Graphics Processing Unit–Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations

Abstract

Rationale and Objectives: Accuracy and speed are essential for the intraprocedural nonrigid MR-to-CT image registration in the assessment of tumor margins during CT-guided liver tumor ablations. While both accuracy and speed can be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially. To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique based on volume subdivision with hardware acceleration using a graphical processing unit (GPU). We compared the registration accuracy and processing time of GPU-accelerated volume subdivision-based nonrigid registration technique to the conventional nonrigid B-spline registration technique. Materials and Methods: Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volume subdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD)), and total processing time including contouring of ROIs and computation were compared using a paired Student’s t-test. Results: Accuracy of the GPU-accelerated registrations and B-spline registrations, respectively were 88.3 ± 3.7% vs 89.3 ± 4.9% (p = 0.41) for DSC and 13.1 ± 5.2 mm vs 11.4 ± 6.3 mm (p = 0.15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 s vs 557 ± 116 s (p < 0.000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (p = 0.71). Conclusion: The GPU-accelerated volume subdivision technique was as accurate as the B-spline technique and required significantly less processing time. The GPU-accelerated volume subdivision technique may enable the implementation of nonrigid registration into routine clinical practice