Search

Validate that a two-dimensional (2D) ionization chamber array (ICA) combined with a double-wedge plate (DWP) can track changes in electron beam energy well within 2.0 mms as recommended by TG-142 for monthly quality assurance (QA). Electron beam profiles of 4-22 MeV were measured for a 25 × 25 cm2 cone using an ICA with a DWP placed on top of it along one diagonal axis. The relationship between the full width half maximum (FWHM) field size created by DWP energy degradation across the field and the depth of 50% dose in water (R50 ) is calibrated for a given ICA/DWP combination in beams of know energies (R50 values). Once this relationship is established, the ICA/DWP system will report the R50 FWHM directly. We calibrated the ICA/DWP on a linear accelerator with energies of 6, 9, 12, 16, 20, and 22 MeV. The R50 FWHM values of these beams and eight other beams with different R50 values were measured and compared with the R50 measured in water, that is, R50 Water. Resolving changes of R50 up to 0.2 cm with ICA/DWP was tested by adding solid-water to shift the energy and was verified with R50 Water measurements. To check the long-term reproducibility of ICA/DWP we measured R50 FWHM on a monthly basis for a period of 3 yr. We proposed a universal calibration procedure considering the off-axis corrections and compared calibrations and measurements on three types of linacs (Varian TrueBeam, Varian C-series, and Elekta) with different nominal energies and R50 values. For all 38 beams on same type of linac with R50 values over a range of 2-8.8 cm, the R50 FWHM reported by the ICA/DWP system agreed with that measured in water within 0.01 ± 0.03 cm (mean ± 1σ) and maximum discrepancy of 0.07 cm. Long-term reproducibility results show the ICA/DWP system to be within 0.04 cm of their baseline over 3 yr. With the universal calibration the maximum discrepancy between R50 FWHM and R50 Water for different types of linac reduced from 0.25 to 0.06 cm. Comparison of R50 FWHM values and R50 Water values and long-term reproducibility of R50 FWHM values indicates that the ICA/DWP can be used for monitoring of electron beam energy constancy well within TG-142 recommended tolerance.

Aim:To investigate the impact of intra-fractional motion on dose distribution in patients treated with intensity-modulated radiotherapy (IMRT) for lung cancer.Materials and methods:Twenty patients who had undergone IMRT for non-small cell lung cancer were selected for this retrospective study. For each patient, a four-dimensional computed tomography (CT) image set was acquired and clinical treatment plans were developed using the average CT. Dose distributions were then recalculated for each of the 10 phases of respiratory cycle and combined using deformable image registration to produce cumulative dose distributions that were compared with the clinical treatment plans.Results:Intra-fractional motion reduced planning target volume (PTV) coverage in all patients. The median reduction of PTV covered by the prescription isodose was 3·4%; D98 was reduced by 3·1 Gy. Changes in the mean lung dose were within ±0·7 Gy. V20 for the lung increased in most patients; the median increase was 1·6%. The dose to the spinal cord was unaffected by intra-fractional motion. The dose to the heart was slightly reduced in most patients. The median reduction in the mean heart dose was 0·22 Gy, and V30 was reduced by 2·5%. The maximum dose to the oesophagus was also reduced in most patients, by 0·74 Gy, whereas V50 did not change significantly. The median number of points in which dose differences exceeded 3%/3 mm was 6·2%.Findings:Intra-fractional anatomical changes reduce PTV coverage compared to the coverage predicted by clinical treatment planning systems that use the average CT for dose calculation. Doses to organs at risk were mostly over-predicted.

Purpose Establish and compare two metrics for monitoring beam energy changes in the Halcyon platform and evaluate the accuracy of these metrics across multiple Halcyon linacs. Method The first energy metric is derived from the diagonal normalized flatness (FDN ), which is defined as the ratio of the average measurements at a fixed off-axis equal distance along the open profiles in two diagonals to the measurement at the central axis with an ionization chamber array (ICA). The second energy metric comes from the area ratio (AR) of the quad wedge (QW) profiles measured with the QW on the top of the ICA. Beam energy is changed by adjusting the magnetron current in a non-clinical Halcyon. With D10cm measured in water at each beam energy, the relationships between FDN or AR energy metrics to D10cm in water is established with linear regression across six energy settings. The coefficients from these regressions allow D10cm (FDN ) calculation from FDN using open profiles and D10cm (QW) calculation from AR using QW profiles. Results Five Halcyon linacs from five institutions were used to evaluate the accuracy of the D10cm (FDN ) and the D10cm (QW) energy metrics by comparing to the D10cm values computed from the treatment planning system (TPS) and D10cm measured in water. For the five linacs, the D10cm (FDN ) reported by the ICA based on FDN from open profiles agreed with that calculated by TPS within -0.29 ± 0.23% and 0.61% maximum discrepancy; the D10cm (QW) reported by the QW profiles agreed with that calculated by TPS within -0.82 ± 1.27% and -2.43% maximum discrepancy. Conclusion The FDN -based energy metric D10cm (FDN ) can be used for acceptance testing of beam energy, and also for the verification of energy in periodic quality assurance (QA) processes.

We tested whether an ionization chamber array (ICA) and a one‐dimensional water scanner (1DS) could be used instead of a three‐dimensional water scanning system (3DWS) for acceptance testing and commissioning verification of the Varian Halcyon–Eclipse Treatment Planning System (TPS). The Halcyon linear accelerator has a single 6‐MV flattening‐filter‐free beam and a nonadjustable beam model for the TPS. Beam data were measured with a 1DS, ICA, ionization chambers, and electrometer. Acceptance testing and commissioning were done simultaneously by comparing the measured data with TPS‐calculated percent‐depth‐dose (PDD) and profiles. The ICA was used to measure profiles of various field sizes (10‐, 20‐, and 28 cm2) at depths of dmax (1.3 cm), 5‐, 10‐, and 20 cm. The 1DS was used for output factors (OFs) and PDDs. OFs were measured with 1DS for various fields (2–28 cm2) at a source‐to‐surface distance of 90 cm. All measured data were compared with TPS‐calculations. Profiles, off‐axis ratios (OAR), PDDs and OFs were also measured with a 3DWS as a secondary check. Profiles between the ICA and TPS (ICA and 3DWS) at various depths across the fields indicated that the maximum discrepancies in high‐dose and low‐dose tail were within 2% and 3%, respectively, and the maximum distance‐to‐agreement in the penumbra region was

To develop standard (SPCA) and regularized (RPCA) principal component analysis models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck (HN) patients and assess their potential use in adaptive radiation therapy, and for extracting quantitative information for treatment response assessment.Planning CT images of ten HN patients were artificially deformed to create "digital phantom" images, which modeled systematic anatomical changes during radiation therapy. Artificial deformations closely mirrored patients' actual deformations and were interpolated to generate 35 synthetic CBCTs, representing evolving anatomy over 35 fractions. Deformation vector fields (DVFs) were acquired between pCT and synthetic CBCTs (i.e., digital phantoms) and between pCT and clinical CBCTs. Patient-specific SPCA and RPCA models were built from these synthetic and clinical DVF sets. EigenDVFs (EDVFs) having the largest eigenvalues were hypothesized to capture the major anatomical deformations during treatment.Principal component analysis (PCA) models achieve variable results, depending on the size and location of anatomical change. Random changes prevent or degrade PCA's ability to detect underlying systematic change. RPCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes and is therefore more successful than SPCA at capturing systematic changes early in treatment. SPCA models were less successful at modeling systematic changes in clinical patient images, which contain a wider range of random motion than synthetic CBCTs, while the regularized approach was able to extract major modes of motion.Leading EDVFs from the both PCA approaches have the potential to capture systematic anatomical change during HN radiotherapy when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the RPCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are established early in a treatment course, or based on population models.