Statistical atlas based registration and planning for ablating bone tumors in minimally invasive interventions

Bone tumor ablation has been a viable treatment in a minimally invasive way compared with surgical resections. In this paper, two key challenges in the computer-Assisted bone tumor ablation have been addressed: 1) establishing the spatial transformation of patient's tumor with respect to a global map of the patient using a minimum number of intra-operative images and 2) optimal treatment planning for large tumors. Statistical atlas is employed to construct the global reference map. The atlas is deformably registered to a pair of intra-operative fluoroscopy images, constructing a patient-specific model, in order to reduce the radiation exposure to the sensitive patients such as pregnant and infants. The optimal treatment planning system incorporates clinical constraints on ablations and trajectories using a multiple objective optimization, which obtains optimal trajectory planning and ablation coverage using integer programming. The proposed system is presented and validated by experiments. © 2012 IEEE.published_or_final_versio

Liver Segmentation and its Application to Hepatic Interventions

The thesis addresses the development of an intuitive and accurate liver segmentation approach, its integration into software prototypes for the planning of liver interventions, and research on liver regeneration. The developed liver segmentation approach is based on a combination of the live wire paradigm and shape-based interpolation. Extended with two correction modes and integrated into a user-friendly workflow, the method has been applied to more than 5000 data sets. The combination of the liver segmentation with image analysis of hepatic vessels and tumors allows for the computation of anatomical and functional remnant liver volumes. In several projects with clinical partners world-wide, the benefit of the computer-assisted planning was shown. New insights about the postoperative liver function and regeneration could be gained, and most recent investigations into the analysis of MRI data provide the option to further improve hepatic intervention planning

COMPUTED-AIDED AND ROBOT-ASSISTED RADIOFREQUENCY ABLATION OF LARGE LIVER TUMOR

Ph.DDOCTOR OF PHILOSOPH

Enter the matrix:On how to improve thyroid nodule management using 3D ultrasound

Roughly two-thirds of the adult population has a thyroid nodule, of which 90% are benign. Of the adults that have a nodule, approximately 5% will experience symptoms that include a feeling of a marble stuck in the throat, difficulty swallowing and breathing, and cosmetic complaints. Thyroid nodule management primarily makes use of ultrasound as the imaging modality for diagnosis, image guidance during therapy (radiofrequency ablation i.e. RFA), and follow-up. Although ultrasound is relatively easy to apply, it is hard to standardize for repeated measurements and across various users. Further, RFA can benefit from 3D imaging information and a planning and navigation system to improve clinical outcome. These challenges may be overcome by using 3D ultrasound. In this thesis, two phantoms were created on which these methods can be developed. Further, it offers insight into the use of 2D and 3D ultrasound for thyroid nodule management.To assess the impact of changes to an intervention, a baseline was determined of the effectiveness of RFA in Dutch hospitalsUsing a simple phantom, we have shown that utilizing a volume-based measurement technique, that the matrix transducer offers, results in improved measurement accuracy. The more complex, anthropomorphic, phantom serves as a platform on which thermal treatments, such as RFA, can be improved. Using this phantom, we have shown that the impact of 2D and 3D ultrasound on RFA efficacy does not differ from one another; however, the matrix transducer might be more user-friendly for needle placement due to the dual-plane imaging. An additional use case for these phantoms is their capacity to compare dominant and non-dominant hand ablations, as well as serve as a training platform. Additional research is required that employs more operators to find stronger evidence supporting a difference between the ablating hands and the difference in effect of 2D and 3D ultrasound guidance.To make full use of 3D ultrasound, stitching algorithms should be integrated into the ultrasound systems to acquire larger volumes. These can then be processed by deep-learning algorithms for use in computer-aided diagnosis and intervention systems. To further improve the applicability of 3D ultrasound in the clinic, integrating analysis methods such as 3D elastography and 3D Doppler is suggested

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

Liver Tumors

This book is oriented towards clinicians and scientists in the field of the management of patients with liver tumors. As many unresolved problems regarding primary and metastatic liver cancer still await investigation, I hope this book can serve as a tiny step on a long way that we need to run on the battlefield of liver tumors