REAL-TIME TARGET POSITION ESTIMATION USING STEREOSCOPIC KILOVOLTAGE/MEGAVOLTAGE IMAGING AND EXTERNAL RESPIRATORY MONITORING FOR DYNAMIC MULTILEAF COLLIMATOR TRACKING

Purpose: To develop a real-time target position estimation method using stereoscopic kilovoltage (kV)/megavoltage (MV) imaging and external respiratory monitoring, and to investigate the performance of a dynamic multileaf collimator tracking system using this method. Methods and Materials: The real-time three-dimensional internal target position estimation was established by creating a time-varying correlation model that connected the external respiratory signals with the internal target motion measured intermittently using kV/MV imaging. The method was integrated into a dynamic multileaf collimator tracking system. Tracking experiments were performed for 10 thoracic/abdominal traces. A three-dimensional motion platform carrying a gold marker and a separate one-dimensional motion platform were used to reproduce the target and external respiratory motion, respectively. The target positions were detected by kV (1 Hz) and MV (5.2 Hz) imaging, and external respiratory motion was captured by an optical system (30 Hz). The beam-target alignment error was quantified as the positional difference between the target and circular beam center on the MV images acquired during tracking. The correlation model error was quantified by comparing a model estimate and measured target positions. Results: The root-mean-square errors in the beam-target alignment that had ranged from 3.1 to 7.6 mm without tracking were reduced to <1.5 mm with tracking, except during the model building period (6 s). The root-mean-square error in the correlation model was submillimeters in all directions. Conclusion: A novel real-time target position estimation method was developed and integrated into a dynamic multileaf collimator tracking system and demonstrated an average submillimeter geometric accuracy after initializing the internal/external correlation model. The method used hardware tools available on linear accelerators and therefore shows promise for clinical implementation. (C) 2011 Elsevier Inc.