We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Characterization of free breathing patterns with 5D lung motion model.
- Authors
Tianyu Zhao; Wei Lu; Deshan Yang; Mutic, Sasa; Noel, Camille E.; Parikh, Parag J.; Bradley, Jeffrey D.; Low, Daniel A.
- Abstract
Purpose: To determine the quiet respiration breathing motion model parameters for lung cancer and nonlung cancer patients. Methods: 49 free breathing patient 4DCT image datasets (25 scans, cine mode) were collected with simultaneous quantitative spirometry. A cross-correlation registration technique was employed to track the lung tissue motion between scans. The registration results were applied to a lung motion model: Xvector =Xvector 0+αvector v+βvector f, where Xvector is the position of a piece of tissue located at reference position Xvector 0 during a reference breathing phase (zero tidal volume v, zero airflow f). αvector is a parameter that characterizes the motion due to air filling (motion as a function of tidal volume v) and βvector is the parameter that accounts for the motion due to the imbalance of dynamical stress distributions during inspiration and exhalation that causes lung motion hysteresis (motion as a function of airflow f). The parameters αvector and βvector together provide a quantitative characterization of breathing motion that inherently includes the complex hysteresis interplay. The αvector and βvector distributions were examined for each patient to determine overall general patterns and interpatient pattern variations. Results: For 44 patients, the greatest values of |αvector | were observed in the inferior and posterior lungs. For the rest of the patients, |αvector | reached its maximum in the anterior lung in three patients and the lateral lung in two patients. The hysteresis motion βvector had greater variability, but for the majority of patients, |βvector | was largest in the lateral lungs. Conclusions: This is the first report of the three-dimensional breathing motion model parameters for a large cohort of patients. The model has the potential for noninvasively predicting lung motion. The majority of patients exhibited similar |αvector | maps and the |βvector | maps showed greater interpatient variability. The motion parameter interpatient variability will inform our need for custom radiation therapy motion models. The utility of this model depends on the parameter stability over time, which is still under investigation.
- Publication
Medical Physics, 2009, Vol 36, Issue 11, p5183
- ISSN
0094-2405
- Publication type
Academic Journal
- DOI
10.1118/1.3246348