Junichi Tokuda1, William Plishker2, Meysam Torabi3, Olutayo I Olubiyi4, George Zaki2, Servet Tatli3, Stuart G Silverman3, Raj Shekher5, Nobuhiko Hata3. 1. Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis St, ASB-I L1-050, Boston, MA 02115. Electronic address: tokuda@bwh.harvard.edu. 2. IGI Technologies, Inc., Elkridge, Maryland. 3. Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis St, ASB-I L1-050, Boston, MA 02115. 4. Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. 5. Children's National Medical Center, Washington, DC.
Abstract
RATIONALE AND OBJECTIVES: Accuracy and speed are essential for the intraprocedural nonrigid magnetic resonance (MR) to computed tomography (CT) image registration in the assessment of tumor margins during CT-guided liver tumor ablations. Although 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 on the basis of volume subdivision with hardware acceleration using a graphics 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 t test. RESULTS: Accuracies of the GPU-accelerated registrations and B-spline registrations, respectively, were 88.3 ± 3.7% versus 89.3 ± 4.9% (P = .41) for DSC and 13.1 ± 5.2 versus 11.4 ± 6.3 mm (P = .15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 versus 557 ± 116 seconds (P < .000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (P = .71). CONCLUSIONS: 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.
RATIONALE AND OBJECTIVES: Accuracy and speed are essential for the intraprocedural nonrigid magnetic resonance (MR) to computed tomography (CT) image registration in the assessment of tumor margins during CT-guided liver tumor ablations. Although 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 on the basis of volume subdivision with hardware acceleration using a graphics 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 t test. RESULTS: Accuracies of the GPU-accelerated registrations and B-spline registrations, respectively, were 88.3 ± 3.7% versus 89.3 ± 4.9% (P = .41) for DSC and 13.1 ± 5.2 versus 11.4 ± 6.3 mm (P = .15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 versus 557 ± 116 seconds (P < .000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (P = .71). CONCLUSIONS: 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.
Authors: David Mattes; David R Haynor; Hubert Vesselle; Thomas K Lewellen; William Eubank Journal: IEEE Trans Med Imaging Date: 2003-01 Impact factor: 10.048
Authors: Daniel I Glazer; Servet Tatli; Paul B Shyn; Mark G Vangel; Kemal Tuncali; Stuart G Silverman Journal: AJR Am J Roentgenol Date: 2017-09-27 Impact factor: 3.959
Authors: Eung-Joo Lee; William Plishker; Nobuhiko Hata; Paul B Shyn; Stuart G Silverman; Shuvra S Bhattacharyya; Raj Shekhar Journal: J Digit Imaging Date: 2021-10-13 Impact factor: 4.903