Quantification of the margin required for treating intraprostatic lesions.

Advances in magnetic resonance imaging (MRI) sequences allow physicians to define the dominant intraprostatic lesion (IPL) in prostate radiation therapy treat-ments allowing for dose escalation and potentially increased tumor control. This work quantifies the margin required around the MRI-defined IPL accounting for both prostate motion and deformation. Ten patients treated with a simultaneous integrated intraprostatic boost (SIIB) were retrospectively selected and replanned with incremental 1 mm margins from 0-5 mm around the IPL to determine if there were any significant differences in dosimetric parameters. Sensitivity analysis was then performed accounting for random and systematic uncertainties in both prostate motion and deformation to ensure adequate dose was delivered to the IPL. Prostate deformation was assessed using daily CBCT imaging and implanted fiducial markers. The average IPL volume without margin was 2.3% of the PTV volume and increased to 11.8% with a 5 mm margin. Despite these changes in vol-ume, the only statistically significant dosimetric difference was found for the PTV maximum dose, which increased with increasing margin. The sensitivity analysis demonstrated that a 3.0 mm margin ensures > 95% IPL coverage accounting for both motion and deformation. We found that a margin of 3.0 mm around the MRI defined IPL is sufficient to account for random and systematic errors in IPL posi-tion for the majority of cases.

Use of TrueBeam developer mode for imaging QA.

The purpose of this study was to automate regular Imaging QA procedures to become more efficient and accurate. Daily and monthly imaging QA for SRS and SBRT protocols were fully automated on a Varian linac. A three-step paradigm where the data are automatically acquired, processed, and analyzed was defined. XML scripts were written and used in developer mode in a TrueBeam linac to automatically acquire data. MATLAB R013B was used to develop an interface that could allow the data to be processed and analyzed. Hardware was developed that allowed the localization of several phantoms simultaneously on the couch. 14 KV CBCTs from the Emma phantom were obtained using a TrueBeam onboard imager as example of data acquisition and analysis. The images were acquired during two months. Artifacts were artificially introduced in the images during the reconstruction process using iTool reconstructor. Support vector machine algorithms to automatically identify each artifact were written using the Machine Learning MATLAB R2011 Toolbox. A daily imaging QA test could be performed by an experienced medical physicist in 14.3 ± 2.4 min. The same test, if automated using our paradigm, could be performed in 4.2 ± 0.7 min. In the same manner, a monthly imaging QA could be performed by a physicist in 70.7 ± 8.0 min and, if fully automated, in 21.8 ± 0.6 min. Additionally, quantitative data analysis could be automatically performed by Machine Learning Algorithms that could remove the subjectivity of data interpretation in the QA process. For instance, support vector machine algorithms could correctly identify beam hardening, rings and scatter artifacts. Traditional metrics, as well as metrics that describe texture, are needed for the classification. Modern linear accelerators are equipped with advanced 2D and 3D imaging capabilities that are used for patient alignment, substantially improving IGRT treatment accuracy. However, this extra complexity exponentially increases the number of QA tests needed. Using the new paradigm described above, not only the bare minimum ­ but also best practice ­ QA programs could be implemented with the same manpower.