Scatter Correction for Industrial Cone-Beam Computed Tomography (CBCT) Using VSHARP, a fast GPU-Based Linear Boltzmann Transport Equation Solver

Epidemiological evidence suggests an increased risk of cancer related to computed tomography (CT) scans, with children exposed to greater risk. The purpose of this work is to test the reliability of a linear Boltzmann transport equation (LBTE) solver for rapid and patient-specific CT dose estimation. This includes building a flexible LBTE framework for modeling modern clinical CT scanners and to validate the resulting dose maps across a range of realistic scanner configurations and patient models. In this study, computational tools were developed for modeling CT scanners, including a bowtie filter, overrange collimation, and tube current modulation. The LBTE solver requires discretization in the spatial, angular, and spectral dimensions, which may affect the accuracy of scanner modeling. To investigate these effects, this study evaluated the LBTE dose accuracy for different discretization parameters, scanner configurations, and patient models (male, female, adults, pediatric). The method used to validate the LBTE dose maps was the Monte Carlo code Geant4, which provided ground truth dose maps. LBTE simulations were implemented on a GeForce GTX 1080 graphic unit, while Geant4 was implemented on a distributed cluster of CPUs. The agreement between Geant4 and the LBTE solver quantifies the accuracy of the LBTE, which was similar across the different protocols and phantoms. The results suggest that 18 views per rotation provides sufficient accuracy, as no significant improvement in the accuracy was observed by increasing the number of projection views. Considering this discretization, the LBTE solver average simulation time was approximately 30s. However, in the LBTE solver the phantom model was implemented with a lower voxel resolution with respect to Geant4, as it is limited by the memory of the GPU. Despite this discretization, the results showed a good agreement between the LBTE and Geant4, with root mean square error of the dose in organs of approximately 3.5% for most of the studied configurations. The LBTE solver is proposed as an alternative to Monte Carlo for patient-specific organ dose estimation. This study demonstrated accurate organ dose estimates for the rapid LBTE solver when considering realistic aspects of CT scanners and a range of phantom models. Future plans will combine the LBTE framework with deep learning autosegmentation algorithms to provide near real-time patient-specific organ dose estimation.

Acuros external beam (Acuros XB) is a novel dose calculation algorithm implemented through the ECLIPSE treatment planning system. The algorithm finds a deterministic solution to the linear Boltzmann transport equation, the same equation commonly solved stochastically by Monte Carlo methods. This work is an evaluation of Acuros XB, by comparison with Monte Carlo, for dose calculation applications involving high-density materials. Existing non-Monte Carlo clinical dose calculation algorithms, such as the analytic anisotropic algorithm (AAA), do not accurately model dose perturbations due to increased electron scatter within high-density volumes. Acuros XB, AAA, and EGSnrc based Monte Carlo are used to calculate dose distributions from 18 MV and 6 MV photon beams delivered to a cubic water phantom containing a rectangular high density (4.0-8.0 g/cm(3)) volume at its center. The algorithms are also used to recalculate a clinical prostate treatment plan involving a unilateral hip prosthesis, originally evaluated using AAA. These results are compared graphically and numerically using gamma-index analysis. Radio-chromic film measurements are presented to augment Monte Carlo and Acuros XB dose perturbation data. Using a 2% and 1 mm gamma-analysis, between 91.3% and 96.8% of Acuros XB dose voxels containing greater than 50% the normalized dose were in agreement with Monte Carlo data for virtual phantoms involving 18 MV and 6 MV photons, stainless steel and titanium alloy implants and for on-axis and oblique field delivery. A similar gamma-analysis of AAA against Monte Carlo data showed between 80.8% and 87.3% agreement. Comparing Acuros XB and AAA evaluations of a clinical prostate patient plan involving a unilateral hip prosthesis, Acuros XB showed good overall agreement with Monte Carlo while AAA underestimated dose on the upstream medial surface of the prosthesis due to electron scatter from the high-density material. Film measurements support the dose perturbations demonstrated by Monte Carlo and Acuros XB data. Acuros XB is shown to perform as well as Monte Carlo methods and better than existing clinical algorithms for dose calculations involving high-density volumes.

PurposeTo describe Acuros® CTS, a new software tool for rapidly and accurately estimating scatter in x-ray projection images by deterministically solving the linear Boltzmann transport equation (LBTE).MethodsThe LBTE describes the behavior of particles as they interact with an object across spatial, energy, and directional (propagation) domains. Acuros CTS deterministically solves the LBTE by modeling photon transport associated with an x-ray projection in three main steps: 1) Ray tracing photons from the x-ray source into the object where they experience their first scattering event and form scattering sources. 2) Propagating photons from their first scattering sources across the object in all directions to form second scattering sources, then repeating this process until all high order scattering sources are computed by using the Source Iteration method. 3) Ray tracing photons from scattering sources within the object to the detector, accounting for the detector’s energy and anti-scatter grid responses.To make this process computationally tractable, a combination of analytical and discrete methods is applied. The three domains are discretized using the Linear Discontinuous Finite Elements, Multigroup, and Discrete Ordinates methods, respectively, which confer the ability to maintain the accuracy of a continuous solution. Furthermore, through implementation in CUDA, we sought to exploit the parallel computing capabilities of graphics processing units (GPUs) to achieve the speeds required for clinical utilization.Acuros CTS was validated against Geant4 Monte Carlo simulations using two digital phantoms: 1) a water phantom containing lung, air, and bone inserts (WLAB phantom) and 2) a pelvis phantom derived from a clinical CT data set. For these studies, we modeled the TrueBeam® (Varian Medical Systems, Palo Alto, CA) kV imaging system with a source energy of 125kVp. The imager comprised a 600 μm-thick Cesium Iodide (CsI) scintillator and a 10:1 one-dimensional anti-scatter grid. For the WLAB studies, the full-fan geometry without a bowtie filter was used (with and without the anti-scatter grid). For the pelvis phantom studies, a half-fan geometry with bowtie was used (with the anti-scatter grid). Scattered and primary photon fluences and energies deposited in the detector were recorded.ResultsThe Acuros CTS and Monte Carlo results demonstrated excellent agreement. For the WLAB studies, the average percent difference between the Monte Carlo- and Acuros-generated scattered photon fluences at the face of the detector was −0.7%. After including the detector response, the average percent differences between the Monte Carlo- and Acuros- generated scatter fractions (SF) were −0.1% without the grid and 0.6% with the grid. For the digital pelvis simulation, the Monte Carlo- and Acuros-generated SFs agreed to within 0.1% on average, despite the scatter-to-primary ratios (SPRs) being as high as 5.5. The Acuros CTS computation time for each scatter image was ~1 second using a single GPU.ConclusionsAcuros CTS enables a fast and accurate calculation of scatter images by deterministically solving the LBTE thus offering a computationally attractive alternative to Monte Carlo methods. Part II describes the application of Acuros CTS to scatter correction of CBCT scans on the TrueBeam system.

In recent years, cone-beam computed tomography (CBCT) has played an important role in medical imaging. However, the applications of CBCT are limited due to the severe scatter contamination. Conventional Monte Carlo (MC) simulation can provide accurate scatter estimation for scatter correction, but the expensive computational cost has always been the bottleneck of MC method in clinical application. In this work, an MC simulation method combined with a variance reduction technique called correlated sampling is proposed for fast iterative scatter correction. Correlated sampling exploits correlation between similar simulation systems to reduce the variance of interest quantities. Specifically, conventional MC simulation is first performed on the scatter-contaminated CBCT to generate the initial scatter signal. In the subsequent correction iterations, scatter estimation is then updated by applying correlated MC sampling to the latest corrected CBCT images by reusing the random number sequences of the task-related photons in conventional MC. Afterward, the corrected projections obtained by subtracting the scatter estimation from raw projections are utilized for FDK reconstruction. These steps are repeated until an adequate scatter correction is obtained. The performance of the proposed framework is evaluated by the accuracy of the scatter estimation, the quality of corrected CBCT images and efficiency. Overall, the difference in mean absolute percentage error between scatter estimation with and without correlated sampling is 0.25% for full-fan case and 0.34% for half-fan case, respectively. In simulation studies, scatter artifacts are substantially eliminated, where the mean absolute error value is reduced from 15 to 2HU in full-fan case and from 53 to 13HU in half-fan case. Scatter-to-primary ratio is reduced to 0.02 for full-fan and 0.04 for half-fan, respectively. In phantom study, the contrast-to-noise ratio (CNR) is increased by a factor of 1.63, and the contrast is increased by a factor of 1.77. As for clinical studies, the CNR is improved by 11% and 14% for half-fan and full-fan, respectively. The contrast after correction is increased by 19% for half-fan and 44% for full-fan. Furthermore, root mean square error is also effectively reduced, especially from 78 to 4HU for full-fan. Experimental results demonstrate that the figure of merit is improved between 23 and 43 folds when using correlated sampling. The proposed method takes less than 25s for the whole iterative scatter correction process. The proposed correlated sampling-based MC simulation method can achieve fast and accurate scatter correction for CBCT, making it suitable for real-time clinical use.

In this work Monte Carlo (MC) simulations are used to correct kilovoltage (kV) cone-beam computed tomographic (CBCT) projections for scatter radiation. All images were acquired using a kV CBCT bench-top system composed of an x-ray tube, a rotation stage and a flat-panel imager. The EGSnrc MC code was used to model the system. BEAMnrc was used to model the x-ray tube while a modified version of the DOSXYZnrc program was used to transport the particles through various phantoms and score phase space files with identified scattered and primary particles. An analytical program was used to read the phase space files and produce image files. The scatter correction was implemented by subtracting Monte Carlo predicted scatter distribution from measured projection images; these projection images were then reconstructed. Corrected reconstructions showed an important improvement in image quality. Several approaches to reduce the simulation time were tested. To reduce the number of simulated scatter projections, the effect of varying the projection angle on the scatter distribution was evaluated for different geometries. It was found that the scatter distribution does not vary significantly over a 30-degree interval for the geometries tested. It was also established that increasing the size of the voxels in the voxelized phantom does not affect the scatter distribution but reduces the simulation time. Different techniques to smooth the scatter distribution were also investigated.

UBES: Unified scatter correction using ultrafast Boltzmann equation solver for conebeam CT

The aim of this study is to remove scattered photons and beam hardening effect in cone beam CT (CBCT) images and make an image available for treatment planning. To remove scattered photons and beam hardening effect, a convolutional neural network (CNN) was used, and trained with distorted projection data including scattered photons and beam hardening effect and supervised projection data calculated with monochromatic X-rays. The number of training projection data was 17,280 with data augmentation and that of test projection data was 540. The performance of the CNN was investigated in terms of the number of photons in the projection data used in the training of the network. Projection data of pelvic CBCT images (32 cases) were calculated with a Monte Carlo simulation with six different count levels ranging from 0.5 to 3 million counts/pixel. For the evaluation of corrected images, the peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and the sum of absolute difference (SAD) were used. The results of simulations showed that the CNN could effectively remove scattered photons and beam hardening effect, and the PSNR, the SSIM, and the SAD significantly improved. It was also found that the number of photons in the training projection data was important in correction accuracy. Furthermore, a CNN model trained with projection data with a sufficient number of photons could yield good performance even though a small number of photons were used in the input projection data.

Kilovoltage (kV) cone beam computed tomography (CBCT) images suffer from a substantial scatter contribution. In this study, Monte Carlo (MC) simulations are used to evaluate the scattered radiation present in projection images. These predicted scatter distributions are also used as a scatter correction technique. Images were acquired using a kV CBCT bench top system. The EGSnrc MC code was used to model the flat panel imager, the phantoms, and the x-ray source. The x-ray source model was validated using first and second half-value layers (HVL) and profile measurements. The HVLs and the profile were found to agree within 3% and 6%, respectively. MC simulated and measured projection images for a cylindrical water phantom and for an anthropomorphic head phantom agreed within 8% and 10%. A modified version of the DOSXYZnrc MC code was used to score phase space files with identified scattered and primary particles behind the phantoms. The cone angle, the source-to-detector distance, the phantom geometry, and the energy were varied to determine their effect on the scattered radiation distribution. A scatter correction technique was developed in which the MC predicted scatter distribution is subtracted from the projections prior to reconstruction. Preliminary testing of the procedure was done with an anthropomorphic head phantom and a contrast phantom. Contrast and profile measurements were obtained for the scatter corrected and noncorrected images. An improvement of 3% for contrast between solid water and a liver insert and 11% between solid water and a Teflon insert were obtained and a significant reduction in cupping and streaking artifacts was observed.

X-ray scatter results in a significant degradation of image quality in computed tomography (CT), representing a major limitation in cone-beam CT (CBCT) and large field-of-view diagnostic scanners. In this work, a novel scatter estimation and correction technique is proposed that utilizes peripheral detection of scatter during the patient scan to simultaneously acquire image and patient-specific scatter information in a single scan, and in conjunction with a proposed compressed sensing scatter recovery technique to reconstruct and correct for the patient-specific scatter in the projection space. The method consists of the detection of patient scatter at the edges of the field of view (FOV) followed by measurement based compressed sensing recovery of the scatter through-out the projection space. In the prototype implementation, the kV x-ray source of the Varian TrueBeam OBI system was blocked at the edges of the projection FOV, and the image detector in the corresponding blocked region was used for scatter detection. The design enables image data acquisition of the projection data on the unblocked central region of and scatter data at the blocked boundary regions. For the initial scatter estimation on the central FOV, a prior consisting of a hybrid scatter model that combines the scatter interpolation method and scatter convolution model is estimated using the acquired scatter distribution on boundary region. With the hybrid scatter estimation model, compressed sensing optimization is performed to generate the scatter map by penalizing the L1 norm of the discrete cosine transform of scatter signal. The estimated scatter is subtracted from the projection data by soft-tuning, and the scatter-corrected CBCT volume is obtained by the conventional Feldkamp-Davis-Kress algorithm. Experimental studies using image quality and anthropomorphic phantoms on a Varian TrueBeam system were carried out to evaluate the performance of the proposed scheme. The scatter shading artifacts were markedly suppressed in the reconstructed images using the proposed method. On the Catphan©504 phantom, the proposed method reduced the error of CT number to 13 Hounsfield units, 10% of that without scatter correction, and increased the image contrast by a factor of 2 in high-contrast regions. On the anthropomorphic phantom, the spatial nonuniformity decreased from 10.8% to 6.8% after correction. A novel scatter correction method, enabling unobstructed acquisition of the high frequency image data and concurrent detection of the patient-specific low frequency scatter data at the edges of the FOV, is proposed and validated in this work. Relative to blocker based techniques, rather than obstructing the central portion of the FOV which degrades and limits the image reconstruction, compressed sensing is used to solve for the scatter from detection of scatter at the periphery of the FOV, enabling for the highest quality reconstruction in the central region and robust patient-specific scatter correction.

Purpose: To quantitatively analyze the image quality of cone-beam CT(CBCT) imaging using a robust image evaluation metric, a clinical analogue of detective quantum efficiency (DQE), to allow for image assessment under variable acquisition parameters. Methods: CBCT image quality assessment, especially between different systems, remains semi-quantitative and lacks the fundamental metrics found in 2D x-ray imaging. Clinical DQE(cDQE) is based on measuring modulation transfer function (MTF) and noise power spectrum(NPS) using a cylindrical acrylic phantom containing modules with line-pairs of various spatial frequencies, and uniform section. Imaging was acquired using a linac based CBCT (Elekta XVI) and a commercial cylindrical imaging QA phantom (Phantom Laboratory Catphan 500). CBCT imaging was obtained with various acquisition settings for collimation size (small S20, medium M20 field-of-views), beam filter (with and without bow-tie filter), and adjustable scatter-to-primary ratios(SPRs). Incident beam fluence was determined based on dose measurements, and fluence-dose conversion ratio computed by Monte Carlo simulations. A standard vendor supplied reconstruction algorithm was used with various SPRs (0, 0.1, 0.2, 0.33). Results: Use of bow-tie filter improved CBCT image quality, as measured by cDQE, by 42% compared to imaging with neutral filter. As expected, cDQE confirmed that small collimation produced superior imaging compared to medium collimation due to reduced scattering. Interestingly, CBCT images reconstructed under various SPRs (0, 0.1, 0.2, 0.33) showed no significant difference in cDQE values. The results of cDQE calculations were consistent with evaluations of the phantom imaging based on traditional contrast-to-noise ratio, spatial resolution visibility, and qualitative observer assessment. Conclusion: cDQE was shown to be an effective metric to assess CBCT imaging under variable acquisition parameters. The proposed approach can be extended to other imaging phantoms and CBCTs, and has significant potential as a QA tool in monitoring and optimizing the imaging performance of CBCT systems for clinical use.

Scatter contamination constitutes a dominant source of degradation of image quality in cone-beam computed tomography (CBCT). We have recently developed an analytic image reconstruction method with a scatter correction capability from the partially blocked cone-beam data out of a single scan. Despite its easy implementation and its computational efficiency, the developed method may result in additional image artifacts for a large cone angle geometry due to data inconsistency. To improve the image quality at a large cone angle, we propose a weighted rebinned backprojection-filtration (wrBPF) algorithm in conjunction with a hybrid type scatter correction approach. The proposed method uses a beam-blocker array that provides partial data for scatter correction and image reconstruction and that only blocks the beam within a limited cone angle. Thisdesign allows a chance to keep the image quality at larger cone angles by use of data redundancy since the projection data corresponding to larger cone angles are not blocked. However, the scatter correction would not be straightforward. In order to correct for the scatter in the projections at larger cone angles, we propose a novel scatter correction method combining a measurement-based and a convolution-based method. We first estimated the scatter signal using a measurement-based method in the partially beam-blocked regions, and then optimized the fitting parameters of a convolution-kernel that can be used for scatter correction in the projections at larger cone angles. For image reconstruction, we developed a wrBPF with butterfly filtering. We have conducted an experimental study to validate the proposed algorithm for image reconstruction and scatter correction. The experimental results revealed that the developed reconstruction method makes full use of the benefits of partial beam-blocking for scatter correction and image reconstruction and at the same time enhances image quality at larger cone angles by use of an optimized convolution-based scatter correction. The proposed method that enjoys the advantages of both measurement-based and convolution-based methods for scatter correction has successfully demonstrated its capability of reconstructing accurate images out of a single scan in circular CBCT.

To develop and evaluate a fast and accurate Monte Carlo simulation (MCS) based scatter correction for PET/MR imaging, based on Geant4 using graphical processing units (GPUs). The GPU implementation by Bert et al. [1] including the photon tracking within a voxelized volume was extended with the modeling of the detection process in the Siemens MR-BrainPET. Using NVIDIA’s CUDA photon pairs undergo several kernel functions in parallel, from their generation according to the activity distribution up to their detection in the scanner. Subsequently, coincidences are sorted and stored in list-mode. The new GPU implementation (GeForce GTX690) was validated by comparison with Gate v6.2 on a single CPU (Intel Core i7, 3.4GHz), showing a speedup factor of 135. The use of multiple GPUs can further increase this acceleration. For Compton scatter correction the reconstructed emission image, the patient MR-derived attenuation map and a CT-derived attenuation template of the MR coils are used as input for the simulation. The resulting simulated projection data are scaled to the normalized measured projection data. Thus, the often unstable fit to the distribution tails, necessary for single scatter simulation (SSS)[2], is circumvented. The scaled simulated scatter distribution is then incorporated in the reconstruction. The proposed scatter correction methodology was evaluated and compared to the SSS approach using sphere phantom measurements. Finally, the effect on patient data was investigated. Phantom results suggest an improved contrast with our new MC-based scatter correction, especially for measurements including the MR coils, where the contrast recovery was improved by 10-14%. First patient images also show improved grey/white matter contrast. Table 1 Comparison of recovery coefficients after scatter correction based on MCS and SSS. MC-based scatter correction shows improved recovery coefficients for the hot spheres. Figure 1 Assessment of image quality according to NEMA NU 2-2007. Images show the phantom with a hot spheres/background ratio of 8:1 measured with MR head coils inside the BrainPET scanner. Figure 2 Comparison of reconstructed FDG images using the scatter estimate provided by the vendor’s SSS (left) and our MCS method (right). Images were reconstructed using the vendor provided 3D OP-OSEM algorithm with 2 subsets and 32 iterations. The new GPU-accelerated MCS allows for fast and accurate estimation of scattered coincidences in PET studies using the Siemens MR-BrainPET with a major speed-up compared to standard MCS and superior image quality compared with the standard correction based on SSS.

Purpose: Scatter contamination in cone-beam computed tomography (CBCT) degrades the image quality by introducing shading artifacts. A moving-blocker-based approach has been proposed to simultaneously estimate scatter and reconstruct the complete volume within field of view (FOV) from a single CBCT scan, where promising results were obtained from simulation studies. In this work, we experimentally demonstrated the effectiveness of the moving-blocker-based scatter correction approach by implementing a moving blocker system on a LINAC on-board kV CBCT imaging system. Methods: A physical attenuator (i.e., “blocker”) consisting equal spaced lead strips was mounted on a linear actuator. A step motor connected to the actuator drove the blocker to move back and forth along gantry rotation axis during the CBCT acquisition. Scatter signal was estimated from the blocked region of imaging panel, and interpolated into the un-blocked region. A sparseness prior based statistical iterative reconstruction algorithm was used to reconstruct CBCT images from unblocked projections after the scatter signal was subtracted. Experimental studies were performed on both a Catphan phantom and an anthropomorphic pelvic phantom to evaluate performance of the moving blocker system. Results: The scatter-induced shading artifacts were substantially reduced in the images acquired with the moving blocker system. CT number error in selected regions of interest of reduced from 318 to 17 and from 23 9 to 10 for the Catphan phantom and pelvic phantom, respectively. Conclusion: We demonstrated for the first time that the moving blocker system can successfully estimate the scatter signal in projection data, reduce the imaging dose and obtain complete volumetric information within the FOV using a single scan.

Cone-beam computed tomography (CBCT) has the notable features of high efficiency and high precision, and is widely used in areas such as medical imaging and industrial non-destructive testing. However, the presence of the ray scatter reduces the quality of CT images. By referencing the slit collimation approach, a scatter correction method for CBCT based on the interlacing-slit scan is proposed. Firstly, according to the characteristics of CBCT imaging, a scatter suppression plate with interlacing slits is designed and fabricated. Then the imaging of the scatter suppression plate is analyzed, and a scatter correction calculation method for CBCT based on the image fusion is proposed, which can splice out a complete set of scatter suppression projection images according to the interlacing-slit projection images of the left and the right imaging regions in the scatter suppression plate, and simultaneously complete the scatter correction within the flat panel detector (FPD). Finally, the overall process of scatter suppression and correction is provided. The experimental results show that this method can significantly improve the clarity of the slice images and achieve a good scatter correction.

Validation of a TOPAS cone beam computed tomography (CBCT) Monte Carlo model towards personalised CBCT dosimetry in interventional radiology (IR).