hybridMANTIS: a CPU-GPU Monte Carlo method for modeling indirect x-ray detectors with columnar scintillators

Computational models of direct and indirect X-ray breast imaging detectors for in silico trials

BACKGROUND

To facilitate in silico studies that investigate digital mammography (DM) and breast tomosynthesis (DBT), models replicating the variety in imaging performance of the DM and DBT systems, observed across manufacturers are needed.

PURPOSE

The main purpose of this work is to develop generic physics models for direct and indirect detector technology used in commercially available systems, with the goal of making them available open source to manufacturers to further tweak and develop the exact in silico replicas of their systems.

METHODS

We recently reported on an in silico version of the SIEMENS Mammomat Inspiration DM/DBT system using an open-source GPU-accelerated Monte Carlo x-ray imaging simulation code (MC-GPU). We build on the previous version of the MC-GPU codes to mimic the imaging performances of two other Food and Drug Administration (FDA)-approved DM/DBT systems, such as Hologic Selenia Dimensions (HSD) and the General Electric Senographe Pristina (GSP) systems. In this work, we developed a hybrid technique to model the optical spread and signal crosstalk observed in the GSP and HSD systems. MC simulations are used to track each x-ray photon till its first interaction within the x-ray detector. On the other hand, the signal spread in the x-ray detectors is modeled using previously developed analytical equations. This approach allows us to preserve the modeling accuracy offered by MC methods in the patient body, while speeding up secondary carrier transport (either electron-hole pairs or optical photons) using analytical equations in the detector. The analytical optical spread model for the indirect detector includes the depth-dependent spread and collection of optical photons and relies on a pre-computed set of point response functions that describe the optical spread as a function of depth. To understand the capabilities of the computational x-ray detector models, we compared image quality metrics like modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE), simulated with our models against measured data. Please note that the purpose of these comparisons with measured data would be to gauge if the model developed as part of this work could replicate commercially used direct and indirect technology in general and not to achieve perfect fits with measured data.

RESULTS

We found that the simulated image quality metrics such as MTF, NNPS, and DQE were in reasonable agreement with experimental data. To demonstrate the imaging performance of the three DM/DBT systems, we integrated the detector models with the VICTRE pipeline and simulated DM images of a fatty breast model containing a spiculated mass and a calcium oxalate cluster. In general, we found that the images generated using the indirect model appeared more blurred with a different noise texture and contrast as compared to the systems with direct detectors.

CONCLUSIONS

We have presented computational models of three commercially available FDA-approved DM/DBT systems, which implement both direct and indirect detector technology. The updated versions of the MC-GPU codes that can be used to replicate three systems are available in open source format through GitHub.

Virtual Clinical Trials in 2D and 3D X-ray Breast Imaging and Dosimetry: Comparison of CPU-Based and GPU-Based Monte Carlo Codes

Simple Summary

Virtual clinical trials in X-ray breast imaging may permit substantial reduction of the costs, times, and exposure risk to patient of clinical trials. Monte Carlo simulation techniques are increasingly adopted for VCT in breast imaging and dosimetry studies. This work aims to compare three different platforms for breast VCT studies, to develop real-time virtual DM, DBT and BCT examinations, where the in-silico image acquisition process takes a computational time comparable to that typical of a corresponding real clinical examination.

Abstract

Computational reproductions of medical imaging tests, a form of virtual clinical trials (VCTs), are increasingly being used, particularly in breast imaging research. The accuracy of the computational platform that is used for the imaging and dosimetry simulation processes is a fundamental requirement. Moreover, for practical usage, the imaging simulation computation time should be compatible with the clinical workflow. We compared three different platforms for in-silico X-ray 3D breast imaging: the Agata (University & INFN Napoli) that was based on the Geant4 toolkit and running on a CPU-based server architecture; the XRMC Monte Carlo (University of Cagliari) that was based on the use of variance reduction techniques, running on a CPU hardware; and the Monte Carlo code gCTD (University of Texas Southwestern Medical Center) running on a single GPU platform with CUDA environment. The tests simulated the irradiation of cylindrical objects as well as anthropomorphic breast phantoms and produced 2D and 3D images and 3D maps of absorbed dose. All the codes showed compatible results in terms of simulated dose maps and imaging values within a maximum discrepancy of 3%. The GPU-based code produced a reduction of the computation time up to factor 10⁴, and so permits real-time VCT studies for X-ray breast imaging.

Investigation on the resolution of a micro cone beam CT scanner scintillating detector using Monte Carlo methods

The impact of several physical quantities on the spatial resolution of an X-ray scintillating pixel detector for a micro cone beam CT (µCBCT) is investigated and discussed. The XtremeCT from SCANCO Medical AG was simulated using the EGSnrc/EGS++ Monte Carlo (MC) framework and extensively benchmarked in a previous work. The resolution of the detector was determined by simulating a titanium knife-edge to obtain the edge spread function (ESF) and the modulation transfer function (MTF). Propagation of the scintillation light through the scintillator and its coupling into the fiber optics system was taken into account. The contribution of particles scattered in the main scanner components to the detector signal is very low and does not affect the spatial resolution of the detector. The resolution obtained from the energy deposition in the scintillator without any blurring due to the propagation of the scintillation light into the fiber optics array was 31 µm. By assuming isotropic light propagation in the scintillator, the resolution degraded to 360 µm. A simple light propagation model taking into account the impact of the scintillator's columnar microstructures was developed and compared with the MANTIS Monte Carlo simulation package. By reducing the width of the model's light propagation kernel by a factor of 2 compared to the isotropic case, the detector resolution can be improved to 83 µm, which corresponds well to the measured resolution of 86 µm. The resolution of the detector is limited mainly by the propagation of the scintillation light through the scintillator layer. It offers the greatest potential to improve the resolution of the µCBCT imaging system.

Modelling the transport of optical photons in scintillation detectors for diagnostic and radiotherapy imaging

Computational modelling of radiation transport can enhance the understanding of the relative importance of individual processes involved in imaging systems. Modelling is a powerful tool for improving detector designs in ways that are impractical or impossible to achieve through experimental measurements. Modelling of light transport in scintillation detectors used in radiology and radiotherapy imaging that rely on the detection of visible light plays an increasingly important role in detector design. Historically, researchers have invested heavily in modelling the transport of ionizing radiation while light transport is often ignored or coarsely modelled. Due to the complexity of existing light transport simulation tools and the breadth of custom codes developed by users, light transport studies are seldom fully exploited and have not reached their full potential. This topical review aims at providing an overview of the methods employed in freely available and other described optical Monte Carlo packages and analytical models and discussing their respective advantages and limitations. In particular, applications of optical transport modelling in nuclear medicine, diagnostic and radiotherapy imaging are described. A discussion on the evolution of these modelling tools into future developments and applications is presented.

Theoretical and Monte Carlo optimization of a stacked three-layer flat-panel x-ray imager for applications in multi-spectral medical imaging

We propose a new design of a stacked three-layer flat-panel x-ray detector for dual-energy (DE) imaging. Each layer consists of its own scintillator of individual thickness and an underlying thin-film-transistor-based flat-panel. Three images are obtained simultaneously in the detector during the same x-ray exposure, thereby eliminating any motion artifacts. The detector operation is two-fold: a conventional radiography image can be obtained by combining all three layers' images, while a DE subtraction image can be obtained from the front and back layers' images, where the middle layer acts as a mid-filter that helps achieve spectral separation. We proceed to optimize the detector parameters for two sample imaging tasks that could particularly benefit from this new detector by obtaining the best possible signal to noise ratio per root entrance exposure using well-established theoretical models adapted to fit our new design. These results are compared to a conventional DE temporal subtraction detector and a single-shot DE subtraction detector with a copper mid-filter, both of which underwent the same theoretical optimization. The findings are then validated using advanced Monte Carlo simulations for all optimized detector setups. Given the performance expected from initial results and the recent decrease in price for digital x-ray detectors, the simplicity of the three-layer stacked imager approach appears promising to usher in a new generation of multi-spectral digital x-ray diagnostics.

Web-based, GPU-accelerated, Monte Carlo simulation and visualization of indirect radiation imaging detector performance

PURPOSE

Monte Carlo simulations play a vital role in the understanding of the fundamental limitations, design, and optimization of existing and emerging medical imaging systems. Efforts in this area have resulted in the development of a wide variety of open-source software packages. One such package, hybridmantis, uses a novel hybrid concept to model indirect scintillator detectors by balancing the computational load using dual CPU and graphics processing unit (GPU) processors, obtaining computational efficiency with reasonable accuracy. In this work, the authors describe two open-source visualization interfaces, webmantis and visualmantis to facilitate the setup of computational experiments via hybridmantis.

METHODS

The visualization tools visualmantis and webmantis enable the user to control simulation properties through a user interface. In the case of webmantis, control via a web browser allows access through mobile devices such as smartphones or tablets. webmantis acts as a server back-end and communicates with an NVIDIA GPU computing cluster that can support multiuser environments where users can execute different experiments in parallel.

RESULTS

The output consists of point response and pulse-height spectrum, and optical transport statistics generated by hybridmantis. The users can download the output images and statistics through a zip file for future reference. In addition, webmantis provides a visualization window that displays a few selected optical photon path as they get transported through the detector columns and allows the user to trace the history of the optical photons.

CONCLUSIONS

The visualization tools visualmantis and webmantis provide features such as on the fly generation of pulse-height spectra and response functions for microcolumnar x-ray imagers while allowing users to save simulation parameters and results from prior experiments. The graphical interfaces simplify the simulation setup and allow the user to go directly from specifying input parameters to receiving visual feedback for the model predictions.

Analytic variance estimates of Swank and Fano factors

PURPOSE

Variance estimates for detector energy resolution metrics can be used as stopping criteria in Monte Carlo simulations for the purpose of ensuring a small uncertainty of those metrics and for the design of variance reduction techniques.

METHODS

The authors derive an estimate for the variance of two energy resolution metrics, the Swank factor and the Fano factor, in terms of statistical moments that can be accumulated without significant computational overhead. The authors examine the accuracy of these two estimators and demonstrate how the estimates of the coefficient of variation of the Swank and Fano factors behave with data from a Monte Carlo simulation of an indirect x-ray imaging detector.

RESULTS

The authors' analyses suggest that the accuracy of their variance estimators is appropriate for estimating the actual variances of the Swank and Fano factors for a variety of distributions of detector outputs.

CONCLUSIONS

The variance estimators derived in this work provide a computationally convenient way to estimate the error or coefficient of variation of the Swank and Fano factors during Monte Carlo simulations of radiation imaging systems.

Validation of columnar CsI x-ray detector responses obtained with hybridMANTIS, a CPU-GPU Monte Carlo code for coupled x-ray, electron, and optical transport

PURPOSE

hybridMANTIS is a Monte Carlo package for modeling indirect x-ray imagers using columnar geometry based on a hybrid concept that maximizes the utilization of available CPU and graphics processing unit processors in a workstation.

METHODS

The authors compare hybridMANTIS x-ray response simulations to previously published MANTIS and experimental data for four cesium iodide scintillator screens. These screens have a variety of reflective and absorptive surfaces with different thicknesses. The authors analyze hybridMANTIS results in terms of modulation transfer function and calculate the root mean square difference and Swank factors from simulated and experimental results.

RESULTS

The comparison suggests that hybridMANTIS better matches the experimental data as compared to MANTIS, especially at high spatial frequencies and for the thicker screens. hybridMANTIS simulations are much faster than MANTIS with speed-ups up to 5260.

CONCLUSIONS

hybridMANTIS is a useful tool for improved description and optimization of image acquisition stages in medical imaging systems and for modeling the forward problem in iterative reconstruction algorithms.