Quantification of Respiratory Motion in Positron Emission Tomography for Precise Radiation Treatment of Lung Cancer

Abstract

A well-established method for treating lung cancer is curative-intent radiation therapy (RT). The most significant challenge for RT is to accurately target the lesion volume while avoiding the irradiation of surrounding healthy tissue. Currently at the Juravinski Cancer Centre (JCC), treatment plans for lung cancer patients are completed using fluorodeoxyglucose positron emission tomography (FDG-PET) and four-dimensional computed tomography (4DCT) images. There is no clear protocol, however, to compensate for respiratory motion in PET images and it is not known how lesion volumes generated from PET reflect the true volume. This project evaluated methods to optimize the use of PET images in the radiation treatment planning workflow and quantify the effects of respiratory motion. First, a 4D XCAT digital phantom was used to quantify respiratory motion and its effects on lesion displacement. A CTN physical phantom and 3D-printed irregularly shaped lesion were imaged to determine the accuracy of the PET EDGE automated segmentation algorithm (ASA). Lastly, rigid and deformable image registration techniques were used to propagate the diagnostic PET scan of the irregular lesion to the 4D planning CT. PET EDGE was used to generate target volumes which were then compared to internal target volumes (ITVs) generated from manual contouring of the 4DCT image alone. We found that lesion displacement due to respiratory motion can be adequately modeled using a moving platform set to oscillate 1 cm and 2 cm for normal and deep breathing, respectively. Optimal target delineation was found when diagnostic PET was propagated to the planning CT using rigid image registration for lesions that experienced 1 cm of oscillatory motion during imaging. In contrast, PET EDGE would overestimate volumes in static cases and underestimate volumes in instances of 2 cm dynamic motion meant to simulate deep breathing.