Graphics Processing Unit–Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations
MetadataShow full item record
CitationTokuda, Junichi, William Plishker, Meysam Torabi, Olutayo I. Olubiyi, George Zaki, Servet Tatli, Stuart G. Silverman, Raj Shekher, and Nobuhiko Hata. 2015. “Graphics Processing Unit–Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations.” Academic Radiology 22 (6) (June): 722–733. doi:10.1016/j.acra.2015.01.007.
AbstractRationale 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:33896763
- HMS Scholarly Articles