PathoSyn: 通过解耦偏差扩散实现影像-病理MRI合成
PathoSyn: Imaging-Pathology MRI Synthesis via Disentangled Deviation Diffusion
AI总结：
PathoSyn通过解耦偏差扩散模型生成高保真MRI合成数据，提升低数据环境下诊断算法的鲁棒性。
摘要：
我们提出了PathoSyn，一种统一的生成框架，用于磁共振成像（MRI）图像合成，将影像-病理学重新表述为稳定解剖流形上的解耦加法偏差。当前的生成模型通常在全局像素域操作或依赖二进制掩码，这些范式往往导致特征纠缠，从而产生受损的解剖基质或结构不连续性。PathoSyn通过将合成任务分解为确定性的解剖重建和随机偏差建模来解决这些限制。框架的核心是一个偏差空间扩散模型，旨在学习病理残差的条件分布，从而捕捉局部强度变化，同时通过构造保持全局结构完整性。为了确保空间一致性，扩散过程与一种缝线意识融合策略和推理时稳定模块相结合，共同抑制边界伪影，产生高质量的内部病变异质性。PathoSyn提供了一个数学上严谨的管道，用于生成高保真的患者特定合成数据集，促进在低数据环境下鲁棒诊断算法的发展。通过允许可解释的反事实疾病进展建模，该框架支持精准干预规划，并提供一个受控环境用于基准测试临床决策支持系统。在肿瘤成像基准上的定量和定性评估表明，PathoSyn在感知真实性和解剖保真度方面均显著优于整体扩散和掩码条件基线。该工作的源代码将公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
