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.
Computed tomography (CT) is used commonly to guide percutaneous tumor ablations of liver tumors . CT can provide high-quality three-dimensional (3D) images of the liver intraprocedurally, which help radiologists understand spatial relationship of the tumor with respect to the surrounding structures . However, typically, only sectional unenhanced CT images are used, and hence, tumor margins may not be delineated well ; this limitation may contribute to inadequate ablation . A recent study showed that the rate of local recurrence following percutaneous radiofrequency ablation of hepatocellular carcinoma decreased significantly from 23% to 0% when the ablation margin, the shortest distance between the outer margin of the tumor and the outer margin of the ablation, was increased from <1 to >3 mm . Therefore, visualizing tumor margins intraprocedurally is important. One way to delineate tumor margins intraprocedurally is to register preprocedural magnetic resonance (MR) images to the intraprocedural CT images; tumor margins depicted by MRI can be directly compared to ablation effects depicted with intraprocedural CT . In addition, the same data can be used to depict tumor and ablation volumes .
Nonrigid image registration techniques can be used to correct accurately the misalignment between structures in two images caused by physiologic variations . For example, the liver may be misaligned because of diaphragmatic motion. B-spline registration is a commonly used nonrigid registration technique that parameterizes a deformation of an image as displacements of control points on a regular grid and calculates the displacements of the voxels between the control points using basis spline (B-spline) interpolation . However, nonrigid registration techniques are not widely used clinically for intraprocedural registration because the registration takes too long to perform and often needs a dedicated software operator. Although computation time for B-spline registration can be shortened by reducing the control points, fewer control points may impact the registration accuracy negatively. In particular, the B-spline cannot model local deformations well with limited control points. A previous analysis of four-dimensional CT by Schreibmann et al. used 15 control points per direction to model the deformations of liver images accurately. This number of control points is much larger than those used in previous studies on intraoperative registration, typically 3–5 control points per direction, which still takes up to 2 minutes . To attain sufficient registration accuracy, a region of interest (ROI) is often used . An ROI can restrict the region to be registered on the liver only and can reduce the effect of local displacement in the surrounding structures. An ROI also helps reduce computation time by reducing the number of voxels to be processed. However, an ROI must be defined by contouring the target organ. Robust automatic contouring of the target organ is a challenging problem and would invariably introduce additional computation time. Manual contouring has been used in past studies but would require an additional human resource for routine clinical use. The long computation time and the need for manual contouring hinder widespread use of nonrigid image registration.
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Materials and methods
Subjects
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Imaging Protocol
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Image Registration Algorithms
GPU-Accelerated Nonrigid Registration Using Volume Subdivision Approach
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Nonrigid B-Spline Registration
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Comparison of Registration Accuracy and Processing Time
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Results
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Table 1
Summary of Results of Nonrigid Registration Between Preprocedural MRI and Intraprocedural CT Images Using B-Spline and GPU-Accelerated Registration Techniques
Mean ± SD Range_P_ Value DSC (%) .41 GPU 88.30 ± 3.74 80.49–93.11 B-Spline 89.32 ± 4.90 79.70–94.46 95% Hausdorff distance (mm) .15 GPU 13.14 ± 5.19 6.43–24.54 B-Spline 11.44 ± 6.32 5.43–23.04 Time, rigid reg. (s) <.002 GPU 8.58 ± 6.31 4.36–29.28 B-Spline 20.53 ± 7.01 14.56–40.08 Time, nonrigid reg. (s) <.00002 GPU 76.90 ± 10.75 61.5–91.43 B-Spline 42.48 ± 11.39 19.38–68.57 Time, resampling (s) <.0000000001 GPU 2.07 ± 0.47 1–3 B-Spline 27.72 ± 5.02 19.87–40.71 Time, total computation (s) .71 GPU 87.55 ± 14.26 69.10–118.25 B-Spline 90.72 ± 21.81 58.42–149.36 Voxel throughput (voxels/s) <.0004 GPU 195024.67 ± 141785.32 104192.50–614978.60 B-Spline 18,050.36 ± 6324.58 12001.98–32628.07 Time, manual ROI contouring NA GPU NA NA B-Spline 466 ± 104 300–701 Time, total processing <.000000002 GPU 87.55 ± 14.26 69.10–118.25 B-Spline 557.08 ± 116.47 375.83–850.36
CT, computed tomography; GPU, graphics processing unit; MRI, magnetic resonance imaging; NA, not applicable; ROI, region of interest.
Table 2
Type of MRI Scanner, Phase of CE MRI Used for Registration, and Registration Performances Including DSC, 95% HD, Processing Time, and Voxel Throughput for Each Subject
Case MRI Scanner (T) CE MRI Phase DSC (%) HD (mm) Process Time: Rigid Reg. (s) Process Time: Nonrigid Reg. (s) Process Time: Resampling (s) Total Process Time (s) Time: Manual Contouring (s) Total Workflow Time (s) Voxel Throughput (Voxels/s) GPU BS GPU BS GPU BS GPU BS GPU BS GPU BS GPU BS GPU BS GPU BS 1 1.5 2 87.9 87.7 13.1 18.1 7.0 40.1 65.3 68.6 2.0 40.7 74.3 149.4 — 701.0 74.3 850.4 614978.6 29900.7 2 3.0 2 83.0 79.7 24.5 23.0 9.7 16.0 90.6 36.6 2.0 29.4 102.4 82.1 — 499.0 102.4 581.1 107551.5 22465.2 3 3.0 3 91.7 92.4 10.0 10.2 5.2 23.7 62.9 51.9 1.0 28.9 69.1 104.6 — 419.0 69.1 523.6 288972.5 18045.4 4 1.5 1 88.5 93.4 10.2 5.6 4.9 26.5 81.5 51.4 2.0 33.7 88.4 111.6 — 512.0 88.4 623.6 237207.6 12002.0 5 3.0 1 80.5 88.7 20.9 9.0 7.3 16.0 83.8 38.9 2.0 24.1 93.1 79.0 — 550.0 93.1 629.0 109813.3 14060.3 6 3.0 2 90.3 80.5 12.0 19.7 12.0 14.6 72.1 34.4 2.0 24.7 86.1 73.7 — 484.0 86.1 557.7 106612.1 17557.9 7 1.5 3 84.2 83.1 18.7 13.5 4.4 26.9 65.5 42.4 2.0 19.9 71.9 89.2 — 571.0 71.9 660.2 335427.6 17322.2 8 1.5 2 93.1 93.2 6.4 6.6 9.0 21.2 61.5 46.5 2.0 27.8 72.5 95.5 — 417.0 72.5 512.5 190781.1 14972.4 9 3.0 3 89.2 91.0 10.0 6.4 5.2 19.3 74.4 43.0 2.0 26.1 81.6 88.4 — 420.0 81.6 508.4 135680.3 13036.0 10 3.0 3 89.1 89.6 13.2 20.4 29.3 15.1 87.0 19.4 2.0 23.9 118.3 58.4 — 518.0 118.3 576.4 104192.5 32628.1 11 3.0 3 85.1 92.8 16.4 6.1 7.0 19.6 91.4 51.1 3.0 30.5 101.4 101.2 — 429.0 101.4 530.2 131795.6 12035.0 12 3.0 1 91.1 90.6 10.8 10.2 7.3 15.7 90.1 36.0 3.0 26.7 100.3 78.4 — 371.0 100.3 449.4 122826.9 18354.8 13 3.0 2 90.5 93.3 9.3 6.0 5.8 14.8 71.4 36.1 2.0 25.0 79.2 75.8 — 300.0 79.2 375.8 109240.5 13329.1 14 1.5 2 91.9 94.5 8.4 5.4 6.2 18.1 79.0 38.2 2.0 26.7 87.2 83.0 — 338.0 87.2 421.0 135265.2 16996.0
BS, B-Spline; CE, contrast-enhanced; DSC, Dice similarity coefficient; GPU, graphics processing unit; HD, Hausdorff distance; MRI, magnetic resonance imaging.
CE MRI phase: 1 = 30 s; 2 = 60 s; 3 = 90s.
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Discussion
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