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

2025-12-15

IEEE International Conference on Bioinformatics and Biomedicine

AI Summary

This study addresses the challenge of 2D–3D vascular registration in transarterial chemoembolization (TACE), where anatomical variations and complex navigation hinder accurate alignment. To overcome this, the authors propose a coarse-to-fine non-rigid registration framework: global alignment is first achieved via a structure-aware perspective-n-point (SA-PnP) method, followed by iterative refinement of local non-rigid deformations using TempDiffReg, a novel temporal diffusion model that leverages multi-frame vascular sequences. This work represents the first application of temporal diffusion models to 2D–3D vascular registration, effectively balancing anatomical plausibility with high registration accuracy. Evaluated on 626 multi-frame samples from 23 patients, the method achieves a mean squared error (MSE) of 0.63 mm and a mean absolute error (MAE) of 0.51 mm, outperforming the current state-of-the-art by 66.7% and 17.7%, respectively.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Planning, Routing, and Scheduling: Temporal Planning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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: https://github.com/LZH970328/TempDiffReg.git

Problem

• 2D-3D vascular registration
• non-rigid registration
• temporal diffusion model
• anatomical variability
• TACE

Innovation

• Temporal Diffusion Model
• Non-Rigid Registration
• 2D-3D Vascular Registration
• Structure-Aware PnP
• TACE Guidance