Classifier-guided registration of coronary CT angiography and intravascular ultrasound

TL;DR

This paper introduces a fully automatic deep learning-based framework for registering coronary CT angiography and intravascular ultrasound images, improving accuracy and practicality for coronary artery disease assessment.

Contribution

It presents a novel method that automatically detects anatomical features and guides registration without manual input or segmentation, handling artifacts effectively.

Findings

Achieved 83.3% successful registration rate on clinical data.

Median centerline overlap F1-score of 0.982 indicating high accuracy.

Median cosine similarities around 0.94 demonstrating precise orientation alignment.

Abstract

Coronary CT angiography (CCTA) and intravascular ultrasound (IVUS) provide complementary information for coronary artery disease assessment, making their registration valuable for comprehensive analysis. However, existing registration methods require manual interaction or extensive segmentations, limiting their practical application. In this work, we present a fully automatic framework for CCTA-IVUS registration using deep learning-based feature detection and a differentiable image registration module. Our approach leverages a convolutional neural network trained to identify key anatomical features from polar-transformed multiplanar reformatted CCTA or IVUS data. These detected anatomical featuers subsequently guide a differentiable registration module to optimize transformation parameters of an automatically extracted coronary artery centerline. The method does not require landmark selection or segmentations as input, while accounting for the presence of IVUS guidewire artifacts. Evaluated on 48 clinical cases with reference CCTA centerlines corresponding to IVUS pullback, our method achieved successful registration in 83.3\% of cases, with a median centerline overlap F$_1$-score of 0.982 and median cosine similarities of 0.940 and 0.944 for cross-sectional plane orientation. Our results demonstrate that automatically detected anatomical features can be leveraged for accurate registration. The fully automatic nature of the approach represents a significant step toward streamlined multimodal coronary analysis, potentially facilitating large-scale studies of coronary plaque characteristics across modalities.