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.

Percutaneous Image-Guided Cryoablation of Hepatic Tumors: Single-Center Experience With Intermediate to Long-Term Outcomes

“…Our results are also comparable to those reported with RFA in the treatment of hepatic metastases and HCC [9, 10, 30]. Reported technical success rates of RFA for metastases have varied from 93% to 100%, and technique efficacy rates have varied from 60.9% to 88.1% [9, 39].…”

“…Relative to heat-based ablation techniques, the principal advantage of cryoablation is that the effects of freezing (visualized as an ice ball) are well depicted intraprocedurally with CT and MRI and, to a lesser extent, with ultrasound; thus, the ablation can be monitored intraprocedurally [30, 31]. Intraprocedural monitoring of ice ball formation maximizes the chance of treating a tumor completely and minimizes the chance of harming adjacent critical structures.…”

Three-dimensional quantitative assessment of ablation margins based on registration of pre- and post-procedural MRI and distance map

“…The technique could also be useful for monitoring the progress of ablation in real-time during the procedure. For example, the technique could be combined with intraprocedural non-rigid image registration for intraprocedure margin assessment [10, 37,49], MR thermometry for microwave ablation monitoring[30], MR-based ice ball monitoring for MRI-guided cryoablations[24]. In addition, the method could potentially integrated with other real-time imaging modalities such as contrast-enhanced ultrasound [52,6].…”

Comparing Two Processing Pipelines to Measure Subcortical and Cortical Volumes in Patients with and without Mild Traumatic Brain Injury

“…One of the more robust and consistently used methods includes the quantification of volumetric features of cortical and subcortical structures in the brain . Hardware and software advances have improved the speed and accuracy of these measurements across neurologic disorders leading to improvements in diagnosis, understanding functional features of disease and injury processes, conceptualizing and assessing treatment approaches, tracking changes over time, and determining prognoses . Currently, most of these technologies remain in the research realm though a few have begun to be used in clinical settings and can be used to augment clinical judgment.…”