A Morphing Technique Applied to Lung Motions in Radiotherapy: Preliminary Results

Abstract

Organ motion leads to dosimetric uncertainties during a patient’s treatment. Much work has been done to quantify the dosimetric effects of lung movement during radiation treatment. There is a particular need for a good description and prediction of organ motion. To describe lung motion more precisely, we have examined the possibility of using a computer technique: a morphing algorithm. Morphing is an iterative method which consists of blending one image into another image. To evaluate the use of morphing, Four Dimensions Computed Tomography (4DCT) acquisition of a patient was performed. The lungs were automatically segmented for different phases, and morphing was performed using the end-inspiration and the end-expiration phase scans only. Intermediate morphing files were compared with 4DCT intermediate images. The results showed good agreement between morphing images and 4DCT images: fewer than 2 % of the 512 by 256 voxels were wrongly classified as belonging/not belonging to a lung section. This paper presents preliminary results, and our morphing algorithm needs improvement. We can infer that morphing offers considerable advantages in terms of radiation protection of the patient during the diagnosis phase, handling of artifacts, definition of organ contours and description of organ motion.

Iterative volume morphing and learning for mobile tumor based on 4DCT

“…To track the tumor positions in real time, various technologies have been developed (Laurent et al 2010, Fassi et al 2015, Leonardi et al 2015. However, these previous tracking approaches are not feasible for use with three-dimensional (3D) tumor shapes in real time, a potentially important factor for accurate dose delivery.…”