An Approach for Live Motion Correction for TRUS-MR Prostate Fusion Biopsy using Deep Learning.

TRUS-MR fusion guided biopsy highly depends on the quality of alignment between pre-operative Magnetic Resonance (MR) image and live trans-rectal ultrasound (TRUS) image during biopsy. Lot of factors influence the alignment of prostate during the biopsy like rigid motion due to patient movement and deformation of the prostate due to probe pressure. For MR-TRUS alignment during live procedure, the efficiency of the algorithm and accuracy plays an important role. In this paper, we have designed a comprehensive framework for fusion based biopsy using an end-to-end deep learning network for performing both rigid and deformation correction. Both rigid and deformation correction in one single network helps in reducing the computation time required for live TRUS-MR alignment. We have used 6500 images from 34 subjects for conducting this study. Our proposed registration pipeline provides Target Registration Error (TRE) of 2.51 mm after rigid and deformation correction on unseen patient dataset. In addition, with a total computation time of 70ms, we are able to achieve a rendering rate of 14 frames per second (FPS) that makes our network well suited for live procedures.Clinical Relevance- It is shown in the literature that systematic biopsy is the standard method for biopsy sampling in prostate that has high false negative rates. TRUS-MR fusion guided biopsy reduces the false negative rate of the sampling in prostate biopsy. Therefore, a live TRUS-MR fusion framework is helpful for prostate biopsy clinical procedures.

Comparison of Accuracies between Real-Time Nonrigid and Rigid Registration in the MRI-US Fusion Biopsy of the Prostate.

Magnetic resonance imaging (MRI) is increasingly important in the detection and localization of prostate cancer. Regarding suspicious lesions on MRI, a targeted biopsy using MRI fused with ultrasound (US) is widely used. To achieve a successful targeted biopsy, a precise registration between MRI and US is essential. The purpose of our study was to show any decrease in errors using a real-time nonrigid registration technique for prostate biopsy. Nineteen patients with suspected prostate cancer were prospectively enrolled in this study. Registration accuracy was calculated by the measuring distance of corresponding points by rigid and nonrigid registration between MRI and US, and compared for rigid and nonrigid registration methods. Overall cancer detection rates were also evaluated by patient and by core. Prostate volume was measured automatically from MRI and manually from US, and compared to each other. Mean distances between the corresponding points in MRI and US were 5.32 ± 2.61 mm for rigid registration and 2.11 ± 1.37 mm for nonrigid registration (p < 0.05). Cancer was diagnosed in 11 of 19 patients (57.9%), and in 67 of 266 biopsy cores (25.2%). There was no significant difference in prostate-volume measurement between the automatic and manual methods (p = 0.89). In conclusion, nonrigid registration reduces targeting errors.