SU‐E‐J‐136: Evaluation of a Non‐Invasive Method on Lung Tumor Tracking

T. Zhao, B. White, D. Low

Research output: Contribution to journalArticlepeer-review


Purpose: to develop a non‐invasive method to track lung motion in free‐breathing patients. Methods: A free‐breathing breathing model has been developed to use tidal volume and air flow rate as surrogates for lung trajectories. In this study, 4D CT data sets were acquired during simulation and were reconstructed into 10 phases. Total lung capacities were calculated from the reconstructed images. Continuous signals from the abdominal pneumatic belt were correlated to the volumes and were therefore converted into a curve of tidal volumes. Air flow rate were calculated as the first order derivative of the tidal volume curve. Lung trajectories in the 10 reconstructed images were obtained using B‐Spline registration. Parameters of the free‐breathing lung motion model were fit from the tidal volumes, airflow rates and lung trajectories using the simulation data. Patients were rescanned every week during the treatment. Prediction of lung trajectories from the model were given and compared to the actual positions in BEV. Results: Trajectories of lung were predicted with residual error of 1.49mm at 95th percentile of all tracked points. Tracking was stable and reproducible over two weeks. Conclusion: Non‐invasive tumor tracking based on a free‐breathing lung motion model is feasible and stable over weeks.

Original languageEnglish
Pages (from-to)3683-3684
Number of pages2
JournalMedical physics
Issue number6
StatePublished - Jun 2012


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