Development of a 4D numerical chest phantom with customizable breathing. Issue 6 (June 2016)

• Record Type: Journal Article
• Authors: Leni, Pierre-Emmanuel; Laurent, Rémy; Salomon, Michel; Gschwind, Régine; Makovicka, Libor; Henriet, Julien
• Is Part Of: Physica medica. Volume 32:Issue 6(2016)
• Page Start: 795
• Page End: 800
• Publication Date: 2016-06
• Subjects: Artificial neural network -- Radiation physics -- Phantoms -- Breath simulation; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2016.05.004
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Customized simulation of the lung contour movements. The simulation is validated by comparing results with 4D scanner acquisitions. Flexible (extension to other organs is presented). Easy to use (no parameter requires manual tuning). Reduction of the doses received by patients during 4D scanner acquisitions. Abstract: 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 patient's 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.