Purpose: To improve CBCT image quality for image‐guided radiotherapy by applying advanced reconstruction algorithms to overcome scatter, noise, and artifact limitations Methods: CBCT is used extensively for patient setup in radiotherapy. However, image quality generally falls short of diagnostic CT, limiting soft‐tissue based positioning and potential applications such as adaptive radiotherapy. The conventional TrueBeam CBCT reconstructor uses a basic scatter correction and FDK reconstruction, resulting in residual scatter artifacts, suboptimal image noise characteristics, and other artifacts like cone‐beam artifacts. We have developed an advanced scatter correction that uses a finite‐element solver (AcurosCTS) to model the behavior of photons as they pass (and scatter) through the object. Furthermore, iterative reconstruction is applied to the scatter‐corrected projections, enforcing data consistency with statistical weighting and applying an edge‐preserving image regularizer to reduce image noise. The combined algorithms have been implemented on a GPU. CBCT projections from clinically operating TrueBeam systems have been used to compare image quality between the conventional and improved reconstruction methods. Planning CT images of the same patients have also been compared. Results: The advanced scatter correction removes shading and inhomogeneity artifacts, reducing the scatter artifact from 99.5 HU to 13.7 HU in a typical pelvis case. Iterative reconstruction provides further benefit by reducing image noise and eliminating streak artifacts, thereby improving soft‐tissue visualization. In a clinical head and pelvis CBCT, the noise was reduced by 43% and 48%, respectively, with no change in spatial resolution (assessed visually). Additional benefits include reduction of cone‐beam artifacts and reduction of metal artifacts due to intrinsic downweighting of corrupted rays. Conclusion: The combination of an advanced scatter correction with iterative reconstruction substantially improves CBCT image quality. It is anticipated that clinically acceptable reconstruction times will result from a multi‐GPU implementation (the algorithms are under active development and not yet commercially available). All authors are employees of and (may) own stock of Varian Medical Systems.

Purpose: Beams‐eye‐view imaging applications such as real‐time soft‐tissue motion estimation and MV‐CBCT are hindered by the inherently low image contrast of electronic portal imaging devices (EPID) currently in clinical use. We investigate a cost effective scintillating glass that provides substantially increased detective quantum efficiency (DQE) and contrast to noise ratio (CNR). Methods: A pixelated scintillator prototype was built from LKH‐5 glass. The array is 12mm thick; 42.4×42.4cm2 wide and features 1.51mm pixel pitch with 20µm separation (glue+septa). The LKH‐5 array was mounted on the active matrix flat panel imager (AMPFI) of an AS‐1200 (Varian) with the GdO2S2:Tb removed. A second AS‐1200 was utilized as reference detector. The prototype EPID was characterized in terms of CNR, modulation transfer function (MTF) and DQE. Additionally, the visibility of various fiducial markers typically used in the clinic as well as a realistic 3D‐printed lung tumor model was assessed. All items were placed in a 12cm thick solid water phantom. CNR is estimated using a Las Vegas contrast phantom, presampled MTF is estimated using a slanted slit technique and the DQE is calculated from measured normalized noise power spectra (NPS) and the MTF. Results: DQE(0) for the LKH‐5 prototype increased by a factor of 8× to about 10%, compared to the AS‐1200 equipped with its standard GdO2S2:Tb scintillator. CNR increased by a factor of 5.3×. Due to the pixel size the MTF50 decreased by about 55% to 0.23lp/mm. The visibility of all fiducial markers as well as the tumor model were however markedly improved in comparison to an acquisition with the same parameters using the GdO2S2:Tb scintillator. Conclusion: LKH‐5 scintillating glasses allow the cost effective construction of thick pixelated scintillators for portal imaging which can yield a substantial increase in DQE and CNR. Soft tissue and fiducial marker visibility was found to be markedly improved. The project was supported in part by NIH grant R01CA188446‐01 and a grant from Varian Medical Systems.

Purpose: To develop a rapid and accurate software tool for computing patient‐specific radiation dose maps of dose delivered from kV computed tomography (CT) scans. Methods: Monte Carlo methods currently provide the gold‐standard for calculating patient‐specific dose maps, but require immense computational resources to achieve sufficiently high statistical accuracy. To overcome this limitation, a deterministic method was implemented to solve the same underlying Boltzmann transport equation (BTE) that governs particle interactions and transport. Phase‐space was discretized according to spatial location, energy, and angle, and a deterministic finite element algorithm was applied to compute the object's photon fluence distribution, which does not exhibit stochastic noise. A computationally efficient GPU implementation for a standard workstation was developed, and comparison was made between the performance of the deterministic BTE solver and a standard Monte Carlo algorithm for a cone‐beam projection of a virtual anthropomorphic chest phantom. Results: The BTE solution and Monte Carlo results were in strong agreement with a relative root‐mean square error (RMSE) of 3.47%. Some larger differences existed at high‐contrast boundaries (e.g., air/water) and within the bone, and are under further investigation. Notably, the computation time of the BTE solver was 8 seconds, while to obtain the same level of statistical uncertainty with conventional Monte Carlo required 1200 CPU‐hours. Additionally, unlike Monte Carlo, the BTE computation time is only weakly dependent on the number of sources, making it extremely well‐suited for CT dose calculations. Therefore, the BTE‐based method is expected to offer a >30,000x speed increase compared to Monte Carlo for entire CT scans, even after application of variance reduction techniques and GPU implementation. Conclusion: The novel deterministic BTE solver offers a significantly faster alternative to Monte Carlo‐based methods for computing dose delivered by CT scans, which can enable estimation of patient‐specific organ doses for each CT examination performed. Adam Wang, Alex Maslowski, Todd Wareing, and Josh Star‐Lack are employees of Varian Medical Systems.

Purpose: The purpose of this study was to validate the use of a cascaded linear system model for MV cone‐beam CT (CBCT) using a multi‐layer (MLI) electronic portal imaging device (EPID) and provide experimental insight into image formation. A validated 3D model provides insight into salient factors affecting reconstructed image quality, allowing potential for optimizing detector design for CBCT applications. Methods: A cascaded linear system model was developed to investigate the potential improvement in reconstructed image quality for MV CBCT using an MLI EPID. Inputs to the three‐dimensional (3D) model include projection space MTF and NPS. Experimental validation was performed on a prototype MLI detector installed on the portal imaging arm of a Varian TrueBeam radiotherapy system. CBCT scans of up to 898 projections over 360 degrees were acquired at exposures of 16 and 64 MU. Image volumes were reconstructed using a Feldkamp‐type (FDK) filtered backprojection (FBP) algorithm. Flat field images and scans of a Catphan model 604 phantom were acquired. The effect of 2×2 and 4×4 detector binning was also examined. Results: Using projection flat fields as an input, examination of the modeled and measured NPS in the axial plane exhibits good agreement. Binning projection images was shown to improve axial slice SDNR by a factor of approximately 1.4. This improvement is largely driven by a decrease in image noise of roughly 20%. However, this effect is accompanied by a subsequent loss in image resolution. Conclusion: The measured axial NPS shows good agreement with the theoretical calculation using a linear system model. Binning of projection images improves SNR of large objects on the Catphan phantom by decreasing noise. Specific imaging tasks will dictate the implementation image binning to two‐dimensional projection images. The project was partially supported by a grant from Varian Medical Systems, Inc. and grant No. R01CA188446‐01 from the National Cancer Institute.