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IMRT QA Using R&V Data, Treatment Records, and a Second Treatment Planning System

J Ohrt

J Ohrt*, P Balter, L Court, M Gillin, UT MD Anderson Cancer Center, Houston, TX

SU-C-213AB-5 Sunday 1:30:00 PM - 2:15:00 PM Room: 213AB

Traditional patient-specific IMRT QA measurements are labor intensive and consume machine time. Calculation-based IMRT QA methods typically are not comprehensive. We have developed a comprehensive calculation-based IMRT QA method to detect uncertainties introduced by the initial dose calculation, the data transfer through the R&V system, and aspects of the physical delivery.

We recomputed the treatment plans in the patient geometry for 48 cases using data from the R&V, and from the delivery unit to calculate the 'as-transferred' and 'as-delivered' doses respectively. These data are sent to the original TPS to verify transfer and delivery or a second TPS to verify the original calculation. For each dataset we examined the dose computed from the R&V record (RV), from the delivery records (Tx), and dose computed with a second verification TPS (vTPS). Each was compared to the clinical dose distribution using 3D gamma analysis and by comparison of mean dose to target volumes. 24 plans were also compared against measurement-based QA.

The average percentage of voxels with passing gamma values for 3%-3mm, 2%-2mm, and 1%-1mm criteria for the RV plan were 100.0 (s=0.0), 100.0 (s=0.0), and 100.0 (s=0.1); for the Tx plan were 100.0 (s=0.1), 99.9 (s=0.6), and 96.0 (s=4.6); and for the vTPS plan were 99.3 (s=0.6), 97.2 (s=1.5), and 79.0 (s=8.6). The average ratio of the mean dose for all target volumes in the RV, Tx, and vTPS plans to the clinical value was 0.999 (s=0.001), 1.008 (s=0.006), and 0.991 (s=0.009). The average calculation-to-measurement ratios were 0.997 (s=0.014), 0.997 (s=0.014), 1.004 (s=0.015), and 0.991 (s=0.016) for clinical, RV, TR, and vTPS plans.

Together with mechanical and dosimetric QA of the treatment unit, our methods for calculative-based IMRT QA promise to minimize the need for patient-specific QA measurements by indentifying outliers in need of further review.

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