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Sci—Fri AM: Mountain — 06: Optimizing planning target volume in lung radiotherapy using deformable registration
Author(s) -
Hoang P,
Wierzbicki M
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4894946
Subject(s) - margin (machine learning) , image registration , medicine , nuclear medicine , radiation treatment planning , breathing , radiation therapy , cone beam computed tomography , image guided radiation therapy , computed tomography , radiology , computer science , computer vision , image (mathematics) , anatomy , machine learning
A four dimensional computed tomography (4DCT) image is acquired for all radically treated, lung cancer patients to define the internal target volume (ITV), which encompasses tumour motion due to breathing and subclinical disease. Patient set‐up error and anatomical motion that is not due to breathing is addressed through an additional 1 cm margin around the ITV to obtain the planning target volume (PTV). The objective of this retrospective study is to find the minimum PTV margin that provides an acceptable probability of delivering the prescribed dose to the ITV. Acquisition of a kV cone beam computed tomography (CBCT) image at each fraction was used to shift the treatment couch to accurately align the spinal cord and carina. Our method utilized deformable image registration to automatically position the planning ITV on each CBCT. We evaluated the percentage of the ITV surface that fell within various PTVs for 79 fractions across 18 patients. Treatment success was defined as a situation where at least 99% of the ITV is covered by the PTV. Overall, this is to be achieved in at least 90% of the treatment fractions. The current approach with a 1cm PTV margin was successful ∼96% of the time. This analysis revealed that the current margin can be reduced to 0.8cm isotropic or 0.6×0.6×1 cm 3 non‐isotropic, which were successful 92 and 91 percent of the time respectively. Moreover, we have shown that these margins maintain accuracy, despite intrafractional variation, and maximize CBCT image guidance capabilities.