Program Information
A Fast Yet Sensitive EPID-Based Real-Time Treatment Verification System
M Ahmad1*, H Nourzadeh1 , B Neal1 , W Watkins4 , J Siebers1 , (1) University of Virginia Health System, Charlottesville, Virginia
Presentations
MO-FG-202-1 (Monday, August 1, 2016) 4:30 PM - 6:00 PM Room: 202
Purpose: To create a real-time EPID-based treatment verification system which robustly detects treatment delivery and patient attenuation variations.
Methods: Treatment plan DICOM files sent to the record-and-verify system are captured and utilized to predict EPID images for each planned control point using a modified GPU-based digitally reconstructed radiograph algorithm which accounts for the patient attenuation, source energy fluence, source size effects, and MLC attenuation. The DICOM and predicted images are utilized by our C++ treatment verification software which compares EPID acquired 1024x768 resolution frames acquired at ~8.5hz from Varian Truebeamâ„¢ system. To maximize detection sensitivity, image comparisons determine (1) if radiation exists outside of the desired treatment field; (2) if radiation is lacking inside the treatment field; (3) if translations, rotations, and magnifications of the image are within tolerance. Acquisition was tested with known test fields and prior patient fields. Error detection was tested in real-time and utilizing images acquired during treatment with another system.
Results: The computational time of the prediction algorithms, for a patient plan with 350 control points and 60x60x42cm^3 CT volume, is 2-3minutes on CPU and <27 seconds on GPU for 1024x768 images. The verification software requires a maximum of ~9ms and ~19ms for 512x384 and 1024x768 resolution images, respectively, to perform image analysis and dosimetric validations. Typical variations in geometric parameters between reference and the measured images are 0.32°for gantry rotation, 1.006 for scaling factor, and 0.67mm for translation. For excess out-of-field /missing in-field fluence, with masks extending 1mm (at isocenter) from the detected aperture edge, the average total in-field area missing EPID fluence was 1.5mm2 the out-of-field excess EPID fluence was 8mm^2, both below error tolerances.
Conclusion:A real-time verification software, with EPID images prediction algorithm, was developed. The system is capable of performing verifications between frames acquisitions and identifying source(s) of any out-of-tolerance variations.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by Varian Medical Systems.
Contact Email: