Data Processing using Artificial Neural Networks to Improve the Simulation of Lung Motion

International audienceTo optimize the delivery in lung radiation therapy, a better understanding of the tumor motion is required. On the one hand to have a better tumor-targeting efficiency, and on the other hand to avoid as much as possible normal tissues. The 4D-CT allows to quantify tumor motion, but due to artifacts it introduces biases and errors in tumor localization. Despite of this disadvantage, we propose a method to simulate lung motion based on data provided by the 4D-CT for several patients. To reduce uncertainties introduced by the 4D-CT scan, we conveniently treated data using artificial neural networks. More precisely, our approach consists in a data augmentation technique. The data resulting from this processing step are then used to build a training set for another artificial neural network that learns the lung motion. To improve the learning accuracy, we have studied the number of phases required to precisely describe the displacement of each point. Thus, from 1118 points scattered across 5 patients and defined over 8 or 10 phases, we obtained 5800 points of 50 phases. After training, the network is used to compute the positions of 40 points from five other patients on 10 phases. These points allow to quantify the prediction performance. In comparison with the original data, the ones issued from our treatment process provide a significant increase of the prediction accuracy: an average improvement of 16% can be observed. The motion computed for several points by the neural network that has learnt the lung one exhibits an hysteresis near the one given by the 4D-CT, with an error smaller than 1 mm in the cranio-caudal axis

The role of Monte Carlo numeric simulations in fields associated to the ionising radiations physics

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Enhancement of breathing simulation using individual lobe segmentation

International audienceTo tackle thorax movement from CT images, we have developed a platform to simulate a customized breathing cycle, where the pulmonary movement has been considered only at the rough border of the whole lung by artificial neural networks (ANN). The goal of this work is to include additional information of the lung lobe. Thus, more ANN will be used and future simulation will be able to take into consideration the impact of tumor on lobe movement. We present a new automatic segmentation algorithm that enables the extraction of lobar contour data using sliding mask and direction estimation. These improvements enhance the overall system performance in which higher precision and more accurate treatments can be expected

Development of a new optical method for gel dosimeters based on the analysis of the scattering light

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A morphing technique applied to lung motion in radiotherapy: preliminary results

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The response of a semiconductor spectrometer to photons - comparison between experimental data and MC calculations

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Theoretical determination of the dose perturbation caused by a hip prosthesis during a pelvic irradiation

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Development of a new optical technique for dosimetric gels based on the analysis of the scattering light

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Monte Carlo calculations of the impact of a hip prosthesis on the dose distribution. Development of CT-based phantoms

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Characterisation of two new radiochromic gel dosimeters TruView™ and ClearView™ in combination with the vista™ optical CT scanner: A feasibility study

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