21 March 2014 Respiratory motion variations from skin surface on lung cancer patients from 4D CT data
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Abstract
In radiation therapy of thorax and abdomen regions, knowing how respiratory motion modifies tumor position and trajectory is crucial for accurate dose delivery to tumors while avoiding healthy tissue and organs at risk. Three types of variations are studied: motion amplitudes measured from the patient's skin surface and internal tumor trajectory, internal/external correlations and tumor trajectory baseline shift. Four male patients with lung cancer with three repeated 4D computed tomography (4DCT) scans, taken on different treatment days, were studied. Surfaces were extracted from 4DCT scans by segmentation. Motion over specific regions of interest was analyzed with respect to the motion of the tumor center of mass and correlation coefficient was computed. Tumor baseline shifts were analyzed after rigid registration based on vertebrae and surface registration. External amplitude variations were observed between fractions. Correlation coefficients of internal trajectories and external distances are greater than 0.6 in the abdomen. This correlation was observable and significant for all patients showing that the external motion is a good surrogate for internal movement on an intra-fraction basis. However for the inter-fraction case, external amplitude variations were observed between fractions and no correlation was found with the internal amplitude variations. Moreover, baseline shifts after surface registration were greater than those after vertebrae registration and the mean distance between surfaces after registration was not correlated to the magnitude of the baseline shift. These two observations show that, with the current representation of the external surface, inter-fraction variations are not detectable on the surface.
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Nicolas Gallego-Ortiz, Jonathan Orban de Xivry, Antonin Descampe, Samuel Goossens, Xavier Geets, Guillaume Janssens, Benoit Macq, "Respiratory motion variations from skin surface on lung cancer patients from 4D CT data", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90342Y (21 March 2014); doi: 10.1117/12.2043477; https://doi.org/10.1117/12.2043477
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