Dr. Ruisi Zhang Profile

Dr. Ruisi Zhang

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Proceedings Article | 2 April 2024 Presentation + Paper

Transformer-based local feature matching for multimodal image registration

Remi Delaunay, Ruisi Zhang, Filipe Pedrosa, Navid Feizi, Dianne Sacco, Rajni Patel, Jayender Jagadeesan

Proceedings Volume 12926, 129260I (2024) https://doi.org/10.1117/12.3005591

KEYWORDS: Ultrasonography, Pose estimation, Computed tomography, Image registration, Feature extraction, Matrices, Network architectures, Kidney

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Ultrasound imaging is a cost-effective and radiation-free modality for visualizing anatomical structures in real-time, making it ideal for guiding surgical interventions. However, its limited field-of-view, speckle noise, and imaging artifacts make it difficult to interpret the images for inexperienced users. In this paper, we propose a new 2D ultrasound to 3D CT registration method to improve surgical guidance during ultrasound-guided interventions. Our approach adopts a dense feature matching method called LoFTR to our multimodal registration problem. We learn to predict dense coarse-to-fine correspondences using a Transformer-based architecture to estimate a robust rigid transformation between a 2D ultrasound frame and a CT scan. Additionally, a fully differentiable pose estimation method is introduced, optimizing LoFTR on pose estimation error during training. Experiments conducted on a multimodal dataset of ex vivo porcine kidneys demonstrate the method’s promising results for intraoperative, trackerless ultrasound pose estimation. By mapping 2D ultrasound frames into the 3D CT volume space, the method provides intraoperative guidance, potentially improving surgical workflows and image interpretation.

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