Business Media Deutschland GmbH
Pages55-65
Number of pages11
ISBN (Print)9783031169793
DOIs
StatePublished - 2022
Event7th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: Sep 18 2022Sep 18 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13570 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period9/18/229/18/22

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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