TempDiffReg: Temporal Diffusion Model for Non-Rigid 2D-3D Vascular Registration

TL;DR

This paper introduces TempDiffReg, a novel temporal diffusion model for accurate 2D-3D vascular registration in liver cancer treatment, improving surgical guidance by capturing complex anatomical variations.

Contribution

The paper proposes TempDiffReg, a new temporal diffusion approach for vessel deformation modeling, combined with a global alignment module for improved 2D-3D registration accuracy.

Findings

Achieves 66.7% lower MSE than state-of-the-art methods.

Demonstrates consistent outperformance in registration accuracy.

Potential to assist clinicians in complex TACE procedures.

Abstract

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}