A constrained linear regression optimization algorithm for diaphragm motion tracking with cone beam CT projections.

PURPOSE: We presented a feasibility study to extract the diaphragm motion from the inferior contrast cone beam computed tomography (CBCT) projection images using a constrained linear regression optimization algorithm.

METHODS: The shape of the diaphragm was fitted by a parabolic function which was initialized by five manually placed points on the diaphragm contour of a pre-selected projection. A constrained linear regression model by exploiting the spatial, algebraic, and temporal constraints of the diaphragm, approximated by a parabola, was employed to estimate the parameters. The algorithm was assessed by a fluoroscopic movie acquired at anterior-posterior (AP) fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients using the Varian 21iX Clinac. The automatic tracing by the proposed algorithm and manual tracking were compared in both space and frequency domains for the algorithm evaluations.

RESULTS: The error between the results estimated by the proposed algorithm and those by manual tracking for the AP fluoroscopic movie was 0.54 mm with standard deviation (SD) of 0.45 mm. For the detected projections the average error was 0.79 mm with SD of 0.64 mm for all six enrolled patients and the maximum deviation was 2.5 mm. The mean sub-millimeter accuracy outcome exhibits the feasibility of the proposed constrained linear regression approach to track the diaphragm motion on rotational fluoroscopic images.

CONCLUSION: The new algorithm will provide a potential solution to rendering diaphragm motion and possibly aiding the tumor target tracking in radiation therapy of thoracic/abdominal cancer patients.