用时序扩散模型提升肝癌介入手术中2D与3D血管配准精度。
TempDiffReg: Temporal Diffusion Model for Non-Rigid 2D-3D Vascular Registration
- 分步配准：先全局对齐，再用时序扩散模型迭代优化变形。
- 误差低至0.63毫米（MSE），比现有方法降低66.7%。
- 适合医疗影像导航，尤其帮助经验较少的医生操作。
经导管动脉化疗栓塞术（TACE）是治疗肝细胞癌等肝脏恶性肿瘤的首选方式，但因术中血管路径复杂、解剖结构变异大，操作极具挑战。精准可靠的2D-3D血管配准对引导微导管和器械至关重要，可实现血管结构精确定位与治疗靶点优化。为此，本文提出一种从粗到细的配准策略：首先引入结构感知透视五点法（SA-PnP）建立2D与3D血管结构对应关系；随后提出TempDiffReg，一种利用时序上下文捕捉复杂解剖变化与局部结构演变的时序扩散模型，实现血管形变的迭代优化。基于23名患者的数据，构建了626组多帧配对样本进行评估。实验表明，该方法在准确性和解剖合理性上均优于当前最优方法，注册误差达到均方误差（MSE）0.63 mm，平均绝对误差（MAE）0.51 mm，较最先进方法分别降低66.7%和17.7%。该技术有望辅助经验不足的临床医师更安全高效地完成复杂TACE手术，提升手术效果与患者护理质量。代码与数据见：https://github.com/LZH970328/TempDiffReg.git
原文摘要 · Abstract (English)
Transarterial chemoembolization (TACE) is a preferred treatment option for hepatocellular carcinoma and other liver malignancies, yet it remains a highly challenging procedure due to complex intra-operative vascular navigation and anatomical variability. Accurate and robust 2D-3D vessel registration is essential to guide microcatheter and instruments during TACE, enabling precise localization of vascular structures and optimal therapeutic targeting. To tackle this issue, we develop a coarse-to-fine registration strategy. First, we introduce a global alignment module, structure-aware perspective n-point (SA-PnP), to establish correspondence between 2D and 3D vessel structures. Second, we propose TempDiffReg, a temporal diffusion model that performs vessel deformation iteratively by leveraging temporal context to capture complex anatomical variations and local structural changes. We collected data from 23 patients and constructed 626 paired multi-frame samples for comprehensive evaluation. Experimental results demonstrate that the proposed method consistently outperforms state-of-the-art (SOTA) methods in both accuracy and anatomical plausibility. Specifically, our method achieves a mean squared error (MSE) of 0.63 mm and a mean absolute error (MAE) of 0.51 mm in registration accuracy, representing 66.7\% lower MSE and 17.7\% lower MAE compared to the most competitive existing approaches. It has the potential to assist less-experienced clinicians in safely and efficiently performing complex TACE procedures, ultimately enhancing both surgical outcomes and patient care. Code and data are available at: \textcolor{blue}{https://github.com/LZH970328/TempDiffReg.git}
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