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

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14229))

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.

Y. Mao and L. Jiang—Contribute equally in this work.

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Notes

  1. 1.

    https://brain-development.org/ixi-dataset/.

  2. 2.

    https://bit.ly/3yethO4.

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Author information

Authors and Affiliations

  1. Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK

    Ye Mao & Chao Li

  2. School of Science and Engineering, University of Dundee, Dundee, UK

    Lan Jiang & Chao Li

  3. Department of Computer Science, University of Bath, Bath, UK

    Xi Chen

  4. School of Medicine, University of Dundee, Dundee, UK

    Chao Li

Authors
  1. Ye Mao
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  2. Lan Jiang
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  3. Xi Chen
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  4. Chao Li
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Corresponding author

Correspondence to Chao Li .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen's University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Mao, Y., Jiang, L., Chen, X., Li, C. (2023). DisC-Diff: Disentangled Conditional Diffusion Model for Multi-contrast MRI Super-Resolution. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_37

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  • DOI: https://doi.org/10.1007/978-3-031-43999-5_37

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