Myocardial Scar Enhancement in LGE Cardiac MRI using Localized Diffusion

Late gadolinium enhancement (LGE) imaging is considered the gold-standard technique for evaluating myocardial scar/fibrosis. In LGE, an in-version pulse is played before imaging to create a contrast between healthy and scarred regions. However, several factors can impact the contrast quali-ty, impacting diagnostic interpretation. Furthermore, the quantification of scar burden is highly dependent on image quality. Deep learning-based au-tomated segmentation algorithms often fail when there is no clear boundary between healthy and scarred tissue. This study sought to develop a genera-tive model for improving the contrast of healthy-scarred myocardium in LGE. We propose a localized conditional diffusion model, in which only a region-of-interest (ROI), in this case heart, is subjected to the noising pro-cess, adapting the learning process to the local nature of our proposed en-hancement. The scar-enhanced images, used as training targets, are generated via tissue-specific gamma correction. A segmentation model is trained and used to extract the heart regions. The inference speed is improved by lever-aging partial diffusion, applying noise only up to an intermediate step. Fur-thermore, utilizing the stochastic nature of diffusion models, repeated infer-ence leads to improved scar enhancement of ambiguous regions. The pro-posed algorithm was evaluated using LGE images collected in 929 patients with hypertrophic cardiomyopathy, in a multi-center, multi-vendor study. Our results show visual improvements of scar-healthy myocardium con-trast. To further demonstrate the strength of our method, we evaluate our performance against various image enhancement models where the proposed approach shows higher contrast enhancement. The code is available at: https://github.com/HMS-CardiacMR/Scar_enhancement.