Chloé Audigier1,2, Tommaso Mansi3, Hervé Delingette4, Saikiran Rapaka3, Tiziano Passerini3, Viorel Mihalef3, Marie-Pierre Jolly3, Raoul Pop5, Michele Diana5, Luc Soler5,6, Ali Kamen3, Dorin Comaniciu3, Nicholas Ayache4. 1. Université Côte d'Azur and Inria Sophia-Antipolis Méditerranée Asclepios team, Inria Sophia Antipolis, France. chloe.audigier@inria.fr. 2. Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, USA. chloe.audigier@inria.fr. 3. Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, USA. 4. Université Côte d'Azur and Inria Sophia-Antipolis Méditerranée Asclepios team, Inria Sophia Antipolis, France. 5. IHU - Institut de Chirugie Guidée Par L'Image, Strasbourg, France. 6. IRCAD - Institut de Recherche Contre Les Cancers de L'Appareil Digestif, Strasbourg, France.
Abstract
PURPOSE: We aim at developing a framework for the validation of a subject-specific multi-physics model of liver tumor radiofrequency ablation (RFA). METHODS: The RFA computation becomes subject specific after several levels of personalization: geometrical and biophysical (hemodynamics, heat transfer and an extended cellular necrosis model). We present a comprehensive experimental setup combining multimodal, pre- and postoperative anatomical and functional images, as well as the interventional monitoring of intra-operative signals: the temperature and delivered power. RESULTS: To exploit this dataset, an efficient processing pipeline is introduced, which copes with image noise, variable resolution and anisotropy. The validation study includes twelve ablations from five healthy pig livers: a mean point-to-mesh error between predicted and actual ablation extent of 5.3 ± 3.6 mm is achieved. CONCLUSION: This enables an end-to-end preclinical validation framework that considers the available dataset.
PURPOSE: We aim at developing a framework for the validation of a subject-specific multi-physics model of liver tumor radiofrequency ablation (RFA). METHODS: The RFA computation becomes subject specific after several levels of personalization: geometrical and biophysical (hemodynamics, heat transfer and an extended cellular necrosis model). We present a comprehensive experimental setup combining multimodal, pre- and postoperative anatomical and functional images, as well as the interventional monitoring of intra-operative signals: the temperature and delivered power. RESULTS: To exploit this dataset, an efficient processing pipeline is introduced, which copes with image noise, variable resolution and anisotropy. The validation study includes twelve ablations from five healthy pig livers: a mean point-to-mesh error between predicted and actual ablation extent of 5.3 ± 3.6 mm is achieved. CONCLUSION: This enables an end-to-end preclinical validation framework that considers the available dataset.
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