RFA Guardian: Comprehensive Simulation of Radiofrequency Ablation Treatment of Liver Tumors

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

Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis

Most tumors exhibit increased glucose metabolism to lactate, however, the extent to which glucose-derived metabolic fluxes are used for alternative processes is poorly understood [1, 2]. Using a metabolomics approach with isotope labeling, we found that in some cancer cells a relatively large amount of glycolytic carbon is diverted into serine and glycine metabolism through phosphoglycerate dehydrogenase (PHGDH). An analysis of human cancers showed that PHGDH is recurrently amplified in a genomic region of focal copy number gain most commonly found in melanoma. Decreasing PHGDH expression impaired proliferation in amplified cell lines. Increased expression was also associated with breast cancer subtypes, and ectopic expression of PHGDH in mammary epithelial cells disrupted acinar morphogenesis and induced other phenotypic alterations that may predispose cells to transformation. Our findings show that the diversion of glycolytic flux into a specific alternate pathway can be selected during tumor development and may contribute to the pathogenesis of human cancer.National Institutes of Health (U.S.)National Cancer Institute (U.S.)Smith Family FoundationDamon Runyon Cancer Research FoundationBurroughs Wellcome Fun

Evaluation of cardiac PET-MRI registration methods using a numerical breathing phantom

The thorax structure movements (breathing, heart motion and patient motion) during the positron emission tomography (PET) scanning cause deformations and blurring to PET scans. These artifacts are important reasons, for the mis-registration of PET volumes. In this paper, a breathing simulated PET phantom was constructed by deforming simulated (static) PET volume according to breathing model. The constructed volume was used as a ground truth in the assessment of a non-rigid registration method for cardiac MR and PET volumes. Results showed that the assessed non-rigid registration method was more accurate than a rigid registration method which was used as a reference

RFA Guardian

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.Peer reviewe