Person:

Hata, Nobuhiko

BACKGROUND: Brain surgery faces important challenges when trying to achieve maximum tumor resection while avoiding postoperative neurological deficits. OBJECTIVE: For surgeons to have optimal intraoperative information concerning white matter (WM) anatomy, we developed a platform that allows the intraoperative real-time querying of tractography data sets during frameless stereotactic neuronavigation. METHODS: Structural magnetic resonance imaging, functional magnetic resonance imaging, and diffusion tensor imaging were performed on 5 patients before they underwent lesion resection using neuronavigation. During the procedure, the tracked surgical tool tip position was transferred from the navigation system to the 3-dimensional Slicer software package, which used this position to seed the WM tracts around the tool tip location, rendering a geometric visualization of these tracts on the preoperative images previously loaded onto the navigation system. The clinical feasibility of this approach was evaluated in 5 cases of lesion resection. In addition, system performance was evaluated by measuring the latency between surgical tool tracking and visualization of the seeded WM tracts. RESULTS: Lesion resection was performed successfully in all 5 patients. The seeded WM tracts close to the lesion and other critical structures, as defined by the functional and structural images, were interactively visualized during the intervention to determine their spatial relationships relative to the lesion and critical cortical areas. Latency between tracking and visualization of tracts was less than a second for a fiducial radius size of 4 to 5 mm. CONCLUSION: Interactive tractography can provide an intuitive way to inspect critical WM tracts in the vicinity of the surgical region, allowing the surgeon to have increased intraoperative WM information to execute the planned surgical resection.

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.

Purpose: Contrast-enhanced MR images are widely used to confirm the adequacy of ablation margin after liver ablation for early prediction of local recurrence. However, quantitative assessment of the ablation margin by comparing pre- and post-procedural images remains challenging. We developed and tested a novel method for three-dimensional quantitative assessment of ablation margin based on non-rigid image registration and 3D distance map. Methods: Our method was tested with pre- and post-procedural MR images acquired in 21 patients who underwent image-guided percutaneous liver ablation. The two images were co-registered using non-rigid intensity-based registration. After the tumor and ablation volumes were segmented, target volume coverage, percent of tumor coverage, and Dice Similarity Coefficient were calculated as metrics representing overall adequacy of ablation. In addition, 3D distance map around the tumor was computed and superimposed on the ablation volume to identify the area with insufficient margins. For patients with local recurrences, the follow-up images were registered to the post-procedural image. Three-D minimum distance between the recurrence and the areas with insufficient margins were quantified. Results: The percent tumor coverage for all non-recurrent cases was 100%. Five cases had tumor recurrences, and the 3D distance map revealed insufficient tumor coverage or a 0-millimeter margin. It also showed that two recurrences were remote to the insufficient margin. Conclusions: Non-rigid registration and 3D distance map allows us to quantitatively evaluate the adequacy of the ablation margin after percutaneous liver ablation. The method may be useful to predict local recurrences immediately following ablation procedure.

Rationale and Objectives: To develop non-rigid image registration between pre-procedure contrast enhanced MR images and intra-procedure unenhanced CT images, to enhance tumor visualization and localization during CT-guided liver tumor cryoablation procedures. Materials and Methods: After IRB approval, a non-rigid registration (NRR) technique was evaluated with different pre-processing steps and algorithm parameters and compared to a standard rigid registration (RR) approach. The Dice Similarity Coefficient (DSC), Target Registration Error (TRE), 95% Hausdorff distance (HD) and total registration time (minutes) were compared using a two-sided Student’s t-test. The entire registration method was then applied during five CT-guided liver cryoablation cases with the intra-procedural CT data transmitted directly from the CT scanner, with both accuracy and registration time evaluated. Results: Selected optimal parameters for registration were section thickness of 5mm, cropping the field of view to 66% of its original size, manual segmentation of the liver, B-spline control grid of 5×5×5 and spatial sampling of 50,000 pixels. Mean 95% HD of 3.3mm (2.5x improvement compared to RR, p<0.05); mean DSC metric of 0.97 (13% increase); and mean TRE of 4.1mm (2.7x reduction) were measured. During the cryoablation procedure registration between the pre-procedure MR and the planning intra-procedure CT took a mean time of 10.6 minutes, the MR to targeting CT image took 4 minutes and MR to monitoring CT took 4.3 minutes. Mean registration accuracy was under 3.4mm. Conclusion: Non-rigid registration allowed improved visualization of the tumor during interventional planning, targeting and evaluation of tumor coverage by the ice ball. Future work is focused on reducing segmentation time to make the method more clinically acceptable.

Purpose: To determine whether a non-rigid registration (NRR) technique was more accurate than a rigid registration (RR) technique when fusing pre-procedural contrast-enhanced MR images to unenhanced CT images during CT-guided percutaneous cryoablation of renal tumors. Methods: Both RR and NRR were applied retrospectively to 11 CT-guided percutaneous cryoablation procedures performed to treat renal tumors (mean diameter; 23 mm). Pre-procedural contrast-enhanced MR images of the upper abdomen were registered to unenhanced intra-procedural CT images obtained just prior to the ablation. RRs were performed manually, and NRRs were performed using an intensity-based approach with affine and Basis-Spline techniques used for modeling displacement. Registration accuracy for each technique was assessed using the 95% Hausdorff distance (HD), Fiducial Registration Error (FRE) and the Dice Similarity Coefficient (DSC). Statistical differences were analyzed using a two-sided Student’s t-test. Time for each registration technique was recorded. Results: Mean 95% HD (1.7 mm), FRE (1.7 mm) and DSC (0.96) using the NRR technique were significantly better than mean 95% HD (6.4 mm), FRE (5.0 mm) and DSC (0.88) using the RR technique (P < 0.05 for each analysis). Mean registration times of NRR and RR techniques were 15.2 and 5.7 min, respectively. Conclusions: The non-rigid registration technique was more accurate than the rigid registration technique when fusing pre-procedural MR images to intra-procedural unenhanced CT images. The non-rigid registration technique can be used to improve visualization of renal tumors during CT-guided cryoablation procedures.

Purpose To develop and evaluate an approach to estimate the respiratory-induced motion of lesions in the chest and abdomen. Materials and methods The proposed approach uses the motion of an initial reference needle inserted into a moving organ to estimate the lesion (target) displacement that is caused by respiration. The needles position is measured using an inertial measurement unit (IMU) sensor externally attached to the hub of an initially placed reference needle. Data obtained from the IMU sensor and the target motion are used to train a learning-based approach to estimate the position of the moving target. An experimental platform was designed to mimic respiratory motion of the liver. Liver motion profiles of human subjects provided inputs to the experimental platform. Variables including the insertion angle, target depth, target motion velocity and target proximity to the reference needle were evaluated by measuring the error of the estimated target position and processing time. Results: The mean error of estimation of the target position ranged between 0.86 and 1.29 mm. The processing maximum training and testing time was 5 ms which is suitable for real-time target motion estimation using the needle position sensor. Conclusion: The external motion of an initially placed reference needle inserted into a moving organ can be used as a surrogate, measurable and accessible signal to estimate in real-time the position of a moving target caused by respiration; this technique could then be used to guide the placement of subsequently inserted needles directly into the target.