Dual-energy CBCT imaging techniques were implemented to synthesize VM CBCT and LM CBCTs. VM CBCT was effective at achieving metal artifact reduction. Depending on the dose-partitioning scheme, LM CBCT demonstrated the potential to improve CNR for low contrast objects compared to single-energy CBCT acquired with equivalent dose.

Purpose Multileaf collimator (MLC) delivery discrepancy between planned and actual (delivered) positions have detrimental effect on the accuracy of dose distributions for both IMRT and VMAT. In this study, we evaluated the consistency of MLC delivery discrepancies over the course of treatment and over time to verify that a predictive machine learning model would be applicable throughout the course of treatment. Next, the MLC and gantry positions recorded in prior trajectory log files were analyzed to build a machine learning algorithm to predict MLC positional discrepancies during delivery for a new treatment plan. An open source tool was developed and released to predict the MLC positional discrepancies at treatment delivery for any given plan. Methods Trajectory log files of 142 IMRT plans and 125 VMAT plans from 9 Varian TrueBeam linear accelerators were collected and analyzed. The consistency of delivery discrepancy over patient‐specific quality assurance (QA) and patient treatment deliveries was evaluated. Data were binned by treatment site and machine type to determine their relationship with MLC and gantry angle discrepancies. Motion‐related parameters including MLC velocity, MLC acceleration, control point, dose rate, and gravity vector, gantry velocity and gantry acceleration, where applicable, were analyzed to evaluate correlations with MLC and gantry discrepancies. Several regression models, such as simple/multiple linear regression, decision tree, and ensemble method (boosted tree and bagged tree model) were used to develop a machine learning algorithm to predict MLC discrepancy based on MLC motion parameters. Results MLC discrepancies at patient‐specific QA differed from those at patient treatment deliveries by a small (mean = 0.0021 ± 0.0036 mm, P = 0.0089 for IMRT; mean = 0.0010 ± 0.0016 mm, P = 0.0003 for VMAT) but statistically significant amount, likely due to setting the gantry angle to zero for QA in IMRT. MLC motion parameters, MLC velocity and gravity vector, showed significant correlation (P < 0.001) with MLC discrepancy, especially MLC velocity, which had an approximately linear relationship (slope = −0.0027, P < 0.001, R2 = 0.79). Incorporating MLC motion parameters, the final generalized model trained by data from all linear accelerators can predict MLC discrepancy to a high degree of accuracy with high correlation (R2 = 0.86) between predicted and actual MLC discrepancies. The same prediction results were found across different treatment sites and linear accelerators. Conclusion We have developed a machine learning model using trajectory log files to predict the MLC discrepancies during delivery. This model has been a released as a research tool in which a DICOM‐RT with predicted MLC positions can be generated using the original DICOM‐RT file as input. This tool can be used to simulate radiotherapy treatment delivery and may be useful for studies evaluating plan robustness and dosimetric uncertainties from treatment delivery.

Small deviations from an ideal geometry can result in crescent artifacts due to steep gradients in the bowtie filter. Angle-dependent blank projections can largely alleviate the artifacts.

Purpose: Trajectory log files are increasingly being utilized clinically for machine and patient specific QA. The process of converting the DICOM-RT plan to a deliverable trajectory by the linac control software introduces some uncertainty that is inherently incorporated into measurement-based patient specific QA but is not necessarily included for trajectory log file-based methods. Roughly half of prior studies have included this uncertainty in the analysis while the remaining studies have ignored it, and it has yet to be quantified in the literature. Methods: We collected DICOM-RT files from the treatment planning system and the trajectory log files from four TrueBeam linear accelerators for 25 IMRT and 10 VMAT plans. We quantified the DICOM-RT Conversion to Trajectory Residual (DCTR, difference between ‘planned’ MLC position from TPS DICOM-RT file and ‘expected’ MLC position (the deliverable MLC positions calculated by the linac control software) from trajectory log file) and compared it to the discrepancy between actual and expected machine parameters recorded in trajectory log files. Results: RMS of the DCTR was 0.0845 mm (range of RMS per field/arc: 0.0173–0.1825 mm) for 35 plans (114 fields/arcs) and was independent of treatment technique, with a maximum observed discrepancy at any control point of 0.7255 mm. DCTR was correlated with MLC velocity and was consistent over the course of treatment and over time, with a slight change in magnitude observed after a linac software upgrade. For comparison, the RMS of trajectory log file reported delivery error for moving MLCs was 0.0205 mm, thus DCTR is about four times the recorded delivery error in the trajectory log file. Conclusion: The uncertainty introduced from the conversion process by the linac control software from DICOM-RT plan to a deliverable trajectory is 3–4 times larger than the discrepancy between actual and expected machine parameters recorded in trajectory log files. This uncertainty should be incorporated into the analysis when using trajectory log file-based methods for analyzing MLC performance or patient-specific QA.

Cross scatter is substantial in dual cone-beam imaging, but its effects can be largely removed by interleaved acquisition, which can be achieved at the same angular sampling rate either by doubling the data acquisition rate or halving the rotation speed.

Purpose Scatter significantly limits the application of the dual-source CBCT by inducing scatter artifacts and degrading CNR, Hounsfield-unit (HU) accuracy and image uniformity. Although our previously developed interleaved acquisition mode addressed the cross scatter between the two x-ray sources, it doubles the scanning time and doesn’t address the forward scatter issue. This study aims to develop a pre-patient grid system to address both forward and cross scatter in the dual-source CBCT. Methods Grids attached to both x-ray sources provide physical scatter reduction during the image acquisition. Image data were measured in the unblocked region, while both forward and cross scatter were measured in the blocked region of the projection for post-scan scatter correction. Complementary projections were acquired with grids at complementary locations and were merged to form complete projections for reconstruction. Experiments were conducted with different phantom sizes, grid blocking ratios, image acquisition modes and reconstruction algorithms to investigate their effects on the scatter reduction and correction. The image quality improvement by the pre-patient grids was evaluated both qualitatively through the artifact reduction and quantitatively through CNR, HU accuracy and uniformity using a CATphan®504 phantom. Results Scatter artifacts were reduced by scatter reduction, and were removed by scatter correction method. CNR, HU accuracy and image uniformity were all substantially improved. The simultaneous acquisition mode achieved comparable CNR as the interleaved and sequential modes after scatter reduction and correction. Higher grid blocking ratio and smaller phantom size led to higher CNR for the simultaneous mode. The iterative reconstruction with TV regularization was more effective than the FDK method in reducing noise caused by the scatter correction to enhance CNR. Conclusions The pre-patient grid system is effective in removing the scatter effects in the simultaneous acquisition mode of the dual-source CBCT, which is useful for scanning time reduction or dual energy imaging.

Purpose To develop and demonstrate a comprehensive method to directly measure radiation isocenter uncertainty and coincidence with the cone‐beam computed tomography (kV‐CBCT) imaging coordinate system that can be carried out within a typical quality assurance (QA) time slot. Methods An N‐isopropylacrylamide (NIPAM) three‐dimensional (3D) dosimeter for which dose is observed as increased electron density in kV‐CBCT is irradiated at eight couch/gantry combinations which enter the dosimeter at unique orientations. One to three CBCTs are immediately acquired, radiation profile is detected per beam, and displacement from imaging isocenter is quantified. We performed this test using a 5 mm diameter MLC field, and 7.5 and 4 mm diameter cones, delivering approximately 16 Gy per beam. CBCT settings were 1035–4050 mAs, 80–125 kVs, smooth filter, 1 mm slice thickness. The two‐dimensional (2D) displacement of each beam from the imaging isocenter was measured within the planning system, and Matlab code developed in house was used to quantify relevant parameters based on the actual beam geometry. Detectability of the dose profile in the CBCT was quantified as the contrast‐to‐noise ratio (CNR) of the irradiated high‐dose regions relative to the surrounding background signal. Our results were compared to results determined by the traditional Winston‐Lutz test, film‐based “star shots,” and the vendor provided machine performance check (MPC). The ability to detect alignment errors was demonstrated by repeating the test after applying a 0.5 mm shift to the MLCs in the direction of leaf travel. In addition to radiation isocenter and coincidence with CBCT origin, the analysis also calculated the actual gantry and couch angles per beam. Results Setup, MV irradiation, and CBCT readout were carried out within 38 min. After subtracting the background signal from the pre‐CBCT, the CNR of the dosimeter signal from the irradiation with the MLCs (125 kVp, 1035 mAs, n = 3), 7.5 mm cone (125 kVp, 1035 mAs, n = 3), and 4 mm cone (80 kVp, 4050 mAs, n = 1) was 5.4, 5.9, and 2.9, respectively. The minimum radius that encompassed all beams calculated using the automated analysis was 0.38, 0.48, and 0.44 mm for the MLCs, 7.5 mm cone, and 4 mm cone, respectively. When determined manually, these values were slightly decreased at 0.28, 0.41, and 0.40 mm. For comparison, traditional Winston‐Lutz test with MLCs and MPC measured the 3D isocenter radius to be 0.24 mm. Lastly, when a 0.5 mm shift to the MLCs was applied, the smallest radius that intersected all beams increased from 0.38 to 0.90 mm. The mean difference from expected value for gantry angle was 0.19 ± 0.29°, 0.17 ± 0.23°, and 0.12 ± 0.14° for the MLCs, 7.5 mm cone, and 4 mm cone, respectively. The mean difference from expected for couch angle was −0.07 ± 0.28°, −0.08 ± 0.66°, and 0.04 ± 0.25°. Conclusions This work demonstrated the feasibility of a comprehensive isocenter verification using a NIPAM dosimeter with sub‐mm accuracy which incorporates evaluation of coincidence with imaging coordin...

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