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Acad Radiol. 2015 Jun;22(6):722-33. doi: 10.1016/j.acra.2015.01.007. Epub 2015 Mar 14.

Graphics Processing Unit-Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations.

Author information

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.

KEYWORDS:

B-spline; GPU-accelerated image processing; Nonrigid image registration; mutual information

PMID:
25784325
PMCID:
PMC4428967
DOI:
10.1016/j.acra.2015.01.007
[Indexed for MEDLINE]
Free PMC Article

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