Digital phantom versus patient-specific radiation dosimetry in adult routine thorax CT examinations.

PURPOSE: The aim of this study was to compare the organ doses assessed through a digital phantom-based and a patient specific-based dosimetric tool in adult routine thorax computed tomography (CT) examinations with reference to physical dose measurements performed in anthropomorphic phantoms. METHODS: Two Monte Carlo based dose calculation tools were used to assess organ doses in routine adult thorax CT examinations. These were a digital phantom-based dosimetry tool (NCICT, National Cancer Institute, USA) and a patient-specific individualized dosimetry tool (ImpactMC, CT Imaging GmbH, Germany). Digital phantoms and patients were classified in four groups according to their water equivalent diameter (Dw). Normalized to volume computed tomography dose index (CTDIvol), organ dose was assessed for lungs, esophagus, heart, breast, active bone marrow, and skin. Organ doses were compared to measurements performed using thermoluminescent detectors (TLDs) in two physical anthropomorphic phantoms that simulate the average adult individual as a male (Alderson Research Labs, USA) and as a female (ATOM Phantoms, USA). RESULTS: The average percent difference of NCICT to TLD and ImpactMC to TLD dose measurements across all organs in both sexes was 13% and 6%, respectively. The average ± 1 standard deviation in dose values across all organs with NCICT, ImpactMC, and TLDs was ± 0.06 (mGy/mGy), ± 0.19 (mGy/mGy), and ± 0.13 (mGy/mGy), respectively. Organ doses decreased with increasing Dw in both NCICT and ImpactMC. CONCLUSION: Organ doses estimated with ImpactMC were in closer agreement to TLDs compared to NCICT. This may be attributed to the inherent property of ImpactMC methodology to generate phantoms that resemble the realistic anatomy of the examined patient as opposed to NCICT methodology that incorporates an anatomical discrepancy between phantoms and patients.

A fully automated machine learning-based methodology for personalized radiation dose assessment in thoracic and abdomen CT.

PURPOSE: To develop a machine learning-based methodology for patient-specific radiation dosimetry in thoracic and abdomen CT. METHODS: Three hundred and thirty-one thoracoabdominal radiotherapy-planning CT examinations with the respective organ/patient contours were collected retrospectively for the development and validation of segmentation 3D-UNets. Moreover, 97 diagnostic thoracic and 89 diagnostic abdomen CT examinations were collected retrospectively. For each of the diagnostic CT examinations, personalized MC dosimetry was performed. The data derived from MC simulations along with the respective CT data were used for the training and validation of a dose prediction deep neural network (DNN). An algorithm was developed to utilize the trained models and perform patient-specific organ dose estimates for thoracic and abdomen CT examinations. The doses estimated with the DNN were compared with the respective doses derived from MC simulations. A paired t-test was conducted between the DNN and MC results. Furthermore, the time efficiency of the proposed methodology was assessed. RESULTS: The mean percentage differences (range) between DNN and MC dose estimates for the lungs, liver, spleen, stomach, and kidneys were 7.2 % (0.2-24.1 %), 5.5 % (0.4-23.0 %), 7.9 % (0.6-22.3 %), 6.9 % (0.0-23.0 %) and 6.7 % (0.3-22.6 %) respectively. The differences between DNN and MC dose estimates were not significant (p-value = 0.12). Moreover, the mean processing time of the proposed workflow was 99 % lower than the respective time needed for MC-based dosimetry. CONCLUSIONS: The proposed methodology can be used for rapid and accurate patient-specific dosimetry in chest and abdomen CT.

Organ dose prediction for patients undergoing radiotherapy CBCT chest examinations using artificial intelligence.

PURPOSE: To propose an artificial intelligence (AI)-based method for personalized and real-time dosimetry for chest CBCT acquisitions. METHODS: CT images from 113 patients who underwent radiotherapy treatment were collected for simulating thorax examinations using cone-beam computed tomography (CBCT) with the Monte Carlo technique. These simulations yielded organ dose data, used to train and validate specific AI algorithms. The efficacy of these AI algorithms was evaluated by comparing dose predictions with the actual doses derived from Monte Carlo simulations, which are the ground truth, utilizing Bland-Altman plots for this comparative analysis. RESULTS: The absolute mean discrepancies between the predicted doses and the ground truth are (0.9 ± 1.3)% for bones, (1.2 ± 1.2)% for the esophagus, (0.5 ± 1.3)% for the breast, (2.5 ± 1.4)% for the heart, (2.4 ± 2.1)% for lungs, (0.8 ± 0.6)% for the skin, and (1.7 ± 0.7)% for integral. Meanwhile, the maximum discrepancies between the predicted doses and the ground truth are (14.4 ± 1.3)% for bones, (12.9 ± 1.2)% for the esophagus, (9.4 ± 1.3)% for the breast, (14.6 ± 1.4)% for the heart, (21.2 ± 2.1)% for lungs, (10.0 ± 0.6)% for the skin, and (10.5 ± 0.7)% for integral. CONCLUSIONS: AI models that can make real-time predictions of the organ doses for patients undergoing CBCT thorax examinations as part of radiotherapy pre-treatment positioning were developed. The results of this study clearly show that the doses predicted by analyzed AI models are in close agreement with those calculated using Monte Carlo simulations.

Is deep learning-enabled real-time personalized CT dosimetry feasible using only patient images as input?

PURPOSE: To propose a novel deep-learning based dosimetry method that allows quick and accurate estimation of organ doses for individual patients, using only their computed tomography (CT) images as input. METHODS: Despite recent advances in medical dosimetry, personalized CT dosimetry remains a labour-intensive process. Current state-of-the-art methods utilize time-consuming Monte Carlo (MC) based simulations for individual organ dose estimation in CT. The proposed method uses conditional generative adversarial networks (cGANs) to substitute MC simulations with fast dose image generation, based on image-to-image translation. The pix2pix architecture in conjunction with a regression model was utilized for the generation of the synthetic dose images. The lungs, heart, breast, bone and skin were manually segmented to estimate and compare organ doses calculated using both the original and synthetic dose images, respectively. RESULTS: The average organ dose estimation error for the proposed method was 8.3% and did not exceed 20% for any of the organs considered. The performance of the method in the clinical environment was also assessed. Using segmentation tools developed in-house, an automatic organ dose calculation pipeline was set up. Calculation of organ doses for heart and lung for each CT slice took about 2 s. CONCLUSIONS: This work shows that deep learning-enabled personalized CT dosimetry is feasible in real-time, using only patient CT images as input.

MV-based relative electron density estimation (iMREDe) for MR-LINAC dose calculation.

BACKGROUND: MR-LINAC systems have been increasingly utilized for real-time imaging in adaptive treatments worldwide. Challenges in MR representation of air cavities and subsequent estimation of electron density maps impede planning efficiency and may lead to dose calculation uncertainties. PURPOSE: To demonstrate the generation of accurate electron density maps using the primary MV beam with a flat-panel imager. METHODS: The ViewRay MRIdian MR-LINAC system was modeled digitally for Monte Carlo simulations. Iron shimming, the magnetic field, and the proposed flat panel detector were included in the model. The effect of the magnetic field on the detector response was investigated. Acquisition of projections over 360 degrees was simulated for digital phantoms of the Catphan 505 phantom and a patient treated for Head and Neck cancer. Shim patterns on the projections were removed and detector noise linearity was assessed. Electron density maps were generated for the digital patient phantom using the flat-panel detector and compared with actual treatment planning CT generated electron density maps of the same patient. RESULTS: The effect of the magnetic field on the detector point-spread function (PSF) was found to be substantial for field strengths above 50 mT. Shims correction in the projection images using air normalization and in-painting effectively removed reconstruction artifacts without affecting noise linearity. The relative difference between reconstructed electron density maps from the proposed method and electron density maps generated from the treatment planning CT was 11% on average along all slices included in the iMREDe reconstruction. CONCLUSIONS: The proposed iMREDe technique demonstrated the feasibility of generating accurate electron densities for the ViewRay MRIdian MR-LINAC system with a flat-panel imager and the primary MV beam. This work is a step towards reducing the time and effort required for adaptive radiotherapy in the current ViewRay MR-LINAC systems.

A kV-MV approach to CBCT metal artifact reduction using multi-layer MV-CBCT.

Objective. To demonstrate that complete cone beam CT (CBCT) scans from both MV-energy and kV-energy LINAC sources can reduce metal artifacts in radiotherapy guidance, while maintaining standard-of-care x-ray doses levels.Approach. MV-CBCT and kV-CBCT scans are acquired at half normal dose. The impact of lowered dose on MV-CBCT data quality is mitigated by the use of a 4-layer MV-imager prototype and reduced LINAC energy settings (2.5 MV) to improve photon capture. Additionally, the MV-CBCT is used to determine the 3D position and pose of metal implants, which in turn is used to guide model-based poly-energetic correction and interleaving of the kV-CBCT and MV-CBCT data. Certain edge-preserving regularization steps incorporated into the model-based correction algorithm further reduce MV data noise.Main results. The method was tested in digital phantoms and a real pelvis phantom with large 2.5″ spherical inserts, emulating hip replacements of different materials. The proposed method demonstrated an appealing compromise between the high contrast of kV-CBCT and low artifact content of MV-CBCT. Contrast-to-noise improved 3-fold compared to MV-CBCT with a clinical 1-layer architecture at matched dose (37 mGy) and edge blur levels. Visual delineation of the bladder and prostate improved noteably over kV- or MV-CBCT alone.Significance. The proposed method demonstrates that a full MV-CBCT scan can be combined with kV-CBCT to reduce metal artifacts without resorting to complicated beam collimation strategies to limit the MV-CBCT dose contribution. Additionally, significant improvements in CNR can be achieved as compared to metal artifact reduction through current clinical MV-CBCT practices.

Rapid estimation of patient-specific organ doses using a deep learning network.

BACKGROUND: Patient-specific organ-dose estimation in diagnostic CT examinations can provide useful insights on individualized secondary cancer risks, protocol optimization, and patient management. Current dose estimation techniques mainly rely on time-consuming Monte Carlo methods or/and generalized anthropomorphic phantoms. PURPOSE: We proposed a proof-of-concept rapid workflow based on deep learning networks to estimate organ doses for individuals following thorax Computed Tomography (CT) examinations. METHODS: CT scan data from 95 individuals undergoing thorax CT examinations were used. Monte Carlo simulations were performed and three-dimensional (3D) dose distributions for each patient were obtained. A fully connected sequential deep learning network model was constructed and trained for each organ considered in this study. Water-equivalent diameter (WED), scan length, and tube current were the independent variables. Organ doses for heart, lungs, esophagus, and bones were calculated from the Monte Carlo 3D distribution and used to train the deep learning networks. Organ dose predictions from each network were evaluated using an independent data set of 19 patients. RESULTS: The trained networks provided organ dose predictions within a second. There was very good agreement between the deep learning network predictions and reference organ dose values calculated from Monte Carlo simulations. The average difference was -1.5% for heart, -1.6% for esophagus, -1.0% for lungs, and -0.4% for bones in the 95 patients dataset, and -5.1%, 4.3%, 0.9%, and 1.4% respectively in the 19 patients test dataset. CONCLUSIONS: The proposed workflow demonstrated that patient-specific organ-doses can be estimated in nearly real-time using deep learning networks. The workflow can be readily implemented and requires a small set of representative data for training.

Monte Carlo model of a prototype flat-panel detector for multi-energy applications in radiotherapy.

BACKGROUND: The incorporation of multi-energy capabilities into radiotherapy flat-panel detectors offers advantages including enhanced soft tissue visualization by reduction of signal from overlapping anatomy such as bone in 2D image projections; creation of virtual monoenergetic images for 3D contrast enhancement, metal artefact reduction and direct acquisition of relative electron density. A novel dual-layer on-board imager offering dual energy processing capabilities is being designed. As opposed to other dual-energy implementation techniques which require separate acquisition with two different x-ray spectra, the dual-layer detector design enables simultaneous acquisition of high and low energy images with a single exposure. A computational framework is required to optimize the design parameters and evaluate detector performance for specific clinical applications. PURPOSE: In this study, we report on the development of a Monte Carlo (MC) model of the imager including model validation. METHODS: The stack-up of the dual-layer imager (DLI) was implemented in GEANT4 Application for Tomographic Emission (GATE). The DLI model has an active area of 43×43 cm2 , with top and bottom Cesium Iodide (CsI) scintillators of 600 and 800 µm thickness, respectively. Measurement of spatial resolution and imaging of dedicated multi-material dual-energy (DE) phantoms were used to validate the model. The modulation transfer function (MTF) of the detector was calculated for a 120 kVp x-ray spectrum using a 0.5 mm thick tantalum edge rotated by 2.5o . For imaging validation, the DE phantom was imaged using a 140 kVp x-ray spectrum. For both validation simulations, corresponding measurements were done using an initial prototype of the imager. Agreement between simulations and measurement was assessed using normalized root mean square error (NRMSE) and 1D profile difference for the MTF and phantom images respectively. Further comparison between measurement and simulation was made using virtual monoenergetic images (VMIs) generated from basis material images derived using precomputed look-up tables. RESULTS: The MTF of the bottom layer of the dual-layer model shows values decreasing more quickly with spatial frequency, compared to the top layer, due to the thicker bottom scintillator thickness and scatter from the top layer. A comparison with measurement shows NRMSE of 0.013 and 0.015 as well as identical MTF50 of 0.8 mm1 and 1.0 mm1 for the top and bottom layer respectively. For the DE imaging of the DE-phantom, although a maximum deviation of 3.3% is observed for the 10 mm aluminum and Teflon inserts at the top layer, the agreement for all other inserts is less than 2.2% of the measured value at both layers. Material decomposition of simulated scatter-free DE images gives an average accuracy in PMMA and aluminum composition of 4.9% and 10.3% for 11-30 mm PMMA and 1-10 mm aluminum objects respectively. A comparison of decomposed values using scatter containing measured and simulated DE images shows good agreement within statistical uncertainty. CONCLUSION: Validation using both MTF and phantom imaging shows good agreement between simulation and measurements. With the present configuration of the digital prototype, the model can generate material decomposed images and virtual monoenergetic images.

Monte Carlo investigation of charge-transport effects on energy resolution and detection efficiency of pixelated CZT detectors for SPECT/PET applications.

PURPOSE: Semiconductor detectors are increasingly considered as alternatives to scintillation crystals for nuclear imaging applications such as positron emission tomography (PET) or single photon emission computed tomography (SPECT). One of the most prominent detector materials is cadmium zinc telluride (CZT), which is currently used in several application-specific nuclear imaging systems. In this work, the charge-transport effects in pixelated CZT detectors in relation to detector pixel size and thickness are investigated for pixels sizes from 0.4 up to 1.6 mm. METHODS: The determination of an optimum pixel size and thickness for use with photon energies of 140 and 511 keV, suitable for SPECT and PET studies, is attempted using photon detection efficiency and energy resolution as figures of merit. The Monte Carlo method combined with detailed finite element analysis was utilized to realistically model photon interactions in the detector and the signal generation process. The GEANT4 Application for Tomographic Emission (GATE) toolkit was used for photon irradiation and interaction simulations. The COMSOL MULTIPHYSICS software application was used to create finite element models of the detector that included charge drift, diffusion, trapping, and generation. Data obtained from the two methods were combined to generate accurate signal induction at the detector pixels. The energy resolution was calculated as the full width at half maximum of the energy spectrum photopeak. Photon detection efficiency was also calculated. The effects of charge transport within the detector and photon escape from primary pixel of interaction were investigated; the extent of diffusion to lateral pixels was also assessed. RESULTS: Charge transport and signal induction were affected by the position of a pixel in the detector. Edge and corner pixels were less susceptible to lateral diffusion than pixels located in the inner part of the detector. Higher detection efficiency and increased photon escape from primary interaction pixel were observed for thicker detectors. Energy resolution achieved better values in 0.7 and 1.0 mm pixel size for 5 mm detector thickness and 1.6 mm pixel size for 10 mm thickness. CONCLUSIONS: Selection of pixel size and thickness depends on the imaging application and photon energy utilized. For systems that integrate two nuclear imaging modalities (i.e., combined SPECT/PET), the pixel size should offer an appropriate balance of the effects that take place in the detector, based on the results of the current work. This allows for a system to be designed with the same detector material and the same geometrical configuration for both modalities.

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.

Abbreviated on-treatment CBCT using roughness penalized mono-energization of kV-MV data and a multi-layer MV imager.

Simultaneous acquisition of cone beam CT (CBCT) projections using both the kV and MV imagers of an image guided radiotherapy system reduces set-up scan times-a benefit to lung cancer radiation oncology patients-but increases noise in the 3D reconstruction. In this article, we present a kV-MV scan time reduction technique that uses two noise-reducing measures to achieve superior performance. The first is a high-DQE multi-layer MV imager prototype. The second is a beam hardening correction algorithm which combines poly-energetic modeling with edge-preserving, regularized smoothing of the projections. Performance was tested in real acquisitions of the Catphan 604 and a thorax phantom. Percent noise was quantified from voxel values in a soft tissue volume of interest (VOI) while edge blur was quantified from a VOI straddling a boundary between air and soft material. Comparisons in noise/resolution performance trade-off were made between our proposed approach, a dose-equivalent kV-only scan, and a kV-MV reconstruction technique previously published by Yinet al(2005Med. Phys.329). The proposed technique demonstrated lower noise as a function of spatial resolution than the baseline kV-MV method, notably a 50% noise reduction at typical edge blur levels. Our proposed method also exhibited fainter non-uniformity artifacts and in some cases superior contrast. Overall, we find that the combination of a multi-layer MV imager, acquiring at a LINAC source energy of 2.5 MV, and a denoised beam hardening correction algorithm enables noise, resolution, and dose performance comparable to standard kV-imager only set-up CBCT, but with nearly half the gantry rotation time.

Effect of scan projection radiography coverage on tube current modulation in pediatric and adult chest CT.

PURPOSE: To investigate the effect of scan projection radiography (SPR) coverage on tube current modulation in pediatric and adult thoracic CT examinations. METHODS: Sixty pediatric and 60 adult chest CT examinations were retrospectively studied to determine the incidence rate of examinations involving SPRs that did not include the entire image volume (IV) or the entire primarily exposed body volume (PEBV). The routine chest CT acquisition procedure on a modern 64-slice CT system was imitated on five anthropomorphic phantoms of different size. SPRs of varying length were successively acquired. The same IV was prescribed each time and the computed tube current modulation plan was recorded. The SPR boundaries were altered symmetrically by several steps of ±10mm with respect to the IV boundaries. RESULTS: The upper IV boundary was found to be excluded from SPR in 52% of pediatric and 40% adult chest CT examinations. The corresponding values for the lower boundary were 15% and 20%, respectively. The computed tube current modulation was found to be considerably affected when the SPR did not encompass the entire IV. SPR deficit of 3cm was found to induce up to 46% increase in the computed tube current value to be applied during the first tube rotations over lung apex. CONCLUSIONS: The tube current modulation mechanism functions properly only if the IV set by the operator is entirely included in the localizing SPR image. Operators should cautiously set the SPR boundaries to avoid partial exclusion of prescribed IV from SPRs and thus achieve optimum tube current modulation.

GPU-accelerated Monte Carlo simulation of MV-CBCT.

Monte Carlo simulation (MCS) is one of the most accurate computation methods for dose calculation and image formation in radiation therapy. However, the high computational complexity and long execution time of MCS limits its broad use. In this paper, we present a novel strategy to accelerate MCS using a graphic processing unit (GPU), and we demonstrate the application in mega-voltage (MV) cone-beam computed tomography (CBCT) simulation. A new framework that generates a series of MV projections from a single simulation run is designed specifically for MV-CBCT acquisition. A Geant4-based GPU code for photon simulation is incorporated into the framework for the simulation of photon transport through a phantom volume. The FastEPID method, which accelerates the simulation of MV images, is modified and integrated into the framework. The proposed GPU-based simulation strategy was tested for its accuracy and efficiency in a Catphan 604 phantom and an anthropomorphic pelvis phantom with beam energies at 2.5 MV, 6 MV, and 6 MV FFF. In all cases, the proposed GPU-based simulation demonstrated great simulation accuracy and excellent agreement with measurement and CPU-based simulation in terms of reconstructed image qualities. The MV-CBCT simulation was accelerated by factors of roughly 900-2300 using an NVIDIA Tesla V100 GPU card against a 2.5 GHz AMD Opteron™ Processor 6380.

A rapid, accurate image simulation strategy for mega-voltage cone-beam computed tomography.

Intensive computation time is required to simulate images of electronic portal imaging device (EPID) using Monte Carlo (MC) technique, limiting the development of applications associated with EPID, such as mega-voltage cone-beam computed tomography (MV-CBCT). In this study, a fast, accurate simulation strategy for MV-CBCT utilizing the FastEPID technique has been developed and validated. During FastEPID simulation, photon detection was determined by pre-calculated photon energy deposition efficiency (η) and particle transport within the EPID was replaced with a pre-calculated optical photon spread function. This method is capable of reducing the time required for EPID image simulation by a factor of 90-140, without compromising image quality. MV-CBCT images reconstructed from the FastEPID simulated projections have been validated against measurement in terms of mean Hounsfield unit (HU), noise, and cupping artifact. These images were obtained with both a Catphan 604 phantom and an anthropomorphic pelvis phantom, under treatment beam energies of 2.5 MV, 6 MV, and 6 MV flattening filter free. The agreement between measurement and simulation was excellent in all cases. This novel strategy was capable of reducing the run time of a full scan simulation of MV-CBCT performed on a CPU cluster to a matter of hours, rather than weeks or months required by a conventional approach. Multiple applications associated with MV-CBCT (e.g. imager design optimization) are anticipated to gain from the implementation of this novel simulation strategy.

Low-dose megavoltage cone-beam computed tomography using a novel multi-layer imager (MLI).

PURPOSE: The feasibility of low-dose megavoltage cone-beam acquisition (MVCBCT) using a novel, high detective quantum efficiency (DQE) multi-layer imager (MLI) was investigated. The aim of this work was to reconstruct MVCBCT images using the MLI at different total dose levels, and assess Hounsfield Unit (HU) accuracy, noise and contrast-to-noise ratio (CNR) for low-dose megavoltage cone-beam acquisition. METHODS: The MLI has four stacked layers; each layer contains a combination of copper filter/converter, gadolinium oxysulfide (GOS) scintillator and a-Si detector array. In total, 720 projections of a CATPHAN® phantom were acquired over 360° at 2.5, 6, and 6 MV flattening filter free (FFF) beam energies on a Varian TrueBeam LINAC. The dose per projection was 0.01, 0.0167, and 0.05 MU for 2.5, 6, and 6 MV FFF, respectively. MVCBCT images were reconstructed with varying numbers of projections to provide a range of doses for evaluation. Hounsfield Unit uniformity, accuracy, noise and CNR were estimated. Improvements were quantified relative to the standard AS1200 single-layer imager. RESULTS: Average HU uniformity for the MLI reconstructions was within a range of 95%-99% for all of the energies studied. Relative electron density estimation from HU values was within 0.4% ± 1.8% from nominal values. The CNR for MVCBCT based on MLI projections was 2-4× greater than from AS1200 projections. The 2.5 MV beam acquisition with the MLI exhibited the lowest noise and the best balance between CNR and dose for low-dose reconstructions. CONCLUSIONS: Megavoltage cone-beam acquisition imaging with a novel MLI prototype mounted on a clinical linear accelerator demonstrated substantial improvement over the standard AS1200 EPID. Further optimization of MVCBCT reconstruction, particularly for 2.5 MV acquisitions, will improve image metrics. Overall, the MLI improves CNR at substantially lower doses than currently required by conventional detectors. This new high DQE detector could provide high-quality MVCBCT at clinically acceptable doses.

Use of 3-D Contrast-Enhanced Ultrasound to Evaluate Tumor Microvasculature After Nanoparticle-Mediated Modulation.

A cost-effective method for serial in vivo imaging of tumor microvasculature has been developed. We evaluated acoustic angiography (AA) for visualizing and assessing non-small cell lung tumor (A549) microvasculature in mice before and after tumor vascular disruption by vascular-targeted gold nanoparticles and radiotherapy. Standard B-mode and microbubble-enhanced AA images were acquired at pre- and post-treatment time points. Using these modes, a new metric, 50% vessel penetration depth, was developed to characterize the 3-D spatial heterogeneity of microvascular networks. We observed an increase in tumor perfusion after radiation-induced vascular disruption, relative to control animals. This was also visualized in vessel morphology mode, which revealed a loss in vessel integrity. We found that tumors with poorly perfused vasculature at day 0 exhibited a reduced growth rate over time. This suggested a new method to reduce in-group treatment response variability using pre-treatment microvessel maps to objectively identify animals for study removal.

A novel method for fast image simulation of flat panel detectors.

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.

Characterizing a novel scintillating glass for application to megavoltage cone-beam computed tomography.

PURPOSE: The purpose of this study was to evaluate the performance of a prototype electric portal imaging device (EPID) with a high detective quantum efficiency (DQE) scintillator, LKH-5. Specifically, image quality in context of both planar and megavoltage (MV) cone-beam computed tomography (CBCT) is analyzed. METHODS: Planar image quality in terms of modulation transfer function (MTF), noise power spectrum (NPS), and DQE are measured and compared to an existing EPID (AS-1200) using the 6 MV beamline for a Varian TrueBeam linac. Imager performance is contextualized for three-dimensional (3D), MV-CBCT performance by measuring imager lag and analyzing the expected degradation of the DQE as a function of dose. Finally, comparisons between reconstructed images of the Catphan phantom in terms of qualitative quality and signal-difference-to-noise ratio (SDNR) are made for 6 MV images using both conventional and LKH-5 EPIDs as well as for the kilovoltage (kV) on-board imager (OBI). RESULTS: Analysis of the NPS reveals linearity at all measured doses using the prototype LKH-5 detector. While the first zero of the MTF is much lower for the LKH-5 detector than the conventional EPID (0.6 cycles/mm vs 1.6 cycles/mm), the normalized NPS (NNPS) multiplied by total quanta (qNNPS) of the LKH-5 detector is roughly a factor of seven to eight times lower, yielding a DQE(0) of approximately 8%. First, second, and third frame lag were measured at approximately 23%, 5%, and 1%, respectively, although no noticeable image artifacts were apparent in reconstructed volumes. Analysis of low-dose performance reveals that DQE(0) remains at 80% of its maximum value at a dose as low as 7.5 × 10-6  MU. For a 400 projection technique, this represents a total scan dose of 0.0030 MU, suggesting that if imaging doses are increased to a value typical of kV-CBCT scans (~2.7 cGy), the LKH-5 detector will retain quantum noise limited performance. Finally, comparing Catphan scans, the prototype detector exhibits much lower image noise than the conventional EPID, resulting in improved small object representation. Furthermore, SDNR of H2 O and polystyrene cylinders improved from -1.95 and 2.94 to -15 and 18.7, respectively. CONCLUSIONS: Imaging performance of the prototype LKH-5 detector was measured and analyzed for both planar and 3D contexts. Improving noise transfer of the detector results in concurrent improvement of DQE(0). For 3D imaging, temporal characteristics were adequate for artifact-free performance and at relevant doses, the detector retained quantum noise limited performance. Although quantitative MTF measurements suggest poorer resolution, small object representation of the prototype imager is qualitatively improved over the conventional detector due to the measured reduction in noise.

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

We have developed a Monte Carlo computational model of a clinically employed electronic portal imaging device (EPID), and demonstrated the impact of phosphor optical properties on the imaging performance. The EPID model was built with Geant4 application for tomographic emission. Both radiative and optical transport were included in the model. Modulation transfer function (MTF), normalized noise-power spectrum times the incident x-ray fluence (qNNPS), and detective quantum efficiency (DQE) were calculated for simulated and measured data, and their agreement was quantified by the normalized root-mean-square error (NRMSE). MTF was computed using a 100 µm wide slit tilted by 1.5° and qNNPS was estimated using the Fujita-Lubberts-Swank method. DQE was calculated from MTF and qNNPS data. The NRMSE value was 0.0467 for MTF, 0.0217 for qNNPS, and 0.0885 for DQE, showing good agreement between measurement and simulation. Five major optical properties, phosphor grain size, phosphor thickness, phosphor refractive index, binder refractive index, and packing ratio were tested for their influence on the qNNPS, MTF, and DQE(0) of the model. Generally, the effect on the qNNPS is greater than MTF, and no impact on DQE(0), except from phosphor thickness, was observed. Multiple applications, such as imager design optimization and investigations of the dosimetric performance, are expected to benefit from the validated model.

Feasibility of closed-MLC tracking using high sensitivity and multi-layer electronic portal imagers.

In radiation therapy, improvements in treatment conformality are often limited by movement of target tissue. To better treat the target, tumor tracking strategies involving beam's-eye-view (BEV) have been explored. However, localization surrogates like implanted fiducial markers may sometimes leave the field-of-view (FOV), as defined by the linear accelerator (LINAC) multi-leaf collimator (MLC). Radiation leakage through the MLC has been measured previously at approximately 1%-2%. High sensitivity prototype detectors imagers may improve the ability to visualize objects outside of the MLC FOV during treatment. The present study presents a proof-of-concept for tracking fiducial markers outside the MLC FOV by employing high sensitivity detectors using a high-efficiency, prototype scintillating glass called LKH-5 and also investigates the impact of multi-layer imager (MLI) architecture. It was found that by improving the detector efficiency, using either of these methods results in a reduction of dose required for fiducial marker visibility. Further, image correction by a rectangular median filter will improve fiducial marker representation in the MLC blocked images. Quantified by measuring the peak-to-sidelobe ratio (PSR) of the normalized cross correlation (NCC) between a template of the fiducial marker with the blocked MLC acquisition, visibility has been found at a threshold of roughly 5 for all configurations with a 3 × 3 cm2 ROI. For typical gadolinium oxysulfide (GOS) detectors in single and simulated 4-layer configurations, the minimum dose required for visualization was 20 and 10 MU, respectively. For LKH-5 detectors in single and simulated 4-layer configurations, this minimum dose was reduced to 4 and 2 MU, respectively. With a 6 MV flattening filter free (FFF) beam dose rate of 1400 MU min-1, the maximum detector frame rate while maintaining fiducial visibility is approximately 12 fps for a 4-layer LKH-5 configuration.