The treatment of cancerous tumours in the liver remains clinically challenging, despite the wide range of treatment possibilities, including radio-frequency ablation (RFA), highintensity focused ultrasound and resection, which are currently available. Each has its own advantages and disadvantages. For non-or minimally invasive modalities, such as RFA, considered here, it is difficult to monitor the treatment in vivo. This is particularly problematic in the liver, where large blood vessels act as heat sinks, dissipating delivered heat and shrinking the size of the lesion (the volume damaged by the heat treatment) locally; considerable experience is needed on the part of the clinician to optimize the heat treatment to prevent recurrence. In this paper, we outline our work towards developing a simulation tool kit that could be used both to optimize treatment protocols in advance and to train the less-experienced clinicians for RFA treatment of liver tumours. This tool is based on a comprehensive mathematical model of bio-heat transfer and cell death. We show how simulations of ablations in two pigs, based on individualized imaging data, compare directly with experimentally measured lesion sizes and discuss the likely sources of error and routes towards clinical implementation. This is the first time that such a 'loop' of mathematical modelling and experimental validation in vivo has been performed in this context, and such validation enables us to make quantitative estimates of error.

A multi-centre retrospective study indicates that the fast RFA solver is capable of providing the IR with the predicted lesion in the short time period before the intervention begins when the patient has been clinically prepared for the treatment.

The RFA Guardian is a comprehensive application for high-performance patient-specific simulation of radiofrequency ablation of liver tumors. We address a wide range of usage scenarios. These include pre-interventional planning, sampling of the parameter space for uncertainty estimation, treatment evaluation and, in the worst case, failure analysis. The RFA Guardian is the first of its kind that exhibits sufficient performance for simulating treatment outcomes during the intervention. We achieve this by combining a large number of high-performance image processing, biomechanical simulation and visualization techniques into a generalized technical workflow. Further, we wrap the feature set into a single, integrated application, which exploits all available resources of standard consumer hardware, including massively parallel computing on graphics processing units. This allows us to predict or reproduce treatment outcomes on a single personal computer with high computational performance and high accuracy. The resulting low demand for infrastructure enables easy and cost-efficient integration into the clinical routine. We present a number of evaluation cases from the clinical practice where users performed the whole technical workflow from patient-specific modeling to final validation and highlight the opportunities arising from our fast, accurate prediction techniques.Radiofrequency ablation (RFA) of liver malignancies has become an important alternative therapy for patients who disqualify for standard surgical treatment or are in an early tumor stage 1,2 . When surgical resection is not feasible, RFA is the preferred treatment option for small liver tumors 1,2 . Moreover, patient recovery after surgical resection takes longer and post-procedural quality of life is lower than after RFA 2 .While many more options for local cancer treatment exist (e.g. Cryo Ablation 3 , Irreversible Electroporation 4 or hyperthermia in conjunction with other treatment methods 5,6 ), the clincial routine prefers RFA (or, occasionally, microwave ablation) treatment for smaller liver tumors. Although microwave ablation has become more prevalent in the past years, no statistically significant difference in survival rates compared to RFA of smaller lesions (diameter below 3.5 cm) in the liver could be found 7,8 .In RFA, interventional radiologists (IR) destroy malignant cells using percutaneous probes that induce heating in a locally delimited region around a tumor. Successful treatment is defined as complete ablation of the tumor with a safety margin of destroyed healthy tissue in its immediate vicinity.However, clinical experience with RFA indicates a significant mismatch between expected and observed lesion size, leading to reduced survival rates due to over-treatment with severe injuries (up to 9%) or under-treatment with tumor recurrence 9 (up to 40%). Further, Hildebrand et al. 10 have shown that the survival rates after 1 and 2 years significantly depend on the experience of the IR: Operating experience of 0-2 years resulted in 69...

From 1980 through 1982, intravenous extracranial digital subtraction angiography (DSA) was performed in 6684 patients at the Cleveland Clinic. Of these, 290 previously unoperated patients had asymptomatic carotid stenosis exceeding 50% of lumen diameter on unequivocal DSA studies. Either the presence or the absence of carotid bruits substantially misrepresented the severity of angiographic stenosis on approximately 30% of sides. Nonoperative management was employed in 195 patients, including 104 (53%) who received antiplatelet therapy, while another group of 95 patients underwent prophylactic carotid endarterectomy. During mean follow-up intervals of 33-38 months, surgical treatment significantly reduced the cumulative incidence of subsequent neurologic events in men (p = 0.05). Statistically unconfirmed trends also suggested that carotid endarterectomy tended to prevent late strokes in subsets of patients with greater than 70% stenosis or bilateral carotid lesions. The overall stroke rate for women was higher in the surgical group (p = 0.03), in part because of their unusual risk for perioperative complications (9%) in this particular series.

In this paper, a novel segmentation method for liver vasculature is presented, intended for numerical simulation of radio frequency ablation (RFA). The developed method is a semiautomatic hybrid based on multi-scale vessel enhancement combined with ridge-oriented region growing and skeleton-based postprocessing. In addition, an interactive tool for segmentation refinement was developed. Four instances of threephase contrast enhanced computed tomography (CT) images of porcine liver were used in the evaluation. The results showed improved accuracy over common approaches and illustrated the method's suitability for simulation purposes.

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IntroductionRadio-frequency ablation (RFA) is a promising minimal-invasive treatment option for early liver cancer, however monitoring or predicting the size of the resulting tissue necrosis during the RFA-procedure is a challenging task, potentially resulting in a significant rate of under- or over treatments. Currently there is no reliable lesion size prediction method commercially available.ObjectivesClinicIMPPACT is designed as multicenter-, prospective-, non-randomized clinical trial to evaluate the accuracy and efficiency of innovative planning and simulation software. 60 patients with early liver cancer will be included at four European clinical institutions and treated with the same RFA system. The preinterventional imaging datasets will be used for computational planning of the RFA treatment. All ablations will be simulated simultaneously to the actual RFA procedure, using the software environment developed in this project. The primary outcome measure is the comparison of the simulated ablation zones with the true lesions shown in follow-up imaging after one month, to assess accuracy of the lesion prediction.DiscussionThis unique multicenter clinical trial aims at the clinical integration of a dedicated software solution to accurately predict lesion size and shape after radiofrequency ablation of liver tumors. Accelerated and optimized workflow integration, and real-time intraoperative image processing, as well as inclusion of patient specific information, e.g. organ perfusion and registration of the real RFA needle position might make the introduced software a powerful tool for interventional radiologists to optimize patient outcomes.

Data below 1 mm voxel size is getting more and more common in the clinical practice but it is still hard to obtain a consistent collection of such datasets for medical image processing research. With this paper we provide a large collection of Contrast Enhanced (CE) Computed Tomography (CT) data from porcine animal experiments and describe their acquisition procedure and peculiarities. We have acquired three CE-CT phases at the highest available scanner resolution of 57 porcine livers during induced respiratory arrest. These phases capture contrast enhanced hepatic arteries, portal venous veins and hepatic veins. Therefore, we provide scan data that allows for a highly accurate reconstruction of hepatic vessel trees. Several datasets have been acquired during Radio-Frequency Ablation (RFA) experiments. Hence, many datasets show also artificially induced hepatic lesions, which can be used for the evaluation of structure detection methods.