Heterogeneity-Adaptive Diffusion Schrodinger Bridge for PET-Guided Whole-Body MRI Translation

2026-07-08

AI Summary

This work addresses the challenge of feature heterogeneity in whole-body imaging caused by anatomical diversity and pathological tissues by proposing a heterogeneity-adaptive diffusion Schrödinger bridge framework for high-quality PET-guided MRI synthesis. The method uniquely integrates region-specific contextual embeddings derived from a vision-language model and PET metabolic priors into the diffusion Schrödinger bridge, employing a two-stage guidance mechanism: an adaptive noise modulation module perturbs features during the forward process, while an attention-based refinement enhances lesion structures in the reverse process. Experimental results demonstrate that the proposed approach significantly improves MRI generation quality in lesion regions across diverse anatomical areas in whole-body image translation tasks, outperforming current state-of-the-art methods.

Abstract

While whole-body multimodal medical imaging scanners have been increasingly recognized for more effective medical applications, the excessive long acquisition time in PET-MR scanning is a major obstacle in more efficient clinical practice. Deep learning-based MRI translation provides a potential solution to reduce scan duration. However, current models often focus on specific anatomical regions and face challenges for whole-body scans that consists of highly heterogeneous feature distributions mainly due to (1) different anatomical regions across whole-body, and (2) lesions or pathological tissues. This paper tackles the challenges through a novel Heterogeneity-Adaptive Diffusion Schrodinger Bridge (HA-DSB) framework. By explicitly modeling translation as stochastic transport between source and target distributions, HA-DSB incorporates region context embeddings derived from a vision-language model (VLM) to enable region-specific modeling. To enhance fidelity of the pathological tissue, lesion-aware metabolic prior from PET is integrated directly into the bridge dynamics through a dual-stage guidance mechanism. Specifically, a PET-guided noise modulation module adaptively scales spatial diffusion perturbations during the forward process, while PET features are leveraged during the reverse process to selectively amplify lesion-relevant structures via an attention mechanism. Experiments demonstrate the superiority of our method across different body regions in whole-body MRI translation and show improved translation quality in lesion areas under PET guidance. Our code is available at Github.

Problem

• whole-body MRI translation
• heterogeneity
• PET-guided imaging
• pathological tissue
• multimodal medical imaging

Innovation

• Heterogeneity-Adaptive Diffusion
• Schrodinger Bridge
• PET-guided MRI Translation
• Vision-Language Model
• Lesion-Aware Prior