Volume Rendering with Advanced GPU Scheduling Strategies

Modern GPUs are powerful enough to enable interactive display of high-quality volume data even despite the fact that many volume rendering methods do not present a natural fit for current GPU hardware. However, there still is a vast amount of computational power that remains unused due to the inefficient use of the available hardware. In this work, we demonstrate how advanced scheduling methods can be employed to implement volume rendering algorithms in a way that better utilizes the GPU by example of three different state-of-the-art volume rendering techniques

A prospective development study of software-guided radio-frequency ablation of primary and secondary liver tumors: Clinical intervention modelling, planning and proof for ablation cancer treatment (ClinicIMPPACT)

Introduction: Radio-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.Objectives: ClinicIMPPACT 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 pre-interventional 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.Discussion: This 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

A prospective development study of software-guided radio-frequency ablation of primary and secondary liver tumors: Clinical intervention modelling, planning and proof for ablation cancer treatment (ClinicIMPPACT)

Introduction Radio-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. Objectives ClinicIMPPACT 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. Discussion This 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

GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours

PURPOSE: Radiofrequency ablation (RFA) is one of the most popular and well-standardized minimally invasive cancer treatments (MICT) for liver tumours, employed where surgical resection has been contraindicated. Less-experienced interventional radiologists (IRs) require an appropriate planning tool for the treatment to help avoid incomplete treatment and so reduce the tumour recurrence risk. Although a few tools are available to predict the ablation lesion geometry, the process is computationally expensive. Also, in our implementation, a few patient-specific parameters are used to improve the accuracy of the lesion prediction. METHODS: Advanced heterogeneous computing using personal computers, incorporating the graphics processing unit (GPU) and the central processing unit (CPU), is proposed to predict the ablation lesion geometry. The most recent GPU technology is used to accelerate the finite element approximation of Penne's bioheat equation and a three state cell model. Patient-specific input parameters are used in the bioheat model to improve accuracy of the predicted lesion. RESULTS: A fast GPU-based RFA solver is developed to predict the lesion by doing most of the computational tasks in the GPU, while reserving the CPU for concurrent tasks such as lesion extraction based on the heat deposition at each finite element node. The solver takes less than 3 min for a treatment duration of 26 min. When the model receives patient-specific input parameters, the deviation between real and predicted lesion is below 3 mm. CONCLUSION: 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

Software-based planning of ultrasound and CT-guided percutaneous radiofrequency ablation in hepatic tumors

Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.Objectives: Radiofrequency ablation (RFA) can be associated with local recurrences in the treatment of liver tumors. Data obtained at our center for an earlier multinational multicenter trial regarding an in-house developed simulation software were re-evaluated in order to analyze whether the software was able to predict local recurrences. Methods: Twenty-seven RFA ablations for either primary or secondary hepatic tumors were included. Colorectal liver metastases were shown in 14 patients and hepatocellular carcinoma in 13 patients. Overlap of the simulated volume and the tumor volume was automatically generated and defined as positive predictive value (PPV) and additionally visually assessed. Local recurrence during follow-up was defined as gold standard. Sensitivity and specificity were calculated using the visual assessment and gold standard. Results: Mean tumor size was 18 mm (95% CI 15–21 mm). Local recurrence occurred in 5 patients. The PPV of the simulation showed a mean of 0.89 (0.84–0.93 95% CI). After visual assessment, 9 incomplete ablations were observed, of which 4 true positives and 5 false positives for the detection of an incomplete ablation. The sensitivity and specificity were, respectively, 80% and 77% with a correct prediction in 78% of cases. No significant correlation was found between size of the tumor and PPV (Pearson Correlation 0.10; p = 0.62) or between PPV and recurrence rates (Pearson Correlation 0.28; p = 0.16). Conclusions: The simulation software shows promise in estimating the completeness of liver RFA treatment and predicting local recurrence rates, but could not be performed real-time. Future improvements in the field of registration could improve results and provide a possibility for real-time implementation.Peer reviewe

Diagnostics et modelisation d'un plasma d'argon-hydrogene cree par decharge H.F. inductive utilisee pour la gravure de silicium

SIGLECNRS T 58179 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Clinical evaluation of in silico planning and real-time simulation of hepatic radiofrequency ablation (ClinicIMPPACT Trial)

Contains fulltext : 219631.pdf (Publisher’s version ) (Closed access)OBJECTIVES: To evaluate the accuracy and clinical integrability of a comprehensive simulation tool to plan and predict radiofrequency ablation (RFA) zones in liver tumors. METHODS: Forty-five patients with 51 malignant hepatic lesions of different origins were included in a prospective multicenter trial. Prior to CT-guided RFA, all patients underwent multiphase CT which included acquisitions for the assessment of liver perfusion. These data were used to generate a 3D model of the liver. The intra-procedural position of the RFA probe was determined by CT and semi-automatically registered to the 3D model. Size and shape of the simulated ablation zones were compared with those of the thermal ablation zones segmented in contrast-enhanced CT images 1 month after RFA; procedure time was compared with a historical control group. RESULTS: Simulated and segmented ablation zone volumes showed a significant correlation (rho = 0.59, p < 0.0001) and no significant bias (Wilcoxon's Z = 0.68, p = 0.25). Representative measures of ablation zone comparison were as follows: average surface deviation (absolute average error, AAE) with 3.4 +/- 1.7 mm, Dice similarity coefficient 0.62 +/- 0.14, sensitivity 0.70 +/- 0.21, and positive predictive value 0.66 +/- 0. There was a moderate positive correlation between AAE and duration of the ablation (t; r = 0.37, p = 0.008). After adjustments for inter-individual differences in t, liver perfusion, and prior transarterial chemoembolization procedures, t was an independent predictor of AAE (ss = 0.03 mm/min, p = 0.01). Compared with a historical control group, the simulation added 3.5 +/- 1.9 min to the procedure. CONCLUSION: The validated simulation tool showed acceptable speed and accuracy in predicting the size and shape of hepatic RFA ablation zones. Further randomized controlled trials are needed to evaluate to what extent this tool might improve patient outcomes. KEY POINTS: * More reliable, patient-specific intra-procedural estimation of the induced RFA ablation zones in the liver may lead to better planning of the safety margins around tumors. * Dedicated real-time simulation software to predict RFA-induced ablation zones in patients with liver malignancies has shown acceptable agreement with the follow-up results in a first prospective multicenter trial suggesting a randomized controlled clinical trial to evaluate potential outcome benefit for patients

High-resolution contrast enhanced multi-phase hepatic computed tomography data fromaporcine Radio-Frequency Ablation study

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