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Feasibility of Monitoring Pancreatic GTV Motion During Breath-Hold Treatments - a Simulation Study

L Zhu*, A Jeung , H Mostafavi , P Jordan , J van Heteren , Varian Medical Systems, Palo Alto, CA


(Tuesday, 7/16/2019) 10:00 AM - 10:30 AM

Room: Exhibit Hall | Forum 9

Purpose: To investigate the feasibility of monitoring pancreatic GTV position during VMAT breath-hold treatments using a stereo source-detector pair and digital tomosynthesis (DTS).

Methods: A simulation was created of a rotating gantry configuration, comprising an orthogonal stereo kV source-detector pair placed symmetrically about the MV treatment head. Realistic x-ray projections containing kV and other noise sources were generated from ten clinical abdominal 4D CT images using the iTools research software (Varian Medical Systems, Palo Alto CA). DTS images with various arc length and rotation start angles were then reconstructed from the projections using the FDK algorithm. DTS images were simulated at every 45o as gantry rotating from 0o to 360o. Given an initial GTV contour retrieved from planning CT, a machine-learning-based tracking algorithm was applied to estimate the center-of-mass (COM) of the GTV in simulated DTS images. A state-of-the-art deformable registration algorithm was used to propagate the initial GTV contour to different simulated breathing phases in order to derive reference GTV positions. Experiments were conducted to investigate the tracking performance compared to reference under different arc lengths.

Results: The absolute COM errors between the machine-learning-based tracking algorithm and the deformable registration reference were 1.7±1mm, 1.6±1mm, and 1.6±1mm, for DTS images with arc length 30o, 60o, 360o (CBCT), respectively.

Conclusion: This study shows that using DTS reconstructed from a stereo source-detector pair may provide for high quality intra-fraction GTV localization with temporal resolution still being adequate for a breath-hold scenario. Future work will include investigating more sophisticated reconstruction and tracking algorithms.


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