Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring During CT-Guided Percutaneous Liver Tumor Cryoablation

Elhawary, Haytham; Oguro, Sota; Tuncalı, Kemal; Morrison, Paul T.; Tatlı, Servet; Shyn, Paul B.; Silverman, Stuart G.; Hata, Nobuhiko

doi:10.1016/j.acra.2010.06.004

Cited by 44 publications

(44 citation statements)

References 38 publications

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“…The HD is symmetric to two input images, with a smaller value indicating a better alignment of brain boundaries. We used the 95th percentile other than the maximum HD, to avoid the influence of outliers, as suggested in [67] and [68]. …”

Section: Evaluation Protocolmentioning

confidence: 99%

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

Akbari

Bilello

et al. 2014

IEEE Trans. Med. Imaging

164

170

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Evaluating various algorithms for the inter-subject registration of brain magnetic resonance images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated image registration algorithms in specific tasks or using specific databases (e.g., only for skull-stripped images, only for single-site images, etc.). Consequently, the choice of registration algorithms seems task- and usage/parameter-dependent. Nevertheless, recent large-scale, often multi-institutional imaging-related studies create the need and raise the question whether some registration algorithms can 1) generally apply to various tasks/databases posing various challenges; 2) perform consistently well, and while doing so, 3) require minimal or ideally no parameter tuning. In seeking answers to this question, we evaluated 12 general-purpose registration algorithms, for their generality, accuracy and robustness. We fixed their parameters at values suggested by algorithm developers as reported in the literature. We tested them in 7 databases/tasks, which present one or more of 4 commonly-encountered challenges: 1) inter-subject anatomical variability in skull-stripped images; 2) intensity homogeneity, noise and large structural differences in raw images; 3) imaging protocol and field-of-view (FOV) differences in multi-site data; and 4) missing correspondences in pathology-bearing images. Totally 7,562 registrations were performed. Registration accuracies were measured by (multi-)expert-annotated landmarks or regions of interest (ROIs). To ensure reproducibility, we used public software tools, public databases (whenever possible), and we fully disclose the parameter settings. We show evaluation results, and discuss the performances in light of algorithms’ similarity metrics, transformation models and optimization strategies. We also discuss future directions for the algorithm development and evaluations.

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Section: Evaluation Protocolmentioning

confidence: 99%

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

Akbari

Bilello

et al. 2014

IEEE Trans. Med. Imaging

164

170

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“…A previous analysis of 4D CT by Schreibmann et al used 15 control points per direction to model the deformations of liver images accurately (17). 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 (12,16). To attain sufficient registration accuracy, a region of interest (ROI) is often employed (12,16,18,19).…”

Section: Introductionmentioning

confidence: 97%

“…Therefore, visualizing tumor margins intraprocedurally is important. One way to delineate tumor margins intraprocedurally is to register preprocedural MR images to the intraprocedural CT images; tumor margins depicted by MRI can be directly compared to ablation effects depicted with intraprocedural CT (12). In addition, the same data can be used to depict tumor and ablation volumes (12).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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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.

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“…Assuming the lowest image resolution of our test images, which was 1.188 mm per pixel, the above mentioned segmentation error would translate to 3.3 mm. For comparison, a study on registering CT with MRI images of the liver performed in 2005 reports a mean registration error of 14.0–18.9 mm,69 while a newer study from 2010 reports a mean error of 3.3 mm 70. If a segmentation of hepatic vessels was performed on CT images and then registered with MRI images for colorectal metastases segmentation the total error of vessel model would be even higher due to the error of segmentation on CT images itself.…”

Section: Discussionmentioning

confidence: 99%

Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver

Marčan

Pavliha

Mušič

et al. 2014

Radiology and Oncology

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Introduction.Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses delivered to tissue via electrodes. To ensure that the whole tumor is covered by the sufficiently high electric field, accurate numerical models are built based on individual patient geometry. For the purpose of reconstruction of hepatic vessels from MRI images we searched for an optimal segmentation method that would meet the following initial criteria: identify major hepatic vessels, be robust and work with minimal user input.Materials and methods.We tested the approaches based on vessel enhancement filtering, thresholding, and their combination in local thresholding. The methods were evaluated on a phantom and clinical data.ResultsResults show that thresholding based on variance minimization provides less error than the one based on entropy maximization. Best results were achieved by performing local thresholding of the original de-biased image in the regions of interest which were determined through previous vessel-enhancement filtering. In evaluation on clinical cases the proposed method scored in average sensitivity of 93.68%, average symmetric surface distance of 0.89 mm and Hausdorff distance of 4.04 mm.ConclusionsThe proposed method to segment hepatic vessels from MRI images based on local thresholding meets all the initial criteria set at the beginning of the study and necessary to be used in treatment planning of electroporation-based treatments: it identifies the major vessels, provides results with consistent accuracy and works completely automatically. Whether the achieved accuracy is acceptable or not for treatment planning models remains to be verified through numerical modeling of effects of the segmentation error on the distribution of the electric field.

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Multimodality Non-rigid Image Registration for Planning, Targeting and Monitoring During CT-Guided Percutaneous Liver Tumor Cryoablation

Cited by 44 publications

References 38 publications

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

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

Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver

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