Rationale and Objectives
The aim of this study was to develop non-rigid image registration between preprocedure contrast-enhanced magnetic resonance (MR) images and intraprocedure unenhanced computed tomographic (CT) images, to enhance tumor visualization and localization during CT imaging–guided liver tumor cryoablation procedures.
Materials and Methods
A non-rigid registration technique was evaluated with different preprocessing steps and algorithm parameters and compared to a standard rigid registration approach. The Dice similarity coefficient, target registration error, 95th-percentile Hausdorff distance, 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 imaging–guided liver cryoablation cases with the intraprocedural CT data transmitted directly from the CT scanner, with both accuracy and registration time evaluated.
Results
Selected optimal parameters for registration were a section thickness of 5 mm, 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. A mean 95th-percentile Hausdorff distance of 3.3 mm (a 2.5 times improvement compared to rigid registration, P < .05), a mean Dice similarity coefficient of 0.97 (a 13% increase), and a mean target registration error of 4.1 mm (a 2.7 times reduction) were measured. During the cryoablation procedure, registration between the preprocedure MR and the planning intraprocedure CT imaging took a mean time of 10.6 minutes, MR to targeting CT image took 4 minutes, and MR to monitoring CT imaging took 4.3 minutes. Mean registration accuracy was <3.4 mm.
Conclusions
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.
Computed tomographic (CT) imaging is used to guide percutaneous liver tumor cryoablation and has proven particularly useful when the tumor is not visible with ultrasound . CT can be used to plan the interventional approach, to facilitate the safe placement of the ablation applicators in the tumor, and to monitor the ablation effects in the case of cryoablation .
Despite the benefits of CT imaging, there can be challenges related to the lack of soft tissue contrast for liver tumors on unenhanced CT images, especially for small or poorly marginated tumors and when there are contraindications to the use of intravenous contrast material . The tumor selected for ablation and the adjacent structures at risk for injury during the procedure may be invisible or poorly visible . Suboptimal visibility can lead to improper applicator placement, resulting in inadequate ablation beyond the tumor margins or thermal injury to adjacent structures . To overcome this problem, interventional radiologists often rely on preprocedure contrast-enhanced CT imaging or magnetic resonance (MR) imaging (MRI) that depicts tumor margins and surrounding structures, including vascular anatomy. The radiologist then performs a mental correlation of the preprocedure and intraprocedure images to estimate tumor location, tumor boundaries, and adjacent anatomic structures. This can be challenging because the liver position, shape, and relation to extrahepatic structures may differ significantly between two exams.
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Materials and methods
Patient Population
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Image Acquisition
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Non-rigid and Rigid Registration Methods
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Registration Assessment Metrics
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Optimization of Registration Between Preprocedural MR and Intraprocedural CT Images
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First Optimization Stage
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Table 1
Experiments Performed to Evaluate the Effects of Different Parameters and Preprocessing Steps on the Accuracy and Time of the Non-rigid Registration Method Between Preprocedure Contrast-enhanced MR and Intraprocedure Unenhanced CT Images
Parameter Set Section Thickness (mm) FOV Size Segmentation Liver Volume 1 (reference) 5 66% Yes >75% 2 3 66% Yes >75% 3 7.5 66% Yes >75% 4 5 100% Yes >75% 5 5 Liver Yes >75% 6 5 66% No >75% 7 5 66% Every 2 slices >75% 8 5 66% Yes 50%–75%
CT, computed tomographic; FOV, field of view; MR, magnetic resonance.
Parameter set 1 acts as a reference set of parameters, and the other sets are defined by modifying a single parameter value from the reference set.
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Second Optimization Stage
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Table 2
Experiments Performed to Evaluate the Effects of Different Algorithmic Parameters on the Accuracy and Time of the Non-rigid Registration Method Between Preprocedure Contrast-enhanced MR and Intraprocedure Unenhanced CT Images
Parameter Set Number of Control Points per Direction Sampling Pixels 1 (reference) 5 50,000 2 4 50,000 3 3 50,000 4 4 20,000 5 3 20,000 6 4 2000
CT, computed tomographic; MR, magnetic resonance.
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Comparison with Rigid Registration
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Registration of Intraprocedural CT Images
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Registration During CT Imaging–guided Liver Tumor Ablation
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Results
Registration for Preprocedural MR to Intraprocedural CT Images
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Registration of Intraprocedural CT Images
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Table 3
Average 95% HD, DSC, and Total Registration Time Measured for the Two Liver Registration Methods for Five Patient Data Sets
Method 1 Method 2 Registered Images 95% HD (mm) DSC Time (min) 95% HD (mm) DSC Time (min) MR to planning CT images 3.29 0.97 19.2 3.29 0.97 19.2 MR to targeting CT images 3.9 0.94 10.2 3.5 0.96 4.6 MR to monitoring CT images 3.9 0.93 10.4 4 0.96 4.3
CT, computed tomographic; DSC, Dice similarity coefficient; HD, Hausdorff distance; MR, magnetic resonance.
Method 1 was a direct registration of preprocedure MR to intraprocedure CT images, while method 2 used the transformation obtained from CT-to-CT registration to deform and register the MR image.
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Registration During CT Imaging–guided Liver Tumor Ablation
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Discussion
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Conclusions
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