用扩散模型生成更逼真的增强CT，避免结构错位和细节丢失
PHASOR: Anatomy- and Phase-Consistent Volumetric Diffusion for CT Virtual Contrast Enhancement
- 基于视频扩散模型处理三维CT序列，提升结构连贯性
- 在三个数据集上显著优于现有方法，增强效果更准确
- 适合医学影像医生和算法研发者参考
增强型计算机断层扫描（CECT）对显示组织灌注和血管分布至关重要，但其临床广泛应用受限于对比剂的侵入性及辐射风险。虚拟对比增强（VCE）可从非增强CT（NCCT）合成CECT作为替代方案，但现有方法在解剖异质性和空间错位方面表现不佳，导致增强模式不一致、细节错误。本文提出PHASOR，一种用于高保真CT VCE的体素扩散框架。通过将CT体积视为连续序列，利用视频扩散模型提升结构一致性和体素精度。为实现解剖-相位一致性合成，引入两个互补模块：首先，解剖路由专家混合（AR-MoE）将不同增强模式锚定至解剖语义，并通过器官特异性记忆捕捉关键细节；其次，强度-相位感知表征对齐（IP-REPA）突出精细对比信号，同时减轻空间错位影响。在三个数据集上的大量实验表明，PHASOR在合成质量与增强准确性方面均显著超越当前最优方法。
原文摘要 · Abstract (English)
Contrast-enhanced computed tomography (CECT) is pivotal for highlighting tissue perfusion and vascularity, yet its clinical ubiquity is impeded by the invasive nature of contrast agents and radiation risks. While virtual contrast enhancement (VCE) offers an alternative to synthesizing CECT from non-contrast CT (NCCT), existing methods struggle with anatomical heterogeneity and spatial misalignment, leading to inconsistent enhancement patterns and incorrect details. This paper introduces PHASOR, a volumetric diffusion framework for high-fidelity CT VCE. By treating CT volumes as coherent sequences, we leverage a video diffusion model to enhance structural coherence and volumetric accuracy. To ensure anatomy-phase consistent synthesis, we introduce two complementary modules. First, anatomy-routed mixture-of-experts (AR-MoE) anchors distinct enhancement patterns to anatomical semantics, with organ-specific memory to capture salient details. Second, intensity-phase aware representation alignment (IP-REPA) highlights intricate contrast signals while mitigating the impact of imperfect spatial alignment. Extensive experiments across three datasets demonstrate that PHASOR significantly outperforms state-of-the-art methods in both synthesis quality and enhancement accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。