Non-rigid registration of liver ct images for ct-guided ablation of liver tumors

CT-guided percutaneous ablation for liver cancer treatment is a relevant technique for patients not eligible for surgery and with tumors that are inconspicuous on US imaging. The lack of real-time imaging and the use of a limited amount of CT contrast agent make targeting the tumor with the needle challenging. In this study, we evaluate a registration framework that allows the integration of diagnostic pre-operative contrast enhanced CT images and intra-operative non-contrast enhanced CT images to improve image guidance in the intervention. The liver and tumor are segmented in the pre-operative contrast enhanced CT images. Next, the contrast enhanced image is registered to the intra-operative CT images in a two-stage approach. First, the contrast-enhanced diagnostic image is non-rigidly registered to a non-contrast enhanced image that is conventionally acquired at the start of the intervention. In case the initial registration is not sufficiently accurate, a refinement step is applied using non-rigid registration method with a local rigidity term. In the second stage, the intra-operative CT-images that are used to check the needle position, which often consist of only a few slices, are registered rigidly to the intra-operative image that was acquired at the start of the intervention. Subsequently, the diagnostic image is registered to the current intra-operative image, using both transformations, this allows the visualization of the tumor region extracted from pre-operative data in the intra-operative CT images containing needle. The method is evaluated on imaging data of 19 patients at the Erasmus MC. Quantitative evaluation is performed using the Dice metric, mean surface distance of the liver border and corresponding landmarks in the diagnostic and the intra-operative images. The registration of the diagnostic CT image to the initial intra-operative CT image did not require a refinement step in 13 cases. For those cases, the resulting registration had a Dice coefficient for the livers of 91.4%, a mean surface distance of 4.4 mm and a mean distance between corresponding landmarks of 4.7 mm. For the three cases with a refinement step, the registration result significantly improved (p<0.05) compared to the result of the initial non rigid registration method (DICE of 90.3% vs 71.3% and mean surface distance of 5.1 mm vs 11.3 mm and mean distanc

Accuracy of semi-automated versus manual localisation of liver tumours in CT-guided ablation procedures

Objectives: To compare the accuracy of liver tumour localisation in intraprocedural computed tomography (CT) images of computer-based rigid registration or non-rigid registration versus mental registration performed by interventional radiologists. Methods: Retrospectively (2009-2017), 35 contrast-enhanced CT (CECT) images incorporating 56 tumours, acquired during CT-guided ablation procedures and their corresponding pre-procedural diagnostic CECTs were retrieved from the picture archiving and communication system (PACS). The original intraprocedural CECTs were de-enhanced to create a virtually unenhanced CT image (VUCT). Alignment of diagnostic CECTs to their corresponding intraprocedural VUCTs was performed with non-rigid or rigid registration. Mental registration was performed by four interventional radiologists. The original intraprocedural CECT served as the reference standard. Accuracy of tumour localisation was assessed with the target registration error (TRE). Statistical differences were analysed with the Wilcoxon sig

Three-Dimensional Quantitative Assessment of Ablation Margins Based on Registration of Pre- and Post-Procedural MRI and Distance Map

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

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

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

A Novel System and Image Processing for Improving 3D Ultrasound-guided Interventional Cancer Procedures

Image-guided medical interventions are diagnostic and therapeutic procedures that focus on minimizing surgical incisions for improving disease management and reducing patient burden relative to conventional techniques. Interventional approaches, such as biopsy, brachytherapy, and ablation procedures, have been used in the management of cancer for many anatomical regions, including the prostate and liver. Needles and needle-like tools are often used for achieving planned clinical outcomes, but the increased dependency on accurate targeting, guidance, and verification can limit the widespread adoption and clinical scope of these procedures. Image-guided interventions that incorporate 3D information intraoperatively have been shown to improve the accuracy and feasibility of these procedures, but clinical needs still exist for improving workflow and reducing physician variability with widely applicable cost-conscience approaches. The objective of this thesis was to incorporate 3D ultrasound (US) imaging and image processing methods during image-guided cancer interventions in the prostate and liver to provide accessible, fast, and accurate approaches for clinical improvements. An automatic 2D-3D transrectal ultrasound (TRUS) registration algorithm was optimized and implemented in a 3D TRUS-guided system to provide continuous prostate motion corrections with sub-millimeter and sub-degree error in 36 ± 4 ms. An automatic and generalizable 3D TRUS prostate segmentation method was developed on a diverse clinical dataset of patient images from biopsy and brachytherapy procedures, resulting in errors at gold standard accuracy with a computation time of 0.62 s. After validation of mechanical and image reconstruction accuracy, a novel 3D US system for focal liver tumor therapy was developed to guide therapy applicators with 4.27 ± 2.47 mm error. The verification of applicators post-insertion motivated the development of a 3D US applicator segmentation approach, which was demonstrated to provide clinically feasible assessments in 0.246 ± 0.007 s. Lastly, a general needle and applicator tool segmentation algorithm was developed to provide accurate intraoperative and real-time insertion feedback for multiple anatomical locations during a variety of clinical interventional procedures. Clinical translation of these developed approaches has the potential to extend the overall patient quality of life and outcomes by improving detection rates and reducing local cancer recurrence in patients with prostate and liver cancer

Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology

Until recently, Computer-Aided Medical Interventions (CAMI) and Medical Robotics have focused on rigid and non deformable anatomical structures. Nowadays, special attention is paid to soft tissues, raising complex issues due to their mobility and deformation. Mini-invasive digestive surgery was probably one of the first fields where soft tissues were handled through the development of simulators, tracking of anatomical structures and specific assistance robots. However, other clinical domains, for instance urology, are concerned. Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU, radiofrequency, or cryoablation), increasingly early detection of cancer, and use of interventional and diagnostic imaging modalities, recently opened new challenges to the urologist and scientists involved in CAMI. This resulted in the last five years in a very significant increase of research and developments of computer-aided urology systems. In this paper, we propose a description of the main problems related to computer-aided diagnostic and therapy of soft tissues and give a survey of the different types of assistance offered to the urologist: robotization, image fusion, surgical navigation. Both research projects and operational industrial systems are discussed