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Accurate understanding and modeling of respiration-induced uncertainties is essential in image-guided radiotherapy. Explicit modeling of the overall lung motion and interaction among different organs promises to be a useful approach. Recently, preliminary studies on 3D fluoroscopic treatment imaging and tumor localization based on principal component analysis motion models and cost function optimization have shown encouraging results. However, the performance of this technique for varying breathing parameters and under realistic conditions remains unclear and thus warrants further investigation. In this work, we present a systematic evaluation of a 3D fluoroscopic image generation algorithm via two different approaches. In the first approach, the model's accuracy is tested for changing parameters for sinusoidal breathing. These parameters include changing respiratory motion amplitude, period and baseline shift. The effects of setup error, imaging noise and different tumor sizes are also examined. In the second approach, we test the model for anthropomorphic images obtained from a modified XCAT phantom. This set of experiments is important as all the underlying breathing parameters are simultaneously tested, as in realistic clinical conditions. Based on our simulation results for more than 250 s of breathing data for eight different lung patients, the overall tumor localization accuracies of the model in left-right, anterior-posterior and superior-inferior directions are 0.1 ± 0.1, 0.5 ± 0.5 and 0.8 ± 0.8 mm, respectively. 3D tumor centroid localization accuracy is 1.0 ± 0.9 mm.

Evaluation of 3D fluoroscopic image generation from a single planar treatment image on patient data with a modified XCAT phantom