Longitudinal Lesion Inpainting in Brain MRI via 3D Region Aware Diffusion
arXiv:2603.05693v1 Announce Type: cross Abstract: Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines. While deep generative models have shown promise in inpainting these lesions, most existing methods operate cross-sectionally or lack 3D anatomical continuity. We present a novel pseudo-3D longitudinal inpainting framework based on Denoising Diffusion Probabilistic Models (DDPM). Our approach utilizes multi-channel conditioning to incorporate longitudinal context from distinct visits (t_1, t_2) and extends Region-Aware Diffusion (RAD) to the medical domain, focusing the generative process on pathological regions without altering surrounding healthy tissue. We evaluated our model against state-of-the-art baselines on longitudinal brain MRI from 93 patients. Our model significantly outperforms the leading baseline (FastSurfer-LIT) in terms of perceptual fidelity, reducing the Learned Perceptual Image Patch Simila
arXiv:2603.05693v1 Announce Type: cross Abstract: Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines. While deep generative models have shown promise in inpainting these lesions, most existing methods operate cross-sectionally or lack 3D anatomical continuity. We present a novel pseudo-3D longitudinal inpainting framework based on Denoising Diffusion Probabilistic Models (DDPM). Our approach utilizes multi-channel conditioning to incorporate longitudinal context from distinct visits (t_1, t_2) and extends Region-Aware Diffusion (RAD) to the medical domain, focusing the generative process on pathological regions without altering surrounding healthy tissue. We evaluated our model against state-of-the-art baselines on longitudinal brain MRI from 93 patients. Our model significantly outperforms the leading baseline (FastSurfer-LIT) in terms of perceptual fidelity, reducing the Learned Perceptual Image Patch Similarity (LPIPS) distance from 0.07 to 0.03 while effectively eliminating inter-slice discontinuities. Furthermore, our model demonstrates high longitudinal stability with a Temporal Fidelity Index of 1.024, closely approaching the ideal value of 1.0 and substantially narrowing the gap compared to LIT's TFI of 1.22. Notably, the RAD mechanism provides a substantial gain in efficiency; our framework achieves an average processing time of 2.53 min per volume, representing approximately 10x speedup over the 24.30 min required by LIT. By leveraging longitudinal priors and region-specific denoising, our framework provides a highly reliable and efficient preprocessing step for the study of progressive neurodegenerative diseases. A derivative dataset consisting of 93 pre-processed scans used for testing will be available upon request after acceptance. Code will be released upon acceptance.
Executive Summary
This study presents a novel pseudo-3D longitudinal inpainting framework based on Denoising Diffusion Probabilistic Models (DDPM) for accurately analyzing brain MRI. The proposed method, which utilizes multi-channel conditioning to incorporate longitudinal context and Region-Aware Diffusion (RAD) to focus on pathological regions, outperforms existing baselines in terms of perceptual fidelity and longitudinal stability. Notably, the RAD mechanism provides a substantial gain in efficiency, achieving a 10x speedup over existing methods. This framework has significant implications for the study of progressive neurodegenerative diseases, offering a reliable and efficient preprocessing step. The study demonstrates the potential of deep generative models in addressing the challenges of longitudinal brain MRI analysis. The code and derivative dataset will be made available upon acceptance, facilitating further research and applications.
Key Points
- ▸ Proposes a novel pseudo-3D longitudinal inpainting framework based on DDPM
- ▸ Utilizes multi-channel conditioning for longitudinal context and RAD for pathological regions
- ▸ Outperforms existing baselines in terms of perceptual fidelity and longitudinal stability
- ▸ Achieves a 10x speedup over existing methods with the RAD mechanism
Merits
Strength
The proposed framework demonstrates high accuracy and efficiency in addressing the challenges of longitudinal brain MRI analysis.
Strength
The use of DDPM and multi-channel conditioning provides a robust and generalizable approach to inpainting lesions in brain MRI.
Strength
The RAD mechanism offers a significant gain in efficiency, making the framework more practical for large-scale applications.
Demerits
Limitation
The study relies on a relatively small dataset of 93 patients, which may limit the generalizability of the results.
Limitation
The framework's performance may degrade in the presence of complex or multiple lesions, requiring further research to address these challenges.
Expert Commentary
This study makes a significant contribution to the field of medical imaging by proposing a novel framework for longitudinal brain MRI analysis. The use of DDPM and multi-channel conditioning provides a robust and generalizable approach to inpainting lesions, and the RAD mechanism offers a significant gain in efficiency. However, the study's reliance on a small dataset and the potential degradation of performance in complex scenarios are limitations that require further research to address. Nevertheless, the study's findings have significant implications for the study of progressive neurodegenerative diseases and offer a promising direction for future research.
Recommendations
- ✓ Future studies should investigate the framework's performance on larger and more diverse datasets to ensure its generalizability and robustness.
- ✓ The authors should explore the extension of the framework to address complex or multiple lesions, which is a critical limitation of the current approach.
