Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images, e.g., for the evaluation of volumetric changes. However, many of the evaluation tools are optimized for analyzing healthy tissue. To enable the evaluation of scans containing pathological tissue, it is therefore required to restore healthy tissue in the pathological areas. In this work, we explore and extend denoising diffusion models for consistent inpainting of healthy 3D brain tissue. We modify state-of-the-art 2D, pseudo-3D, and 3D methods working in the image space, as well as 3D latent and 3D wavelet diffusion models, and train them to synthesize healthy brain tissue. Our evaluation shows that the pseudo-3D model performs best regarding the structural-similarity index, peak signal-to-noise ratio, and mean squared error. To emphasize the clinical relevance, we fine-tune this model on data containing synthetic MS lesions and evaluate it on a downstream brain tissue segmentation task, whereby it outperforms the established FMRIB Software Library (FSL) lesion-filling method.
@article{arxiv.2403.14499,
title = {Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting},
author = {Alicia Durrer and Julia Wolleb and Florentin Bieder and Paul Friedrich and Lester Melie-Garcia and Mario Ocampo-Pineda and Cosmin I. Bercea and Ibrahim E. Hamamci and Benedikt Wiestler and Marie Piraud and Özgür Yaldizli and Cristina Granziera and Bjoern H. Menze and Philippe C. Cattin and Florian Kofler},
journal= {arXiv preprint arXiv:2403.14499},
year = {2024}
}