Purpose A novel Megavoltage (MV) multi-layer imager (MLI) design featuring higher detective quantum efficiency and lower noise than current conventional MV imagers in clinical use has been recently reported. Optimization of the MLI design for multiple applications including tumor tracking, MV-CBCT and portal dosimetry requires a computational model that will provide insight into the physics processes that affect the overall and individual components’ performance. The purpose of the current work was to develop and validate a comprehensive computational model that can be used for MLI optimization. Methods The MLI model was built using the Geant4 Application for Tomographic Emission (GATE) application. The model includes x-ray and charged particle interactions as well as the optical transfer within the phosphor. A first prototype MLI device featuring a stack of four detection layers was used for model validation. Each layer of the prototype contains a copper buildup sheet, a phosphor screen and photodiode array. The model was validated against measured data of Modulation Transfer Function (MTF), Noise Power Spectrum (NPS) and Detective Quantum Efficiency (DQE). MTF was computed using a slanted slit with 2.3° angle and 0.1 mm width. NPS was obtained using the autocorrelation function technique. DQE was calculated from MTF and NPS data. The comparison metrics between simulated and measured data were the Pearson’s correlation coefficient (r) and the normalized root-mean-square error (NRMSE). Results Good agreement between measured and simulated MTF and NPS values was observed. Pearson’s correlation coefficient for the combined signal from all layers of the MLI was equal to 0.9991 for MTF and 0.9992 for NPS; NRMSE was 0.0121 for MTF and 0.0194 for NPS. Similarly, the DQE correlation coefficient for the combined signal was 0.9888 and the NRMSE was 0.0686. Conclusions A comprehensive model of the novel MLI design was developed using the GATE toolkit and validated against measured MTF, NPS and DQE data acquired with a prototype device featuring four layers. This model will be used for further optimization of the imager components and configuration for clinical radiotherapy applications.

Purpose In-treatment imaging using an electronic portal imaging device (EPID) can be used to confirm patient and tumor positioning. Real-time tumor tracking performance using current digital megavolt (MV) imagers is hindered by poor image quality. Novel EPID designs may help to improve quantum noise response, while also preserving the high spatial resolution of the current clinical detector. Recently investigated EPID design improvements include but are not limited to multi-layer imager (MLI) architecture, thick crystalline and amorphous scintillators, and phosphor pixilation and focusing. The goal of the present study was to provide a method of quantifying improvement in tracking performance as well as to reveal the physical underpinnings of detector design that impact tracking quality. The study employs a generalizable ideal observer methodology for the quantification of tumor tracking performance. The analysis is applied to study both the effect of increasing scintillator thickness on a standard, single-layer imager (SLI) design as well as the effect of MLI architecture on tracking performance. Methods The present study uses the ideal observer signal-to-noise ratio (d′) as a surrogate for tracking performance. We employ functions which model clinically relevant tasks and generalized frequency-domain imaging metrics to connect image quality with tumor tracking. A detection task for relevant Cartesian shapes (i.e. spheres and cylinders) was used to quantify trackability of cases employing fiducial markers. Automated lung tumor tracking algorithms often leverage the differences in benign and malignant lung tissue textures. These types of algorithms (e.g. soft tissue localization – STiL) were simulated by designing a discrimination task, which quantifies the differentiation of tissue textures, measured experimentally and fit as a power-law in trend (with exponent β) using a cohort of MV images of patient lungs. The modeled MTF and NPS were used to investigate the effect of scintillator thickness and MLI architecture on tumor tracking performance. Results Quantification of MV images of lung tissue as an inverse power-law with respect to frequency yields exponent values of β = 3.11 and 3.29 for benign and malignant tissues, respectively. Tracking performance with and without fiducials was found to be generally limited by quantum noise, a factor dominated by quantum detective efficiency (QDE). For generic SLI construction, increasing the scintillator thickness (gadolinium oxysulfide – GOS) from a standard 290 μm to 1720 μm reduces noise to about 10%. However, 81% of this reduction is appreciated between 290 and 1000 μm. In comparing MLI and SLI detectors of equivalent individual GOS layer thickness, the improvement in noise is equal to the number of layers in the detector (i.e. 4) with almost no difference in MTF. Further, improvement in tracking performance was slightly less than the square-root of the reduction in noise, approximately 84–90%. In comparing an MLI detector with an SLI with a GOS scintillator of...

We have developed a novel method for fast image simulation of flat panel detectors, based on the photon energy deposition efficiency and the optical spread function (OSF). The proposed method, FastEPID, determines the photon detection using photon energy deposition and replaces particle transport within the detector with precalculated OSFs. The FastEPID results are validated against experimental measurement and conventional Monte Carlo simulation in terms of modulation transfer function (MTF), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contrast, and relative difference of pixel value, obtained with a slanted slit image, Las Vegas phantom, and anthropomorphic pelvis phantom. Excellent agreement is observed between simulation and measurement in all cases. Without degrading image quality, the FastEPID method is capable of reducing simulation time up to a factor of 150. Multiple applications, such as imager design optimization for planar and volumetric imaging, are expected to benefit from the implementation of the FastEPID method.

Megavoltage (MV) cone-beam computed tomography (CBCT) using an electronic portal imaging (EPID) offers advantageous features, including 3D mapping, treatment beam registration, high-z artifact suppression, and direct radiation dose calculation. Adoption has been slowed by image quality limitations and concerns about imaging dose. Developments in imager design, including pixelated scintillators, structured phosphors, inexpensive scintillation materials, and multi-layer imager (MLI) architecture have been explored to improve EPID image quality and reduce imaging dose. The present study employs a hybrid Monte Carlo and linear systems model to determine the effect of detector design elements, such as multi-layer architecture and scintillation materials. We follow metrics of image quality including modulation transfer function (MTF) and noise power spectrum (NPS) from projection images to 3D reconstructions to in-plane slices and apply a task based figure-of-merit, the ideal observer signal-to-noise ratio (d') to determine the effect of detector design on object detectability. Generally, detectability was limited by detector noise performance. Deploying an MLI imager with a single scintillation material for all layers yields improvement in noise performance and d' linear with the number of layers. In general, improving x-ray absorption using thicker scintillators results in improved DQE(0). However, if light yield is low, performance will be affected by electronic noise at relatively high doses, resulting in rapid image quality degradation. Maximizing image quality in a heterogenous MLI detector (i.e. multiple different scintillation materials) is most affected by limiting total noise. However, while a second-order effect, maximizing total spatial resolution of the MLI detector is a balance between the intensity contribution of each layer against its individual MTF. So, while a thinner scintillator may yield a maximal individual-layer MTF, its quantum efficiency will be relatively low in comparison to a thicker scintillator and thus, intensity contribution may be insufficient to noticeably improve the total detector MTF.

Electronic portal imaging devices (EPIDs) lend themselves to beams-eye view clinical applications, such as tumor tracking, but are limited by low contrast and detective quantum efficiency (DQE). We characterize a novel EPID prototype consisting of multiple layers and investigate its suitability for use under clinical conditions. A prototype multi-layer imager (MLI) was constructed utilizing four conventional EPID layers, each consisting of a copper plate, a Gd2O2S:Tb phosphor scintillator, and an amorphous silicon flat panel array detector. We measured the detector’s response to a 6 MV photon beam with regards to modulation transfer function, noise power spectrum, DQE, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and the linearity of the detector’s response to dose. Additionally, we compared MLI performance to the single top layer of the MLI and the standard Varian AS-1200 detector. Pre-clinical imaging was done on an anthropomorphic phantom, and the detector’s CNR, SNR and spatial resolution were assessed in a clinical environment. Images obtained from spine and liver patient treatment deliveries were analyzed to verify CNR and SNR improvements. The MLI has a DQE(0) of 9.7%, about 5.7 times the reference AS-1200 detector. Improved noise performance largely drives the increase. CNR and SNR of clinical images improved three-fold compared to reference. A novel MLI was characterized and prepared for clinical translation. The MLI substantially improved DQE and CNR performance while maintaining the same resolution. Pre-clinical tests on an anthropomorphic phantom demonstrated improved performance as predicted theoretically. Preliminary patient data were analyzed, confirming improved CNR and SNR. Clinical applications are anticipated to include more accurate soft tissue tracking.