R. Laurent

Case-Based Reasoning adaptation of numerical representations of human organs by interpolation

Case-Based Reasoning (CBR) and interpolation tools can provide solutions to unknown problems by adapting solutions from other problems already solved. We propose a generic approach using an interpolation tool during the CBR-adaptation phase. The application EquiVox, which attempts to design three dimensional representations of human organs according to external measurements, was modelled. It follows the CBR-cycle with its adaptation tool based on Artificial Neural Networks and its performances are evaluated and discussed. The results show that this adaptation tool meets the requirements of radiation protection experts who use such prototypes and also what the limits are of such tools in CBR-adaptation. When adaptations are guided by experience grained through trial and error by experts, interpolation tools become well-suited methods for automatically and quickly providing adaptation strategies and knowledge through training phases.

Development of a new CBR-based platform for human contamination emergency situations.

In the case of a radiological emergency situation, involving accidental human exposure, it is necessary to establish as soon as possible a dosimetry evaluation. In most cases, this evaluation is based on numerical representations and models of the victims. Unfortunately, personalised and realistic human representations are often unavailable for the exposed subjects. Hence, existing models like the Reference Man representative of the average male individual are used. However, the accuracy of the treatment depends on the similarity of the phantom to the victim. The EquiVox platform (Research of Equivalent Voxel phantom) developed in this work uses the case-based reasoning principles to retrieve, from a set of existing phantoms, the most adapted one to represent the victim. This paper introduces the EquiVox platform and gives the example of in vivo lung monitoring optimisation to prove its efficiency in choosing the right model. It also presents the artificial neural network tools being developed to adapt the model to the victim.

Respiratory lung motion using an artificial neural network

One of the possibilities to enhance the accuracy of lung radiotherapy is to improve the understanding of the individual lung motion of each patient. Indeed, using this knowledge, it becomes possible to follow the evolution of the clinical target volume defined by a set of points according to the lung breathing phase. This paper presents an innovative method to simulate the positions of points in a person’s lungs for each breathing phase. Our method, based on an artificial neural network (ANN), allowed us to learn the lung motion of five different patients and then to simulate it accurately for three other patients using only beginning and end points. The training set for our ANN consisted of more than 1,100 points spread over ten breathing phases from the five patients on a specific area of the lungs. The points were defined by a medical expert. The first results are very promising: we obtain an average accuracy of 1.5xa0mm while the spatial resolution is 1xa0xa0×xa0xa01xa0xa0×xa0xa02.5xa0mm3. The accuracy of the method will be improved even more with additional data and providing complete lung coverage.

Adapting numerical representations of lung contours using Case-Based Reasoning and Artificial Neural Networks

In case of a radiological emergency situation involving accidental human exposure, a dosimetry evaluation must be established as soon as possible. In most cases, this evaluation is based on numerical representations and models of subjects. Unfortunately, personalised and realistic human representations are often unavailable for the exposed subjects. However, accuracy of treatment depends on the similarity of the phantom to the subject. The EquiVox platform (Research of Equivalent Voxel phantom) developed in this study uses Case-Based Reasoning principles to retrieve and adapt, from among a set of existing phantoms, the one to represent the subject. This paper introduces the EquiVox platform and Artificial Neural Networks developed to interpolate the subject’s 3D lung contours. The results obtained for the choice and construction of the contours are presented and discussed.

EquiVox: an example of adaptation using an artificial neural network on a case-based reasoning platform

In case of a radiological emergency situation involving accidental human exposure, a dosimetry evaluation must be established as soon as possible. In most cases, this evaluation is based on numerical representations and models of victims. Unfortunately, personalised and realistic human representations are often unavailable for the exposed subjects. However, accuracy of treatment depends on the similarity of the phantom to the victim. The EquiVox platform (Research of Equivalent Voxel phantom) developed in this study uses Case-Based Reasoning (CBR) principles to retrieve and adapt, from among a set of existing phantoms, the one to represent the victim. This paper introduces the EquiVox platform and the Artificial Neural Network (ANN) developed to interpolate the victims 3D lung contours. The results obtained for the choice and construction of the contours are presented and discussed.

Development of a 4D numerical chest phantom with customizable breathing

Respiratory movement information is useful for radiation therapy, and is generally obtained using 4D scanners (4DCT). In the interest of patient safety, reducing the use of 4DCT could be a significant step in reducing radiation exposure, the effects of which are not well documented. The authors propose a customized 4D numerical phantom representing the organ contours. Firstly, breathing movement can be simulated and customized according to the patients anthroporadiametric data. Using learning sets constituted by 4D scanners, artificial neural networks can be trained to interpolate the lung contours corresponding to an unknown patient, and then to simulate its respiration. Lung movement during the breathing cycle is modeled by predicting the lung contours at any respiratory phases. The interpolation is validated comparing the obtained lung contours with 4DCT via Dice coefficient. Secondly, a preliminary study of cardiac and œsophageal motion is also presented to demonstrate the flexibility of this approach. The application may simulate the position and volume of the lungs, the œsophagus and the heart at every phase of the respiratory cycle with a good accuracy: the validation of the lung modeling gives a Dice index greater than 0.93 with 4DCT over a breath cycle.

DATA PROCESSING USING ARTIFICIAL NEURAL NETWORKS TO IMPROVE THE SIMULATION OF LUNG MOTION

To optimize the delivery in lung radiation therapy, a better understanding of the tumor motion is required, on one hand, to have a better tumor-targeting efficiency, and on the other hand to avoid as much as possible normal tissues. The four-dimensional computed tomography (4D-CT) allows to quantify tumor motion, but due to artifacts, it introduces biases and errors in tumor localization. Despite 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 of 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 five patients and defined over 8 or 10 phases, we obtained 5800 points from 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.