2D Ultrasound and 3D MR Image Registration of the Prostate for Brachytherapy Surgical Navigation

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.

Focal therapy for prostate cancer: the technical challenges

Focal therapy for prostate cancer has been proposed as an alternative treatment to whole gland therapy, offering the opportunity for tumor dose escalation and/or reduced toxicity. Brachytherapy, either low-dose-rate or high-dose-rate, provides an ideal approach, offering both precision in dose delivery and opportunity for a highly conformal, non-uniform dose distribution. Whilst multiple consensus documents have published clinical guidelines for patient selection, there are insufficient data to provide clear guidelines on target volume delineation, treatment planning margins, treatment planning approaches, and many other technical issues that should be considered before implementing a focal brachytherapy program. Without consensus guidelines, there is the potential for a diversity of practices to develop, leading to challenges in interpreting outcome data from multiple centers. This article provides an overview of the technical considerations for the implementation of a clinical service, and discusses related topics that should be considered in the design of clinical trials to ensure precise and accurate methods are applied for focal brachytherapy treatments.

Real-time registration of 3D to 2D ultrasound images for image-guided prostate biopsy

PURPOSE

During image-guided prostate biopsy, needles are targeted at tissues that are suspicious of cancer to obtain specimen for histological examination. Unfortunately, patient motion causes targeting errors when using an MR-transrectal ultrasound (TRUS) fusion approach to augment the conventional biopsy procedure. This study aims to develop an automatic motion correction algorithm approaching the frame rate of an ultrasound system to be used in fusion-based prostate biopsy systems. Two modes of operation have been investigated for the clinical implementation of the algorithm: motion compensation using a single user initiated correction performed prior to biopsy, and real-time continuous motion compensation performed automatically as a background process.

METHODS

Retrospective 2D and 3D TRUS patient images acquired prior to biopsy gun firing were registered using an intensity-based algorithm utilizing normalized cross-correlation and Powell's method for optimization. 2D and 3D images were downsampled and cropped to estimate the optimal amount of image information that would perform registrations quickly and accurately. The optimal search order during optimization was also analyzed to avoid local optima in the search space. Error in the algorithm was computed using target registration errors (TREs) from manually identified homologous fiducials in a clinical patient dataset. The algorithm was evaluated for real-time performance using the two different modes of clinical implementations by way of user initiated and continuous motion compensation methods on a tissue mimicking prostate phantom.

RESULTS

After implementation in a TRUS-guided system with an image downsampling factor of 4, the proposed approach resulted in a mean ± std TRE and computation time of 1.6 ± 0.6 mm and 57 ± 20 ms respectively. The user initiated mode performed registrations with in-plane, out-of-plane, and roll motions computation times of 108 ± 38 ms, 60 ± 23 ms, and 89 ± 27 ms, respectively, and corresponding registration errors of 0.4 ± 0.3 mm, 0.2 ± 0.4 mm, and 0.8 ± 0.5°. The continuous method performed registration significantly faster (P < 0.05) than the user initiated method, with observed computation times of 35 ± 8 ms, 43 ± 16 ms, and 27 ± 5 ms for in-plane, out-of-plane, and roll motions, respectively, and corresponding registration errors of 0.2 ± 0.3 mm, 0.7 ± 0.4 mm, and 0.8 ± 1.0°.

CONCLUSIONS

The presented method encourages real-time implementation of motion compensation algorithms in prostate biopsy with clinically acceptable registration errors. Continuous motion compensation demonstrated registration accuracy with submillimeter and subdegree error, while performing < 50 ms computation times. Image registration technique approaching the frame rate of an ultrasound system offers a key advantage to be smoothly integrated to the clinical workflow. In addition, this technique could be used further for a variety of image-guided interventional procedures to treat and diagnose patients by improving targeting accuracy.