SU-F-J-192: A Quick and Effective Method to Validate Patient's Daily Setup and Geometry Changes Prior to Proton Treatment Delivery Based On Water Equivalent Thickness Projection Imaging (WETPI) for Head Neck Cancer (HNC) Patient

Authors


Abstract

Purpose:

With the implementation of Cone-beam Computed-Tomography (CBCT) in proton treatment, we introduces a quick and effective tool to verify the patient's daily setup and geometry changes based on the Water-Equivalent-Thickness Projection-Image(WETPI) from individual beam angle.

Methods:

A bilateral head neck cancer(HNC) patient previously treated via VMAT was used in this study. The patient received 35 daily CBCT during the whole treatment and there is no significant weight change. The CT numbers of daily CBCTs were corrected by mapping the CT numbers from simulation CT via Deformable Image Registration(DIR). IMPT plan was generated using 4-field IMPT robust optimization (3.5% range and 3mm setup uncertainties) with beam angle 60, 135, 300, 225 degree. WETPI within CTV through all beam directions were calculated. 3%/3mm gamma index(GI) were used to provide a quantitative comparison between initial sim-CT and mapped daily CBCT. To simulate an extreme case where human error is involved, a couch bar was manually inserted in front of beam angle 225 degree of one CBCT. WETPI was compared in this scenario.

Results:

The average of GI passing rate of this patient from different beam angles throughout the treatment course is 91.5 ± 8.6. In the cases with low passing rate, it was found that the difference between shoulder and neck angle as well as the head rest often causes major deviation. This indicates that the most challenge in treating HNC is the setup around neck area. In the extreme case where a couch bar is accidently inserted in the beam line, GI passing rate drops to 52 from 95.

Conclusion:

WETPI and quantitative gamma analysis give clinicians, therapists and physicists a quick feedback of the patient's setup accuracy or geometry changes. The tool could effectively avoid some human errors. Furthermore, this tool could be used potentially as an initial signal to trigger plan adaptation.

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