# Bi-Directional MS Lesion Filling and Synthesis Using Denoising Diffusion Implicit Model-based Lesion Repainting

arXiv: https://arxiv.org/abs/2410.05027 · HTML: https://mlsanity.com/paper/2410.05027/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 7 of 7)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles](https://mlsanity.com/paper/1903.12152/) (2019)
- [Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation](https://mlsanity.com/paper/2310.20586/) (2023)
- [Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion](https://mlsanity.com/paper/2312.01460/) (2023)

## Built on by (most cited first, 6 of 6)

- [Encoder-Only Image Registration](https://mlsanity.com/paper/2509.00451/) (2025)
- [UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2508.03982/) (2025)
- [Diffusion-Driven Generation of Minimally Preprocessed Brain MRI](https://mlsanity.com/paper/2510.26834/) (2025)
- [MSRepaint: Multiple Sclerosis Repaint with Conditional Denoising Diffusion Implicit Model for Bidirectional Lesion Filling and Synthesis](https://mlsanity.com/paper/2510.02063/) (2025)
- [Longitudinal Lesion Inpainting in Brain MRI via 3D Region Aware Diffusion](https://mlsanity.com/paper/2603.05693/) (2026)
- [Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions](https://mlsanity.com/paper/2607.21904/) (2026)

## Abstract (arXiv)

Automatic magnetic resonance (MR) image processing pipelines are widely used to study people with multiple sclerosis (PwMS), encompassing tasks such as lesion segmentation and brain parcellation. However, the presence of lesion often complicates these analysis, particularly in brain parcellation. Lesion filling is commonly used to mitigate this issue, but existing lesion filling algorithms often fall short in accurately reconstructing realistic lesion-free images, which are vital for consistent downstream analysis. Additionally, the performance of lesion segmentation algorithms is often limited by insufficient data with lesion delineation as training labels. In this paper, we propose a novel approach leveraging Denoising Diffusion Implicit Models (DDIMs) for both MS lesion filling and synthesis based on image inpainting. Our modified DDIM architecture, once trained, enables both MS lesion filing and synthesis. Specifically, it can generate lesion-free T1-weighted or FLAIR images from those containing lesions; Or it can add lesions to T1-weighted or FLAIR images of healthy subjects. The former is essential for downstream analyses that require lesion-free images, while the latter is valuable for augmenting training datasets for lesion segmentation tasks. We validate our approach through initial experiments in this paper and demonstrate promising results in both lesion filling and synthesis, paving the way for future work.