Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT:A validation study

Purpose: To evaluate deep-learning based calcium quantification on Chest CT scans compared with manual evaluation, and to enable interpretation in terms of the traditional Agatston score on dedicated Cardiac CT. Methods: Automated calcium quantification was performed using a combination of deep-learning convolution neural networks with a ResNet-architecture for image features and a fully connected neural network for spatial coordinate features. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using coronary probability maps and aortic segmentation. The algorithm was first trained on cardiac-CTs and refined on non-triggered chest-CTs. This study used on 95 patients (cohort 1), who underwent both dedicated calcium scoring and chest-CT acquisitions using the Agatston score as reference standard and 168 patients (cohort 2) who underwent chest-CT only using qualitative expert assessment for external validation. Results from the deep-learning model were compared to Agatston-scores(cardiac-CTs) and manually determined calcium volumes(chest-CTs) and risk classifications. Results: In cohort 1, the Agatston score and AI determined calcium volume shows high correlation with a correlation coefficient of 0.921(p < 0.001) and R-2 of 0.91. According to the Agatston categories, a total of 67(70 %) were correctly classified with a sensitivity of 91 % and specificity of 92 % in detecting presence of coronary calcifications. Manual determined calcium volume on chest-CT showed excellent correlation with the AI volumes with a correlation coefficient of 0.923(p < 0.001) and R-2 of 0.96, no significant difference was found (p = 0.247). According to qualitative risk classifications in cohort 2, 138(82 %) cases were correctly classified with a k-coefficient of 0.74, representing good agreement. All wrongly classified scans (30(18 %)) were attributed to an adjacent category. Conclusion: Artificial intelligence based calcium quantification on chest-CTs shows good correlation compared to reference standards. Fully automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional acquisitions

Lattice Boltzmann Method For Fast Patient-Specific Simulation of Liver Tumor Ablation from CT Images

International audienceRadio-frequency ablation (RFA), the most widely used minimally invasive ablative therapy of liver cancer, is challenged by a lack of patient-specifi c planning. In particular, the presence of blood vessels and time varying thermal di ffusivity makes the prediction of the extent of the ablated tissue diffi cult. This may result in incomplete treatments and increased risk of recurrence. We propose a new model of the physical mechanisms involved in RFA of abdominal tumors based on Lattice Boltzmann Method to predict the extent of ablation given the probe location and the biological parameters. Our method relies on patient images, from which level set representations of liver geometry, tumor shape and vessels are extracted. Then a computational model of heat diff usion, cellular necrosis and blood flow through vessels and liver is solved to estimate the extent of ablated tissue. After quantitative verifi cations against an analytical solution, we apply our framework to 5 patients datasets which include pre- and post-operative CT images, yielding promising correlation between predicted and actual ablation extent (mean point to mesh errors of 8.7 mm). Implemented on graphics processing units, our method may enable RFA planning in clinical settings as it leads to near real-time computation: 1 minute of ablation is simulated in 1.14 minutes,which is almost 60 faster than standard fi nite element method

Challenges to Validate Multi-physics Model of Liver Tumor Radiofrequency Ablation from Pre-clinical Data

International audienceThe planning and interventional guidance of liver tumor ra-diofrequency ablation (RFA) is difficult due to the cooling effect of large vessels and the large variability of tissue parameters. Subject-specific modeling of RFA is challenging as it requires the knowledge of model geometry and hemodynamics as well as the simulation of heat transfer and cell death mechanisms. In this paper, we propose to validate such a model from pre-operative multi-modal images and intra-operative signals (temperature and power) measured by the ablation device itself. In particular , the RFA computation becomes subject-specific after three levels of personalization: anatomical, heat transfer and a novel cellular necro-sis model. We propose an end-to-end pre-clinical validation framework that considers the most comprehensive dataset for model validation. This framework can also be used for parameter estimation and we evaluate its predictive power in order to fully assess the possibility to personalize our model in the future. Such a framework would therefore not require any necrosis information, thus better suited for clinical applications. We evaluated our approach on seven ablations from three healthy pigs. The predictive power of the model was tested: a mean point to mesh error between predicted and actual ablation extent of 3.5 mm was achieved

Comprehensive Pre-Clinical Evaluation of a Multi-physics Model of Liver Tumor Radiofrequency Ablation

International audiencePurpose: 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 multi-modal, pre-and post-operative anatomical and functional images, as well as the interventional monitoring of intra-operative signals: the temperature and delivered power. Results: To exploit this data set, 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 pre-clinical validation framework that considers the available data set

Système et méthode pour la simulation patient-spécifique interactive de thérapie d'ablation par radiofréquence

A method and system for interactive patient-specific simulation of liver tumor ablation is disclosed. A patient-specific anatomical model of the liver and circulatory system of the liver is estimated from 3D medical image data of a patient. A computational domain is generated from the patient-specific anatomical model of the liver. Blood flow in the liver and the circulatory system of the liver is simulated based on the patient-specific anatomical model. Heat diffusion due to ablation is simulated based on a virtual ablation probe position and the simulated blood flow in the liver and the circulatory system of the liver by solving a bio-heat equation for each node on the level-set representation using a Lattice-Boltzmann method (LBM) implementation. Cellular necrosis in the liver is computed based on the simulated heat diffusion. Visualizations of a computed necrosis region and temperature maps of the liver are generated. A user input is interactively received to modify the position of the virtual ablation probe, the heat diffusion and cellular necrosis is re-simulated based on the user input, and the visualizations of the computed necrosis region and the temperature maps are updatedLa présente invention concerne un procédé et un système pour la simulation patient-spécifique interactive de thérapie d’ablation d’une tumeur hépatique. Le modèle anatomique patient-spécifique du système hépatique et circulatoire du foie est estimé à partir de données d’image médicale 3D d’un patient. Un domaine de calcul est généré à partir du modèle anatomique patient-spécifique du foie. La circulation sanguine dans le foie et le système circulatoire du foie est simulée sur la base du modèle anatomique patient-spécifique. La diffusion thermique due à l’ablation est simulée sur la base d’une position de sonde d’ablation virtuelle et la circulation sanguine simulée dans le foie et le système circulatoire du foie par résolution d’une équation biothermique pour chaque nœud sur la représentation définie par niveau au moyen d’une mise en œuvre du procédé de Lattice-Boltzmann (LBM). La nécrose cellulaire dans le foie est calculée sur la base de la diffusion thermique simulée. Des visualisations d’une région de nécrose calculée et des cartes de température du foie sont générées. Une entrée d’utilisateur est reçue de façon interactive pour modifier la position de la sonde d’ablation virtuelle, la diffusion thermique et la nécrose cellulaire sont re-simulées sur la base de l’entrée d’utilisateur, et les visualisations de la région de nécrose calculée et les cartes de température sont mises à jour

System and method for personalized computation of tissue ablation extent based on medical images

A method and system for personalized computation of tissue ablation extent based on medical images of a patient is disclosed. A patient-specific anatomical model of the liver and liver vessels is estimated from medical image data of a patient. Blood flow in the liver and liver vessels is simulated. An ablation simulation is performed that uses a bio-heat model to simulate heat diffusion due to an ablation based on the simulated blood flow and a cellular necrosis model to simulate cellular necrosis in the liver based on the simulated heat diffusion. Personalized tissue parameters of the bio-heat model and the cellular necrosis model are estimated based on observed results of a preliminary ablation procedure. Planning of the ablation procedure is then performed using the personalized bio-heat equation and the cellular necrosis model. The model can be subsequently refined as more ablation observations are obtained