End-to-end Ultrasound Frame to Volume Registration

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 very challenging due to several significant obstacles such as dimensional mismatch, large modal appearance difference, and heavy computational load. 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. To achieve efficient training and inference, we introduce a differentiable 2D slice sampling module which allows gradients backpropagating from an unsupervised image similarity loss for content correspondence learning. Our experiments demonstrate the proposed method’s superior efficiency for real-time interventional guidance with highly competitive registration accuracy. Source code of this work is publicly available at https://github.com/DIAL-RPI/FVR-Net.