A Monte Carlo study of the impact of phosphor optical properties on EPID imaging performance

A Comprehensive Evaluation of the Application of the Halcyon(2.0) IMRT Technique in Long-Course Radiotherapy for Rectal Cancer

Objective: To evaluate if the Halcyon(2.0) Intensity Modulation Radiotherapy (IMRT) technique has an advantage in the long-course rectal cancer radiotherapy. Methods: A total of 20 clinical IMRT plans of Halcyon(2.0) for long-course (2Gy in 25 fractions) rectal cancer radiotherapy were randomly selected. Based on the parameters of these plans, 20 TrueBeam (with the Millennium 120 MLC) plans were redesigned, respectively. The dosimetry indexes, field complexity parameters, the Gamma Passing Rates (GPR), and the delivery time of the 2 groups of plans were obtained as measures of the plan quality, the modulation complexity, the delivery accuracy, and the delivery efficiency. The differences between the 2 groups of parameters were analyzed, with P < .05 means statistically significant. Results: In terms of dosimetry, there was no significant or clinical difference between the 2 groups in critical dosimetry parameters. The Monitor Unit of the Halcyon(2.0) fields is lower than the TrueBeam fields by 26.39, while the modulation complexity score (MCS), the mean aperture area variability (AAV), and the mean leaf sequence variability (LSV) of the Halcyon(2.0) fields were 23.8%, 20%, and 2.3% larger than those of the TrueBeam fields, respectively. Neither the ArcCheck-based GPRs nor the portal-dosimetry-based GPRs in both 3%/3 mm and 2%/2 mm criteria showed the difference between the Halcyon(2.0) fields and the TrueBeam fields. The Pearson correlation coefficient between GPR(2%/2 mm) and MCS of the Halcyon(2.0) fields was 0.335, while that of the TrueBeam fields was 0.502. The mean total delivery time of the TrueBeam plans was 195.55 ± 22.86 s, while that of Halcyon(2.0) was 124.25 ± 10.42 s (P < .001), which was reduced approximatively by 36%. Conclusion: For long-course rectal cancer radiotherapy, the Halcyon(2.0) IMRT plans behave almost the same in dosimetry and delivery accuracy as the TrueBeam plans. However, the lower MU and the field modulation complexity, combined with the higher delivery efficiency, make Halcyon(2.0) a feasible and reliable platform in long-course radiotherapy for the rectal cancer.

Experimental validation of Fastcat kV and MV cone beam CT (CBCT) simulator

PURPOSE

To experimentally validate the Fastcat cone beam computed tomography (CBCT) simulator against kV and MV CBCT images acquired with a Varian Truebeam linac.

METHODS

kV and MV CBCT images of a Catphan 504 phantom were acquired using a 100 kVp beam with the on-board imager (OBI) and a 6 MV treatment beam with the electronic portal imaging device (EPID), respectively. The kV Fastcat simulation was performed using detailed models of the x-ray source, bowtie filter, a high resolution voxelized virtual Catphan phantom, anti-scatter grid, and the CsI scintillating detector. Likewise, an MV Fastcat CBCT was simulated with detailed models for the beam energy spectrum, flattening filter, a high-resolution voxelized virtual Catphan phantom, and the gadolinium oxysulfide (GOS) scintillating detector. Experimental and simulated CBCT images of the phantom were compared with respect to HU values, contrast to noise ratio (CNR), and dose linearity. Detector modulation transfer function (MTF) for the two detectors were also experimentally validated. Fastcat's dose calculations were compared to Monte Carlo (MC) dose calculations performed with Topas.

RESULTS

For the kV and MV simulations, respectively: Contrast agreed within 14 and 9 HUs and detector MTF agreed within 4.2% and 2.5%. Likewise, CNR had a root mean squared error (RMSE) of 2.6% and 1.4%. Dose agreed within 2.4% and 1.6% of MC values. The kV and MV CBCT images took 71 and 72 s to simulate in Fastcat with 887 and 493 projections, respectively.

CONCLUSIONS

We present a multienergy experimental validation of a fast and accurate CBCT simulator against a commercial linac. The simulator is open source and all models found in this work can be downloaded from https://github.com/jerichooconnell/fastcat.git.

Monte Carlo methods for device simulations in radiation therapy

Monte Carlo (MC) simulations play an important role in radiotherapy, especially as a method to evaluate physical properties that are either impossible or difficult to measure. For example, MC simulations (MCSs) are used to aid in the design of radiotherapy devices or to understand their properties. The aim of this article is to review the MC method for device simulations in radiation therapy. After a brief history of the MC method and popular codes in medical physics, we review applications of the MC method to model treatment heads for neutral and charged particle radiation therapy as well as specific in-room devices for imaging and therapy purposes. We conclude by discussing the impact that MCSs had in this field and the role of MC in future device design.

Monte Carlo methods for device simulations in radiation therapy

Monte Carlo (MC) simulations play an important role in radiotherapy, especially as a method to evaluate physical properties that are either impossible or difficult to measure. For example, MC simulations (MCSs) are used to aid in the design of radiotherapy devices or to understand their properties. The aim of this article is to review the MC method for device simulations in radiation therapy. After a brief history of the MC method and popular codes in medical physics, we review applications of the MC method to model treatment heads for neutral and charged particle radiation therapy as well as specific in-room devices for imaging and therapy purposes. We conclude by discussing the impact that MCSs had in this field and the role of MC in future device design.

fastCAT: Fast cone beam CT (CBCT) simulation

PURPOSE

To develop fastCAT, a fast cone-beam computed tomography (CBCT) simulator. fastCAT uses pre-calculated Monte Carlo (MC) CBCT phantom-specific scatter and detector response functions to reduce simulation time for megavoltage (MV) and kilovoltage (kV) CBCT imaging.

METHODS

Pre-calculated x-ray beam energy spectra, detector optical spread functions and energy deposition, and phantom scatter kernels are combined with GPU raytracing to produce CBCT volumes. MV x-ray beam spectra are simulated with EGSnrc for 2.5- and 6 MeV electron beams incident on a variety of target materials and kV x-ray beam spectra are calculated analytically for an x-ray tube with a tungsten anode. Detectors were modeled in Geant4 extended by Topas and included optical transport in the scintillators. Two MV detectors were modeled-a standard Varian AS1200 GOS detector and a novel CWO high detective quantum efficiency detector. A kV CsI detector was also modeled. Energy-dependent scatter kernels were created in Topas for two 16 cm diameter phantoms: A Catphan 515 contrast phantom and an anthropomorphic head phantom. The Catphan phantom contained inserts of 1-5 mm in diameter of six different tissue types: brain, deflated lung, compact and cortical bone, adipose, and B-100.

RESULTS

fastCAT simulations retain high fidelity to measurements and MC simulations: MTF curves were within 3.5% and 1.2% of measured values for the CWO and GOS detectors, respectively. HU values and CNR in a fastCAT Catphan 515 simulation were seen to be within 95% confidence intervals of an equivalent MC simulation for all of the tissues with root mean squared errors less than 16 HU and 1.6 in HU values and CNR comparisons, respectively. The anthropomorphic head phantom CWO detector CBCT image resulted in much higher tissue contrast and lower noise compared to the GOS detector CBCT image. A fastCAT simulation of the Catphan 515 module with an image size of 1024  ×  1024  ×  10 voxels took 61 s on a GPU while the equivalent Topas MC was estimated to take more than 0.3 CPU years.

CONCLUSIONS

We present an open source fast CBCT simulation with high fidelity to MC simulations. The fastCAT python package can be found at https://github.com/jerichooconnell/fastCAT.git.

Frequency-dependent optimal weighting approach for megavoltage multilayer imagers

Multi-layer imaging (MLI) devices improve the detective quantum efficiency (DQE) while maintaining the spatial resolution of conventional mega-voltage (MV) x-ray detectors for applications in radiotherapy. To date, only MLIs with identical detector layers have been explored. However, it may be possible to instead use different scintillation materials in each layer to improve the final image quality. To this end, we developed and validated a method for optimally combining the individual images from each layer of MLI devices that are built with heterogeneous layers. Two configurations were modeled within the GATE Monte Carlo package by stacking different layers of a terbium doped gadolinium oxysulfide Gd2O2S:Tb (GOS) phosphor and a LKH-5 glass scintillator. Detector response was characterized in terms of the modulation transfer function (MTF), normalized noise power spectrum (NNPS) and DQE. Spatial frequency-dependent weighting factors were then analytically derived for each layer such that the total DQE of the summed combination image would be maximized across all spatial modes. The final image is obtained as the weighted sum of the sub-images from each layer. Optimal weighting factors that maximize the DQE were found to be the quotient of MTF and NNPS of each layer in the heterogeneous MLI detector. Results validated the improvement of the DQE across the entire frequency domain. For the LKH-5 slab configuration, DQE(0) increases between 2%-3% (absolute), while the corresponding improvement for the LKH-5 pixelated configuration was 7%. The performance of the weighting method was quantitatively evaluated with respect to spatial resolution, contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of simulated planar images of phantoms at 2.5 and 6 MV. The line pair phantom acquisition exhibited a twofold increase in CNR and SNR, however MTF was degraded at spatial frequencies greater than 0.2 lp mm^-1. For the Las Vegas phantom, the weighting improved the CNR by around 30% depending on the contrast region while the SNR values are higher by a factor of 2.5. These results indicate that the imaging performance of MLI systems can be enhanced using the proposed frequency-dependent weighting scheme. The CNR and SNR of the weighted combined image are improved across all spatial scales independent of the detector combination or photon beam energy.

A Bayesian control chart based on the beta distribution for monitoring the two-dimensional gamma index pass rate in the context of patient-specific quality assurance

PURPOSE

In the context of quality assurance in intensity modulated radiation therapy (IMRT), the aim of this work was two-fold: (a) to show that the beta distribution characterizes the two-dimensional gamma index pass rate (GIPR), and that the quantiles of the distribution should be used in order to compute the control limit (CL) for the detection of abnormally low GIPR, and (b) to introduce a Bayesian control chart that allows calculation of CLs from the first measurement.

METHODS

In order to enable monitoring of the GIPR from the first measurement, we developed a Bayesian control chart based on the beta distribution, elaborated according to the following two steps: (a) an iterative bayesian inference approach without any prior information on the GIPR distribution was used at the start of monitoring and the CL was progressively updated; and (b) when sufficient in-control arcs had been recorded and the estimators of the parameters of the beta distribution were sufficiently accurate, the CL of the chart was fixed to a constant value corresponding to the quantile of the beta distribution. The clinical utility of this approach is illustrated through a real data case study: monitoring the GIPR of patients treated with a moving gantry IMRT technique RapidArc^TM on a Novalis TrueBeam STx (Varian Medical Systems) linear accelerator equipped with an aS1200 electronic portal imager device.

RESULTS

We showed that some commonly used distributions for monitoring GIPR in the literature, such as normal or logarithm transformation, are not appropriate. We compared the CLs of those solutions with the CL of our chart based on the BD (CL = 95.14%). The comparison revealed that the CL for the normal law (CL = 97.62%) generated too many false positives, and that the CL of the Logarithm transformation (CL = 83.74%) could fail to efficiently detect (i.e., sufficiently early on or faster) changes in the process.

CONCLUSIONS

Successful GIPR monitoring requires careful and rigorous application of well-established statistical concepts in the field of statistical process control. In this paper, we stress the importance of carefully analyzing the distribution of the monitored characteristic that is plotted on the control chart. We propose a Bayesian control chart that can be viewed as a practical solution for early implementation of GIPR monitoring, starting from the first arc. We demonstrate that beta distribution is a better method for characterizing the GIPR, and thus, the use of this approach is expected to improve patient-specific quality assurance plans in radiotherapy.

Performance of a new star-bar phantom designed for MTF calculations in x-ray imaging systems

PURPOSE

A new phantom, designed and manufactured for modulation transfer function (MTF) calculations is presented in this work. The phantom has a star-bar pattern and is manufactured in stainless steel. Modulation transfer function determinations are carried out with the new phantom and with an edge phantom to compare their performance and to compare them with previous theoretical predictions.

METHODS

The phantoms are imaged in an x-ray imaging system using different beam qualities and different entrance air KERMA. Methods, previously developed for synthetic images and simulations, are adapted to real measurements, solving practical implementation issues.

RESULTS

In the case of the star-bar, in order to obtain optimal MTF determinations it is necessary to accurately determine the center of the pattern. Also, to avoid underestimates in MTF calculations, the length in pixels of each of the scanning circumferences must be an integer multiple of the number of cycles in the pattern. Both methods, star-bar and edge, give similar mean values of the MTF in all cases analyzed. Also, the dependence with frequency of the experimental MTF standard deviation (SD) agrees with the theoretical expressions presented in previous works. In this regard, the precision is better for the star-bar method than for the edge and differences in precision between both methods are higher for the lowest beam quality.

CONCLUSIONS

The star-bar phantom can be used for MTF determinations with the advantage of having an improved precision. However, precision is reduced when the radiation quality increases. This fact suggests that, for the highest beam qualities, materials with an attenuation coefficient greater than that of steel should be used to manufacture the phantom.

Evaluating the sensitivity of Halcyon's automatic transit image acquisition for treatment error detection: A phantom study using static IMRT

PURPOSE

The Varian Halcyon™ electronic portal imaging detector is always in-line with the beam and automatically acquires transit images for every patient with full-field coverage. These images could be used for "every patient, every monitor unit" quality assurance (QA) and eventually adaptive radiotherapy. This study evaluated the imager's sensitivity to potential clinical errors and day-to-day variations from clinical exit images.

METHODS

Open and modulated fields were delivered for each potential error. To evaluate output changes, monitor units were scaled by 2%-10% and delivered to solid water slabs and a homogeneous CIRS phantom. To mimic weight changes, 0.5-5.0 cm of buildup was added to the solid water. To evaluate positioning changes, a homogeneous and heterogeneous CIRS phantom were shifted 2-10 cm and 0.2-1.5 cm, respectively. For each test, mean relative differences (MRDs) and standard deviations in the pixel-difference histograms (σ_RD ) between test and baseline images were calculated. Lateral shift magnitudes were calculated using cross-correlation and edge-detection filtration. To assess patient variations, MRD and σ_RD were calculated from six prostate patients' daily exit images and compared between fractions with and without gas present.

RESULTS

MRDs responded linearly to output and buildup changes with a standard deviation of 0.3%, implying a 1% output change and 0.2 cm changes in buildup could be detected with 2.5σ confidence. Shifting the homogenous phantom laterally resulted in detectable MRD and σ_RD changes, and the cross-correlation function calculated the shift to within 0.5 mm for the heterogeneous phantom. MRD and σ_RD values were significantly associated with the presence of gas for five of the six patients.

CONCLUSIONS

Rapid analyses of automatically acquired Halcyon™ exit images could detect mid-treatment changes with high sensitivity, though appropriate thresholds will need to be set. This study presents the first steps toward developing effortless image evaluation for all aspects of every patient's treatment.