DisC-Diff: Disentangled Conditional Diffusion Model for Multi-contrast MRI Super-Resolution

Ye Mao, Lan Jiang, Xi Chen, Chao Li

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

1 Citation (Scopus)

Abstract

Multi-contrast magnetic resonance imaging (MRI) is the most common management tool used to characterize neurological disorders based on brain tissue contrasts. However, acquiring high-resolution MRI scans is time-consuming and infeasible under specific conditions. Hence, multi-contrast super-resolution methods have been developed to improve the quality of low-resolution contrasts by leveraging complementary information from multi-contrast MRI. Current deep learning-based super-resolution methods have limitations in estimating restoration uncertainty and avoiding mode collapse. Although the diffusion model has emerged as a promising approach for image enhancement, capturing complex interactions between multiple conditions introduced by multi-contrast MRI super-resolution remains a challenge for clinical applications. In this paper, we propose a disentangled conditional diffusion model, DisC-Diff, for multi-contrast brain MRI super-resolution. It utilizes the sampling-based generation and simple objective function of diffusion models to estimate uncertainty in restorations effectively and ensure a stable optimization process. Moreover, DisC-Diff leverages a disentangled multi-stream network to fully exploit complementary information from multi-contrast MRI, improving model interpretation under multiple conditions of multi-contrast inputs. We validated the effectiveness of DisC-Diff on two datasets: the IXI dataset, which contains 578 normal brains, and a clinical dataset with 316 pathological brains. Our experimental results demonstrate that DisC-Diff outperforms other state-of-the-art methods both quantitatively and visually.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2023
Subtitle of host publication26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part X
EditorsHayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
Place of PublicationCham
PublisherSpringer
Pages387-397
Number of pages11
Edition1
ISBN (Electronic)9783031439995
ISBN (Print)9783031439988
DOIs
Publication statusPublished - 1 Oct 2023
Event26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver Convention Centre Canada, Vancouver, Canada
Duration: 8 Oct 202312 Oct 2023
Conference number: 26
https://conferences.miccai.org/2023/en/ (Link to conference information)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14229
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Abbreviated titleMICCAI
Country/TerritoryCanada
CityVancouver
Period8/10/2312/10/23
Internet address

Keywords

  • Conditional diffusion model
  • Magnetic resonance imaging
  • Multi-contrast super-resolution

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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