fastCAT: Fast cone beam CT (CBCT) simulation

A simulation framework for preclinical proton irradiation workflow

Objective.The integration of proton beamlines with x-ray imaging/irradiation platforms has opened up possibilities for image-guided Bragg peak irradiations in small animals. Such irradiations allow selective targeting of normal tissue substructures and tumours. However, their small size and location pose challenges in designing experiments. This work presents a simulation framework useful for optimizing beamlines, imaging protocols, and design of animal experiments. The usage of the framework is demonstrated, mainly focusing on the imaging part.Approach.The fastCAT toolkit was modified with Monte Carlo (MC)-calculated primary and scatter data of a small animal imager for the simulation of micro-CT scans. The simulated CT of a mini-calibration phantom from fastCAT was validated against a full MC TOPAS CT simulation. A realistic beam model of a preclinical proton facility was obtained from beam transport simulations to create irradiation plans in matRad. Simulated CT images of a digital mouse phantom were generated using single-energy CT (SECT) and dual-energy CT (DECT) protocols and their accuracy in proton stopping power ratio (SPR) estimation and their impact on calculated proton dose distributions in a mouse were evaluated.Main results.The CT numbers from fastCAT agree within 11 HU with TOPAS except for materials at the centre of the phantom. Discrepancies for central inserts are caused by beam hardening issues. The root mean square deviation in the SPR for the best SECT (90 kV/Cu) and DECT (50 kV/Al-90 kV/Al) protocols are 3.7% and 1.0%, respectively. Dose distributions calculated for SECT and DECT datasets revealed range shifts <0.1 mm, gamma pass rates (3%/0.1 mm) greater than 99%, and no substantial dosimetric differences for all structures. The outcomes suggest that SECT is sufficient for proton treatment planning in animals.Significance.The framework is a useful tool for the development of an optimized experimental configuration without using animals and beam time.

Monte Carlo methods for medical imaging research

In radiation-based medical imaging research, computational modeling methods are used to design and validate imaging systems and post-processing algorithms. Monte Carlo methods are widely used for the computational modeling as they can model the systems accurately and intuitively by sampling interactions between particles and imaging subject with known probability distributions. This article reviews the physics behind Monte Carlo methods, their applications in medical imaging, and available MC codes for medical imaging research. Additionally, potential research areas related to Monte Carlo for medical imaging are discussed.

A rotating beam-blocker method for cone beam CT scatter correction

BACKGROUND

Cone beam CT (CBCT) is widely utilized in clinics. However, the scatter artifact degrades the CBCT image quality, hampering the expansion of CBCT applications. Recently, beam-blocker methods have been used for CBCT scatter correction and proved their high cost-effectiveness.

PURPOSE

A rotating beam-blocker (RBB) method for CBCT scatter correction was proposed to complete scatter correction and image reconstruction within a single scan in both full- and half-fan scan scenarios.

METHODS

The RBB consisted of two open regions and two blocked regions, and was designed as a centrosymmetric structure. The open and blocked projections could be alternatively obtained within one single rotation. The open projections were corrected with the scatter signal calculated from the blocked projections, and then used to reconstruct the 3D image via the Feldkamp-Davis-Kress algorithm. The performance of the RBB method was evaluated on head and pelvis phantoms in scenarios with and without a bowtie filter. The images obtained from nine repeated scans in each scenario were used to calculate the evaluation metrics including the CT number error, spatial nonuniformity (SNU) and contrast-to-noise ratio (CNR).

RESULTS

For the head phantom, the CT number error was decreased to <5 after scatter correction from >200 HU before correction when scanned without a bowtie filter, and to <4 from >160 HU when scanned with a full bowtie filter. For the pelvis phantom, the CT number error was reduced to <12 after scatter correction from >250 HU before correction when scanned without a bowtie filter, and to <10 from >190 HU when scanned with a half bowtie filter. After scatter correction, the uniformity and contrast were both improved, resulting in an SNU of >79% decrease and CNR of >2 times increase, respectively.

CONCLUSIONS

High-quality CBCT images could be obtained in a single scan after using the proposed RBB method for scatter correction, enabling more accurate image guidance for surgery and radiation therapy applications. With almost no time delay between the successive open and blocked projections, the RBB method could eliminate the motion-induced anatomical mismatches between the corresponding open and blocked projections and could find particular usefulness in thoracic and abdominal imaging.

Non-coplanar lung SABR treatments delivered with a gantry-mounted x-ray tube

Objective.To create two non-coplanar, stereotactic ablative radiotherapy (SABR) lung patient treatment plans compliant with the radiation therapy oncology group (RTOG) 0813 dosimetric criteria using a simple, isocentric, therapy with kilovoltage arcs (SITKA) system designed to provide low cost external radiotherapy treatments for low- and middle-income countries (LMICs).Approach.A treatment machine design has been proposed featuring a 320 kVp x-ray tube mounted on a gantry. A deep learning cone-beam CT (CBCT) to synthetic CT (sCT) method was employed to remove the additional cost of planning CTs. A novel inverse treatment planning approach using GPU backprojection was used to create a highly non-coplanar treatment plan with circular beam shapes generated by an iris collimator. Treatments were planned and simulated using the TOPAS Monte Carlo (MC) code for two lung patients. Dose distributions were compared to 6 MV volumetric modulated arc therapy (VMAT) planned in Eclipse on the same cases for a Truebeam linac as well as obeying the RTOG 0813 protocols for lung SABR treatments with a prescribed dose of 50 Gy.Main results.The low-cost SITKA treatments were compliant with all RTOG 0813 dosimetric criteria. SITKA treatments showed, on average, a 6.7 and 4.9 Gy reduction of the maximum dose in soft tissue organs at risk (OARs) as compared to VMAT, for the two patients respectively. This was accompanied by a small increase in the mean dose of 0.17 and 0.30 Gy in soft tissue OARs.Significance.The proposed SITKA system offers a maximally low-cost, effective alternative to conventional radiotherapy systems for lung cancer patients, particularly in low-income countries. The system's non-coplanar, isocentric approach, coupled with the deep learning CBCT to sCT and GPU backprojection-based inverse treatment planning, offers lower maximum doses in OARs and comparable conformity to VMAT plans at a fraction of the cost of conventional radiotherapy.

TOPAS-imaging: extensions to the TOPAS simulation toolkit for medical imaging systems

Objective. The TOol for PArticle Simulation (TOPAS) is a Geant4-based Monte Carlo software application that has been used for both research and clinical studies in medical physics. So far, most users of TOPAS have focused on radiotherapy-related studies, such as modeling radiation therapy delivery systems or patient dose calculation. Here, we present the first set of TOPAS extensions to make it easier for TOPAS users to model medical imaging systems.Approach. We used the extension system of TOPAS to implement pre-built, user-configurable geometry components such as detectors (e.g. flat-panel and multi-planar detectors) for various imaging modalities and pre-built, user-configurable scorers for medical imaging systems (e.g. digitizer chain).Main results. We developed a flexible set of extensions that can be adapted to solve research questions for a variety of imaging modalities. We then utilized these extensions to model specific examples of cone-beam CT (CBCT), positron emission tomography (PET), and prompt gamma (PG) systems. The first of these new geometry components, the FlatImager, was used to model example CBCT and PG systems. Detected signals were accumulated in each detector pixel to obtain the intensity of x-rays penetrating objects or prompt gammas from proton-nuclear interaction. The second of these new geometry components, the RingImager, was used to model an example PET system. Positron-electron annihilation signals were recorded in crystals of the RingImager and coincidences were detected. The simulated data were processed using corresponding post-processing algorithms for each modality and obtained results in good agreement with the expected true signals or experimental measurement.Significance. The newly developed extension is a first step to making it easier for TOPAS users to build and simulate medical imaging systems. Together with existing TOPAS tools, this extension can help integrate medical imaging systems with radiotherapy simulations for image-guided radiotherapy.

A correlated sampling-based Monte Carlo simulation for fast CBCT iterative scatter correction

BACKGROUND

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.

PURPOSE

In this work, an MC simulation method combined with a variance reduction technique called correlated sampling is proposed for fast iterative scatter correction.

METHODS

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.

RESULTS

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 2 HU in full-fan case and from 53 to 13 HU 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 4 HU 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 25 s for the whole iterative scatter correction process.

CONCLUSIONS

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.

Full modulation transfer functions of thick parallel- and focused-element scintillator arrays obtained by a Monte Carlo optical transport model

BACKGROUND

Arrays of thick segmented crystalline scintillators are useful x-ray converters for image-guided radiation therapy using electronic portal imaging (EPI) and megavoltage cone-beam computed tomography (MV-CBCT). Ionizing-radiation-only simulations previously showed relatively low modulation transfer function (MTF) in parallel-element arrays because of beam divergence. Hence, a focused-element geometry (matching the beam divergence) has been proposed. The "full" (ionizing and optical) MTF performance of such a focused geometry compared to its radiation-only MTF has, however, not been fully investigated.

PURPOSE

To study the full MTF performance of such arrays in a more realistic situation in which optical characteristics are also included using an in-house detector model that supports light transport, and quantify the errors in MTF estimation when the optical stage is ignored.

METHODS

First, radiation (x-ray and electron) transport was simulated. Then, transport of the generated optical photons was modeled using ScintSim2, an optical Monte Carlo (MC) code developed in MATLAB for simulation of two-dimensional (2D) parallel- and focused-element scintillator arrays. The full-MTF responses of focused- and parallel-element geometries, for a large array of 3 × 3 mm² CsI:Tl detector elements of 10, 40, and 60 mm thicknesses, were examined. For each configuration, a composite line spread function (LSF) was calculated to obtain the MTF.

RESULTS

At the Nyquist frequency, for 10 mm-thick central elements and 60 mm-thick peripheral parallel elements, full-MTF exhibited a drop of up to 15 and 79 times, respectively, compared with radiation-only MTF. This was found to be partly attributable to the angular distribution of the light emerging from the detector-element exit face and the dependence on its aspect ratio, since the light exiting thicker scintillators exhibited a more forward-directed distribution. Focused elements provided an increase of up to nine times in peripheral-area full MTF values.

CONCLUSIONS

Full MTF was up to 79 times lower than radiation-only MTF. Focused arrays preserved full MTF by up to nine times compared to parallel elements. The differences in the results obtained with and without inclusion of optical photons emphasize the need to include light transport when optimizing thick segmented scintillation detectors. Besides their application in detector optimization for radiotherapy megavoltage photon imaging, these findings can also be useful for other segmented-scintillator-based imaging systems, for example, in nuclear medicine, or in 2D detection systems for quality assurance of MR-linacs.

Next generation high resolution perovskite direct conversion detector: Monte Carlo design optimisation and virtual clinical trial

We implement virtual clinical integration of next-generation perovskite detectors into common x-ray imaging devices. This was achieved by performing Monte Carlo (MC) optimisation of the design and benchmarking of low cost, high spatial resolution, direct conversion perovskite crystal x-ray flat panel imagers for a next generation of breast-, MV-, and kV-cone beam CT detectors. Semiconductor methylammonium lead bromide perovskite crystals energy deposition efficiencies calculated in TOPAS were benchmarked against four common detector materials for twelve detector crystal thicknesses between 40 to 15 mm and ten beam energies ranging from 20 keV to 6 MeV. Based on these simulations, Koning's dedicated breast CT, and Varian's Truebeam kV- and MV-cone beam CT systems were designated as suitable applications for perovskite detectors. System specific Fastcat hybrid MC cone beam CT image simulation was subsequently used to optimise the perovskite detector design and conduct virtual clinical trials. Device-specific optimal perovskite crystal thicknesses were calculated to be 0.30, 0.86, and 1.99 mm for Koning breast CT and Truebeam kV- and MV-cone beam CT systems, respectively. Replacing the current detectors on these machines with low cost perovskite crystal detectors could be advantageous as it would simultaneously yield 12.1%, 9.5% and 86.1% improvements in detective quantum efficiency as well as increases in contrast to noise ratio in brain, lung, and bone tissues.

Metal artifact correction in photon-counting detector computed tomography: metal trace replacement using high-energy data

BACKGROUND

Metal artifacts have been an outstanding issue in computed tomography (CT) since its first uses in the clinic and continue to interfere. Metal artifact reduction (MAR) methods continue to be proposed and photon-counting detectors (PCDs) have recently been the subject of research toward this purpose. PCDs offer the ability to distinguish the energy of incident x-rays and sort them in a set number of energy bins. High-energy data captured using PCDs have been shown to reduce metal artifacts in reconstructions due to reduced beam hardening.

PURPOSE

High-energy reconstructions using PCD-CT have their drawbacks, such as reduced image contrast and increased noise. Here, we demonstrate a MAR algorithm, trace replacement MAR (TRMAR), in which the data corrupted by metal artifacts in full energy spectrum projections are corrected using the high-energy data captured during the same scan. The resulting reconstructions offer similar MAR to that seen in high-energy reconstructions, but with improved image quality.

METHODS

Experimental data were collected using a bench-top PCD-CT system with a cadmium zinc telluride PCD. Simulations were performed to determine the optimal high-energy threshold and to test TRMAR in simulations using the XCAT phantom and a biological sample. For experiments a 100-mm diameter cylindrical phantom containing vials of water, two screws, various densities of Ca(ClO₄ )₂ , and a spatial resolution phantom was imaged with and without the screws. The screws were segmented in the initial reconstruction and forward projected to identify them in the sinogram space in order to perform TRMAR. The resulting reconstructions were compared to the control and to reconstructions corrected using normalized metal artifact reduction (NMAR). Additionally, a beef short rib was imaged with and without metal to provide a more realistic phantom.

RESULTS

XCAT simulations showed a reduction in the streak artifact from -978 HU in uncorrected images to -10 HU with TRMAR. The magnitude of the metal artifact in uncorrected images of the 100-mm phantom was -442 HU, compared to the desired -81 HU with no metal. TRMAR reduced the magnitude of the artifact to -142 HU, with NMAR reducing the magnitude to -96 HU. Relative image noise was reduced from 176% in the high-energy image to 56% using TRMAR. Density quantification was better with NMAR, with the Ca(ClO₄ )₂ vial affected most by metal artifacts showing 0.8% error compared to 2.1% with TRMAR. Small features were preserved to a greater extent with TRMAR, with the limiting spatial frequency at 20% of the MTF fully maintained at 1.31 lp/mm, while with NMAR it was reduced to 1.22 lp/mm. Images of the beef short rib showed better delineation of the shape of the metal using TRMAR.

CONCLUSIONS

NMAR offers slightly better performance compared to TRMAR in streak reduction and image quality metrics. However, TRMAR is less susceptible to metal segmentation errors and can closely approximate the reduction in the streak metal artifact seen in NMAR at 1/3 the computation time. With the recent introduction of PCD-CT into the clinic, TRMAR offers notable potential for fast, effective MAR.

XCIST-an open access x-ray/CT simulation toolkit

Objective. X-ray-based imaging modalities including mammography and computed tomography (CT) are widely used in cancer screening, diagnosis, staging, treatment planning, and therapy response monitoring. Over the past few decades, improvements to these modalities have resulted in substantially improved efficacy and efficiency, and substantially reduced radiation dose and cost. However, such improvements have evolved more slowly than would be ideal because lengthy preclinical and clinical evaluation is required. In many cases, new ideas cannot be evaluated due to the high cost of fabricating and testing prototypes. Wider availability of computer simulation tools could accelerate development of new imaging technologies. This paper introduces the development of a new open-access simulation environment for x-ray-based imaging. The main motivation of this work is to publicly distribute a fast but accurate ray-tracing x-ray and CT simulation tool along with realistic phantoms and 3D reconstruction capability, building on decades of developments in industry and academia.Approach. The x-ray-based Cancer Imaging Simulation Toolkit (XCIST) is developed in the context of cancer imaging, but can more broadly be applied. XCIST is physics-based, written in Python and C/C++, and currently consists of three major subsets: digital phantoms, the simulator itself (CatSim), and image reconstruction algorithms; planned future features include a fast dose-estimation tool and rigorous validation. To enable broad usage and to model and evaluate new technologies, XCIST is easily extendable by other researchers. To demonstrate XCIST's ability to produce realistic images and to show the benefits of using XCIST for insight into the impact of separate physics effects on image quality, we present exemplary simulations by varying contributing factors such as noise and sampling.Main results. The capabilities and flexibility of XCIST are demonstrated, showing easy applicability to specific simulation problems. Geometric and x-ray attenuation accuracy are shown, as well as XCIST's ability to model multiple scanner and protocol parameters, and to attribute fundamental image quality characteristics to specific parameters.Significance. This work represents an important first step toward the goal of creating an open-access platform for simulating existing and emerging x-ray-based imaging systems. While numerous simulation tools exist, we believe the combined XCIST toolset provides a unique advantage in terms of modeling capabilities versus ease of use and compute time. We publicly share this toolset to provide an environment for scientists to accelerate and improve the relevance of their research in x-ray and CT.

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.