Transformer-Based Local Feature Matching for Multimodal Image Registration
arxiv(2024)
摘要
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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