PathoSyn: Imaging-Pathology MRI Synthesis via Disentangled Deviation Diffusion

TL;DR

PathoSyn is a novel MRI synthesis framework that disentangles anatomy and pathology, enabling high-fidelity, patient-specific image generation and disease progression modeling for improved diagnostic tool development.

Contribution

It introduces a diffusion-based model that separates anatomical structure from pathological deviations, enhancing realism and interpretability in MRI synthesis.

Findings

Outperforms baseline models in realism and anatomical accuracy

Generates high-fidelity synthetic datasets for low-data regimes

Supports interpretable disease progression modeling

Abstract

We present PathoSyn, a unified generative framework for Magnetic Resonance Imaging (MRI) image synthesis that reformulates imaging-pathology as a disentangled additive deviation on a stable anatomical manifold. Current generative models typically operate in the global pixel domain or rely on binary masks, these paradigms often suffer from feature entanglement, leading to corrupted anatomical substrates or structural discontinuities. PathoSyn addresses these limitations by decomposing the synthesis task into deterministic anatomical reconstruction and stochastic deviation modeling. Central to our framework is a Deviation-Space Diffusion Model designed to learn the conditional distribution of pathological residuals, thereby capturing localized intensity variations while preserving global structural integrity by construction. To ensure spatial coherence, the diffusion process is coupled with a seam-aware fusion strategy and an inference-time stabilization module, which collectively suppress boundary artifacts and produce high-fidelity internal lesion heterogeneity. PathoSyn provides a mathematically principled pipeline for generating high-fidelity patient-specific synthetic datasets, facilitating the development of robust diagnostic algorithms in low-data regimes. By allowing interpretable counterfactual disease progression modeling, the framework supports precision intervention planning and provides a controlled environment for benchmarking clinical decision-support systems. Quantitative and qualitative evaluations on tumor imaging benchmarks demonstrate that PathoSyn significantly outperforms holistic diffusion and mask-conditioned baselines in both perceptual realism and anatomical fidelity. The source code of this work will be made publicly available.