3-D/2-D Registration of Cardiac Structures by 3-D Contrast Agent Distribution Estimation

TL;DR

This paper introduces two novel similarity measures for robust 3D/2D registration of cardiac structures using contrast agent distribution, effective even with minimal contrast, validated on clinical datasets.

Contribution

It proposes two new similarity measures, including a contrast agent distribution estimate, for improved registration with limited contrast agent presence.

Findings

Achieved an average registration error of 7.9 mm with well-contrasted data.

Performed robust registration with minimal contrast agent, error of 8.8 mm.

Outperformed shadow-based registration significantly (p<0.05).

Abstract

For augmented fluoroscopy during cardiac catheter ablation procedures, a preoperatively acquired 3-D model of the left atrium of the patient can be registered to X-ray images. Therefore the 3D-model is matched with the contrast agent based appearance of the left atrium. Commonly, only small amounts of contrast agent (CA) are used to locate the left atrium. This is why we focus on robust registration methods that work also if the structure of interest is only partially contrasted. In particular, we propose two similarity measures for CA-based registration: The first similarity measure, explicit apparent edges, focuses on edges of the patient anatomy made visible by contrast agent and can be computed quickly on the GPU. The second novel similarity measure computes a contrast agent distribution estimate (CADE) inside the 3-D model and rates its consistency with the CA seen in biplane fluoroscopic images. As the CADE computation involves a reconstruction of CA in 3-D using the CA within the fluoroscopic images, it is slower. Using a combination of both methods, our evaluation on 11 well-contrasted clinical datasets yielded an error of 7.9+/-6.3 mm over all frames. For 10 datasets with little CA, we obtained an error of 8.8+/-6.7 mm. Our new methods outperform a registration based on the projected shadow significantly (p<0.05).