End-to-end Ultrasound Frame to Volume Registration

• URL: http://arxiv.org/abs/2107.06449v1
• Date: Wed, 14 Jul 2021 01:59:42 GMT
• Title: End-to-end Ultrasound Frame to Volume Registration
• Authors: Hengtao Guo, Xuanang Xu, Sheng Xu, Bradford J. Wood, Pingkun Yan
• Abstract summary: We propose an end-to-end frame-to-volume registration network (FVR-Net) for 2D and 3D registration. Our model shows superior efficiency for real-time interventional guidance with highly competitive registration accuracy.
• Score: 9.738024231762465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fusing intra-operative 2D transrectal ultrasound (TRUS) image with pre-operative 3D magnetic resonance (MR) volume to guide prostate biopsy can significantly increase the yield. However, such a multimodal 2D/3D registration problem is a very challenging task. In this paper, we propose an end-to-end frame-to-volume registration network (FVR-Net), which can efficiently bridge the previous research gaps by aligning a 2D TRUS frame with a 3D TRUS volume without requiring hardware tracking. The proposed FVR-Net utilizes a dual-branch feature extraction module to extract the information from TRUS frame and volume to estimate transformation parameters. We also introduce a differentiable 2D slice sampling module which allows gradients backpropagating from an unsupervised image similarity loss for content correspondence learning. Our model shows superior efficiency for real-time interventional guidance with highly competitive registration accuracy.

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