A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration

TL;DR

This paper introduces a novel 3D cross-modal keypoint descriptor for MRI-ultrasound matching and registration, leveraging synthetic data, contrastive learning, and a probabilistic detection strategy to improve robustness and accuracy.

Contribution

It proposes a new descriptor learning framework that is robust to artifacts and modality differences, with a patient-specific synthesis approach and a probabilistic keypoint detection method.

Findings

Outperforms state-of-the-art methods with 69.8% average precision in keypoint matching.

Achieves a mean Target Registration Error of 2.39 mm on the ReMIND2Reg benchmark.

Demonstrates robustness to ultrasound artifacts and field-of-view variations.

Abstract

Intraoperative registration of real-time ultrasound (iUS) to preoperative Magnetic Resonance Imaging (MRI) remains an unsolved problem due to severe modality-specific differences in appearance, resolution, and field-of-view. To address this, we propose a novel 3D cross-modal keypoint descriptor for MRI-iUS matching and registration. Our approach employs a patient-specific matching-by-synthesis approach, generating synthetic iUS volumes from preoperative MRI. This enables supervised contrastive training to learn a shared descriptor space. A probabilistic keypoint detection strategy is then employed to identify anatomically salient and modality-consistent locations. During training, a curriculum-based triplet loss with dynamic hard negative mining is used to learn descriptors that are i) robust to iUS artifacts such as speckle noise and limited coverage, and ii) rotation-invariant. At inference, the method detects keypoints in MR and real iUS images and identifies sparse matches, which are then used to perform rigid registration. Our approach is evaluated using 3D MRI-iUS pairs from the ReMIND dataset. Experiments show that our approach outperforms state-of-the-art keypoint matching methods across 11 patients, with an average precision of 69.8%. For image registration, our method achieves a competitive mean Target Registration Error of 2.39 mm on the ReMIND2Reg benchmark. Compared to existing iUS-MR registration approaches, our framework is interpretable, requires no manual initialization, and shows robustness to iUS field-of-view variation. Code, data and model weights are available at https://github.com/morozovdd/CrossKEY.