MCGPU-PET: An Open-Source Real-Time Monte Carlo PET Simulator.

Monte Carlo (MC) simulations are commonly used to model the emission, transmission, and/or detection of radiation in Positron Emission Tomography (PET). In this work, we introduce a new open-source MC software for PET simulation, MCGPU-PET, which has been designed to fully exploit the computing capabilities of modern GPUs to simulate the acquisition of more than 100 million coincidences per second from voxelized sources and material distributions. The new simulator is an extension of the PENELOPE-based MCGPU code previously used in cone-beam CT and mammography applications. We validated the accuracy of the accelerated code by comparing it to GATE and PeneloPET simulations achieving an agreement within 10 percent approximately. As an example application of the code for fast estimation of PET coincidences, a scan of the NEMA IQ phantom was simulated. A fully 3D sinogram with 6382 million true coincidences and 731 million scatter coincidences was generated in 54 seconds in one GPU. MCGPU-PET provides an estimation of true and scatter coincidences and spurious background (for positron-gamma emitters such as 124I) at a rate 3 orders of magnitude faster than CPU-based MC simulators. This significant speed-up enables the use of the code for accurate scatter and prompt-gamma background estimations within an iterative image reconstruction process.

Method to measure the size of a radiographic field larger than a detector by imaging fluorescence X-rays with a slit camera.

PURPOSE: X-ray imaging devices contain a collimator system that defines a rectangular irradiation field on the detector plane. The size and position of the X-ray field, and its congruence with the corresponding light field, must be regularly tested for quality control. We propose a new method to estimate how far the x-ray field extends beyond the detector which does not require the use of external detectors. METHODS: A metallic foil is inserted perpendicularly between the source and the detector. A slit camera, a linear extension of a pinhole camera, is used to project onto the detector the fluorescence X-rays emitted by the irradiated foil. The location where the fluorescence signal inside the camera vanishes is used to extrapolate the location of the field boundary. Monte Carlo simulations were performed to determine the optimal composition and thickness of the foil. A prototype camera with a 1-mm-wide slit was built and tested in a clinical mammography and digital breast tomosynthesis (DBT) system. RESULTS: The simulations estimated that a foil made of 25 µm of Molybdenum provided the maximum signal inside the camera for a 39 kVp beam. The boundary of the X-ray fields in mammography and DBT views were experimentally measured. With the camera along the chest wall side, the measured field in multiple DBT views had a variability of only 0.4 ± 0.1 mm compared to mammography. A difference in the measured boundary position of 2.4 and -1.0 mm was observed when comparing to measurements with a fluorescent ruler and self-developing film. CONCLUSION: The introduced technique provides a practical alternative method to detect the boundary of an X-ray field. The method can be combined with other testing methods to assess the congruence of the X-rays and light fields, and to determine if the X-ray field extends beyond the detector more than permitted.

Pediatric evaluations for deep learning CT denoising.

BACKGROUND: Deep learning (DL) CT denoising models have the potential to improve image quality for lower radiation dose exams. These models are generally trained with large quantities of adult patient image data. However, CT, and increasingly DL denoising methods, are used in both adult and pediatric populations. Pediatric body habitus and size can differ significantly from adults and vary dramatically from newborns to adolescents. Ensuring that pediatric subgroups of different body sizes are not disadvantaged by DL methods requires evaluations capable of assessing performance in each subgroup. PURPOSE: To assess DL CT denoising in pediatric and adult-sized patients, we built a framework of computer simulated image quality (IQ) control phantoms and evaluation methodology. METHODS: The computer simulated IQ phantoms in the framework featured pediatric-sized versions of standard CatPhan 600 and MITA-LCD phantoms with a range of diameters matching the mean effective diameters of pediatric patients ranging from newborns to 18 years old. These phantoms were used in simulating CT images that were then inputs for a DL denoiser to evaluate performance in different sized patients. Adult CT test images were simulated using standard-sized phantoms scanned with adult scan protocols. Pediatric CT test images were simulated with pediatric-sized phantoms and adjusted pediatric protocols. The framework's evaluation methodology consisted of denoising both adult and pediatric test images then assessing changes in image quality, including noise, image sharpness, CT number accuracy, and low contrast detectability. To demonstrate the use of the framework, a REDCNN denoising model trained on adult patient images was evaluated. To validate that the DL model performance measured with the proposed pediatric IQ phantoms was representative of performance in more realistic patient anatomy, anthropomorphic pediatric XCAT phantoms of the same age range were also used to compare noise reduction performance. RESULTS: Using the proposed pediatric-sized IQ phantom framework, size differences between adult and pediatric-sized phantoms were observed to substantially influence the adult trained DL denoising model's performance. When applied to adult images, the DL model achieved a 60% reduction in noise standard deviation without substantial loss in sharpness in mid or high spatial frequencies. However, in smaller phantoms the denoising performance dropped due to different image noise textures resulting from the smaller field of view (FOV) between adult and pediatric protocols. In the validation study, noise reduction trends in the pediatric-sized IQ phantoms were found to be consistent with those found in anthropomorphic phantoms. CONCLUSION: We developed a framework of using pediatric-sized IQ phantoms for pediatric subgroup evaluation of DL denoising models. Using the framework, we found the performance of an adult trained DL denoiser did not generalize well in the smaller diameter phantoms corresponding to younger pediatric patient sizes. Our work suggests noise texture differences from FOV changes between adult and pediatric protocols can contribute to poor generalizability in DL denoising and that the proposed framework is an effective means to identify these performance disparities for a given model.

PyMCGPU-IR Monte Carlo code test for occupational dosimetry.

PyMCGPU-IR is an innovative occupational dose monitoring tool for interventional radiology procedures. It reads the radiation data from the Radiation Dose Structured Report of the procedure and combines this information with the position of the monitored worker recorded using a 3D camera system. This information is used as an input file for the fast Monte Carlo radiation transport code MCGPU-IR in order to assess the organ doses, Hp(10) and Hp(0.07), as well as the effective dose. In this study, Hp(10) measurements of the first operator during an endovascular aortic aneurysm repair procedure and a coronary angiography using a ceiling suspended shield are compared to PyMCGPU-IR calculations. Differences in the two reported examples are found to be within 15%, which is considered as being very satisfactory. The study highlights the promising advantages of PyMCGPU-IR, although there are still several improvements that need to be implemented before its final clinical use.

A database for estimating organ dose for coronary angiography and brain perfusion CT scans for arbitrary spectra and angular tube current modulation.

PURPOSE: The purpose of this study was to develop a database for estimating organ dose in a voxelized patient model for coronary angiography and brain perfusion CT acquisitions with any spectra and angular tube current modulation setting. The database enables organ dose estimation for existing and novel acquisition techniques without requiring Monte Carlo simulations. METHODS: The study simulated transport of monoenergetic photons between 5 and 150 keV for 1000 projections over 360° through anthropomorphic voxelized female chest and head (0° and 30° tilt) phantoms and standard head and body CTDI dosimetry cylinders. The simulations resulted in tables of normalized dose deposition for several radiosensitive organs quantifying the organ dose per emitted photon for each incident photon energy and projection angle for coronary angiography and brain perfusion acquisitions. The values in a table can be multiplied by an incident spectrum and number of photons at each projection angle and then summed across all energies and angles to estimate total organ dose. Scanner-specific organ dose may be approximated by normalizing the database-estimated organ dose by the database-estimated CTDI(vol) and multiplying by a physical CTDI(vol) measurement. Two examples are provided demonstrating how to use the tables to estimate relative organ dose. In the first, the change in breast and lung dose during coronary angiography CT scans is calculated for reduced kVp, angular tube current modulation, and partial angle scanning protocols relative to a reference protocol. In the second example, the change in dose to the eye lens is calculated for a brain perfusion CT acquisition in which the gantry is tilted 30° relative to a nontilted scan. RESULTS: Our database provides tables of normalized dose deposition for several radiosensitive organs irradiated during coronary angiography and brain perfusion CT scans. Validation results indicate total organ doses calculated using our database are within 1% of those calculated using Monte Carlo simulations with the same geometry and scan parameters for all organs except red bone marrow (within 6%), and within 23% of published estimates for different voxelized phantoms. Results from the example of using the database to estimate organ dose for coronary angiography CT acquisitions show 2.1%, 1.1%, and -32% change in breast dose and 2.1%, -0.74%, and 4.7% change in lung dose for reduced kVp, tube current modulated, and partial angle protocols, respectively, relative to the reference protocol. Results show -19.2% difference in dose to eye lens for a tilted scan relative to a nontilted scan. The reported relative changes in organ doses are presented without quantification of image quality and are for the sole purpose of demonstrating the use of the proposed database. CONCLUSIONS: The proposed database and calculation method enable the estimation of organ dose for coronary angiography and brain perfusion CT scans utilizing any spectral shape and angular tube current modulation scheme by taking advantage of the precalculated Monte Carlo simulation results. The database can be used in conjunction with image quality studies to develop optimized acquisition techniques and may be particularly beneficial for optimizing dual kVp acquisitions for which numerous kV, mA, and filtration combinations may be investigated.

Spatiotemporal Monte Carlo transport methods in x-ray semiconductor detectors: application to pulse-height spectroscopy in a-Se.

PURPOSE: The authors describe a detailed Monte Carlo (MC) method for the coupled transport of ionizing particles and charge carriers in amorphous selenium (a-Se) semiconductor x-ray detectors, and model the effect of statistical variations on the detected signal. METHODS: A detailed transport code was developed for modeling the signal formation process in semiconductor x-ray detectors. The charge transport routines include three-dimensional spatial and temporal models of electron-hole pair transport taking into account recombination and trapping. Many electron-hole pairs are created simultaneously in bursts from energy deposition events. Carrier transport processes include drift due to external field and Coulombic interactions, and diffusion due to Brownian motion. RESULTS: Pulse-height spectra (PHS) have been simulated with different transport conditions for a range of monoenergetic incident x-ray energies and mammography radiation beam qualities. Two methods for calculating Swank factors from simulated PHS are shown, one using the entire PHS distribution, and the other using the photopeak. The latter ignores contributions from Compton scattering and K-fluorescence. Comparisons differ by approximately 2% between experimental measurements and simulations. CONCLUSIONS: The a-Se x-ray detector PHS responses simulated in this work include three-dimensional spatial and temporal transport of electron-hole pairs. These PHS were used to calculate the Swank factor and compare it with experimental measurements. The Swank factor was shown to be a function of x-ray energy and applied electric field. Trapping and recombination models are all shown to affect the Swank factor.

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.

Development of metaverse for intelligent healthcare.

The metaverse integrates physical and virtual realities, enabling humans and their avatars to interact in an environment supported by technologies such as high-speed internet, virtual reality, augmented reality, mixed and extended reality, blockchain, digital twins and artificial intelligence (AI), all enriched by effectively unlimited data. The metaverse recently emerged as social media and entertainment platforms, but extension to healthcare could have a profound impact on clinical practice and human health. As a group of academic, industrial, clinical and regulatory researchers, we identify unique opportunities for metaverse approaches in the healthcare domain. A metaverse of 'medical technology and AI' (MeTAI) can facilitate the development, prototyping, evaluation, regulation, translation and refinement of AI-based medical practice, especially medical imaging-guided diagnosis and therapy. Here, we present metaverse use cases, including virtual comparative scanning, raw data sharing, augmented regulatory science and metaversed medical intervention. We discuss relevant issues on the ecosystem of the MeTAI metaverse including privacy, security and disparity. We also identify specific action items for coordinated efforts to build the MeTAI metaverse for improved healthcare quality, accessibility, cost-effectiveness and patient satisfaction.

A PENELOPE-based system for the automated Monte Carlo simulation of clinacs and voxelized geometries-application to far-from-axis fields.

PURPOSE: Two new codes, PENEASY and PENEASYLINAC, which automate the Monte Carlo simulation of Varian Clinacs of the 600, 1800, 2100, and 2300 series, together with their electron applicators and multileaf collimators, are introduced. The challenging case of a relatively small and far-from-axis field has been studied with these tools. METHODS: PENEASY is a modular, general-purpose main program for the PENELOPE Monte Carlo system that includes various source models, tallies and variance-reduction techniques (VRT). The code includes a new geometry model that allows the superposition of voxels and objects limited by quadric surfaces. A variant of the VRT known as particle splitting, called fan splitting, is also introduced. PENEASYLINAC, in turn, automatically generates detailed geometry and configuration files to simulate linacs with PENEASY. These tools are applied to the generation of phase-space files, and of the corresponding absorbed dose distributions in water, for two 6 MV photon beams from a Varian Clinac 2100 C∕D: a 40 × 40 cm(2) centered field; and a 3 × 5 cm(2) field centered at (4.5, -11.5) cm from the beam central axis. This latter configuration implies the largest possible over-traveling values of two of the jaws. Simulation results for the depth dose and lateral profiles at various depths are compared, by using the gamma index, with experimental values obtained with a PTW 31002 ionization chamber. The contribution of several VRTs to the computing speed of the more demanding off-axis case is analyzed. RESULTS: For the 40 × 40 cm(2) field, the percentages γ(1) and γ(1.2) of voxels with gamma indices (using 0.2 cm and 2% criteria) larger than unity and larger than 1.2 are 0.2% and 0%, respectively. For the 3 × 5 cm(2) field, γ(1) = 0%. These figures indicate an excellent agreement between simulation and experiment. The dose distribution for the off-axis case with voxels of 2.5 × 2.5 × 2.5 mm(3) and an average standard statistical uncertainty of 2% (1σ) is computed in 3.1 h on a single core of a 2.8 GHz Intel Core 2 Duo processor. This result is obtained with the optimal combination of the tested VRTs. In particular, fan splitting for the off-axis case accelerates execution by a factor of 240 with respect to standard particle splitting. CONCLUSIONS: PENEASY and PENEASYLINAC can simulate the considered Varian Clinacs both in an accurate and efficient manner. Fan splitting is crucial to achieve simulation results for the off-axis field in an affordable amount of CPU time. Work to include Elekta linacs and to develop a graphical interface that will facilitate user input is underway.

A Review of Doses for Dental Imaging in 2010-2020 and Development of a Web Dose Calculator.

Dental imaging is one of the most common types of diagnostic radiological procedures in modern medicine. We introduce a comprehensive table of organ doses received by patients in dental imaging procedures extracted from literature and a new web application to visualize the summarized dose information. We analyzed articles, published after 2010, from PubMed on organ and effective doses delivered by dental imaging procedures, including intraoral radiography, panoramic radiography, and cone-beam computed tomography (CBCT), and summarized doses by dosimetry method, machine model, patient age, and technical parameters. Mean effective doses delivered by intraoral, 1.32 (0.60-2.56) µSv, and panoramic, 17.93 (3.47-75.00) µSv, procedures were found to be about1% and 15% of that delivered by CBCT, 121.09 (17.10-392.20) µSv, respectively. In CBCT imaging, child phantoms received about 29% more effective dose than the adult phantoms received. The effective dose of a large field of view (FOV) (>150 cm2) was about 1.6 times greater than that of a small FOV (<50 cm2). The maximum CBCT effective dose with a large FOV for children, 392.2 µSv, was about 13% of theeffective dose that a person receives on average every year from natural radiation, 3110 µSv. Monte Carlo simulations of representative cases of the three dental imaging procedures were then conducted to estimate and visualize the dose distribution within the head. The user-friendly interactive web application (available at http://dentaldose.org) receives user input, such as the number of intraoral radiographs taken, and displays total organ and effective doses, dose distribution maps, and a comparison with other medical and natural sources of radiation. The web dose calculator provides a practical resource for patients interested in understanding the radiation doses delivered by dental imaging procedures.

Exploring CNN potential in discriminating benign and malignant calcifications in conventional and dual-energy FFDM: simulations and experimental observations.

Purpose: Deep convolutional neural networks (CNN) have demonstrated impressive success in various image classification tasks. We investigated the use of CNNs to distinguish between benign and malignant microcalcifications, using either conventional or dual-energy mammography x-ray images. The two kinds of calcifications, known as type-I (calcium oxalate crystals) and type-II (calcium phosphate aggregations), have different attenuation properties in the mammographic energy range. However, variations in microcalcification shape, size, and density as well as compressed breast thickness and breast tissue background make this a challenging discrimination task for the human visual system. Approach: Simulations (conventional and dual-energy mammography) and phantom experiments (conventional mammography only) were conducted using the range of breast thicknesses and randomly shaped microcalcifications. The off-the-shelf Resnet-18 CNN was trained on the regions of interest with calcification clusters of the two kinds. Results: Both Monte Carlo simulations and experimental phantom data suggest that deep neural networks can be trained to separate the two classes of calcifications with high accuracy, using dual-energy mammograms. Conclusions: Our work shows the encouraging results of using the CNNs for non-invasive testing for type-I and type-II microcalcifications and may stimulate further research in this area with expanding presence of the novel breast imaging modalities like dual-energy mammography or systems using photon-counting detectors.

Using convolutional neural networks to discriminate between cysts and masses in Monte Carlo-simulated dual-energy mammography.

PURPOSE: A substantial percentage of recalls (up to 20%) in screening mammography is attributed to extended round lesions. Benign fluid-filled breast cysts often appear similar to solid tumors in conventional mammograms. Spectral imaging (dual-energy or photon-counting mammography) has been shown to discriminate between cysts and solid masses with clinically acceptable accuracy. This work explores the feasibility of using convolutional neural networks (CNNs) for this task. METHODS: A series of Monte Carlo experiments was conducted with digital breast phantoms and embedded synthetic lesions to produce realistic dual-energy images of both lesion types. We considered such factors as nonuniform anthropomorphic background, size of the mass, breast compression thickness, and variability in lesion x-ray attenuation. These data then were used to train a deep neural network (ResNet-18) to learn the differences in x-ray attenuation of cysts and masses. RESULTS: Our simulation results showed that the CNN-based classifier could reliably discriminate between cystic and solid mass round lesions in dual-energy images with an area under the receiver operating characteristic curve (ROC AUC) of 0.98 or greater. CONCLUSIONS: The proposed approach showed promising performance and ease of implementation, and could be applied to novel photon-counting detector-based spectral mammography systems.

Computational reader design and statistical performance evaluation of an in-silico imaging clinical trial comparing digital breast tomosynthesis with full-field digital mammography.

A recent study reported on an in-silico imaging trial that evaluated the performance of digital breast tomosynthesis (DBT) as a replacement for full-field digital mammography (FFDM) for breast cancer screening. In this in-silico trial, the whole imaging chain was simulated, including the breast phantom generation, the x-ray transport process, and computational readers for image interpretation. We focus on the design and performance characteristics of the computational reader in the above-mentioned trial. Location-known lesion (spiculated mass and clustered microcalcifications) detection tasks were used to evaluate the imaging system performance. The computational readers were designed based on the mechanism of a channelized Hotelling observer (CHO), and the reader models were selected to trend human performance. Parameters were tuned to ensure stable lesion detectability. A convolutional CHO that can adapt a round channel function to irregular lesion shapes was compared with the original CHO and was found to be suitable for detecting clustered microcalcifications but was less optimal in detecting spiculated masses. A three-dimensional CHO that operated on the multiple slices was compared with a two-dimensional (2-D) CHO that operated on three versions of 2-D slabs converted from the multiple slices and was found to be optimal in detecting lesions in DBT. Multireader multicase reader output analysis was used to analyze the performance difference between FFDM and DBT for various breast and lesion types. The results showed that DBT was more beneficial in detecting masses than detecting clustered microcalcifications compared with FFDM, consistent with the finding in a clinical imaging trial. Statistical uncertainty smaller than 0.01 standard error for the estimated performance differences was achieved with a dataset containing approximately 3000 breast phantoms. The computational reader design methodology presented provides evidence that model observers can be useful in-silico tools for supporting the performance comparison of breast imaging systems.

Evaluation of data augmentation via synthetic images for improved breast mass detection on mammograms using deep learning.

We evaluated whether using synthetic mammograms for training data augmentation may reduce the effects of overfitting and increase the performance of a deep learning algorithm for breast mass detection. Synthetic mammograms were generated using in silico procedural analytic breast and breast mass modeling algorithms followed by simulated x-ray projections of the breast models into mammographic images. In silico breast phantoms containing masses were modeled across the four BI-RADS breast density categories, and the masses were modeled with different sizes, shapes, and margins. A Monte Carlo-based x-ray transport simulation code, MC-GPU, was used to project the three-dimensional phantoms into realistic synthetic mammograms. 2000 mammograms with 2522 masses were generated to augment a real data set during training. From the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set, we used 1111 mammograms (1198 masses) for training, 120 mammograms (120 masses) for validation, and 361 mammograms (378 masses) for testing. We used faster R-CNN for our deep learning network with pretraining from ImageNet using the Resnet-101 architecture. We compared the detection performance when the network was trained using different percentages of the real CBIS-DDSM training set (100%, 50%, and 25%), and when these subsets of the training set were augmented with 250, 500, 1000, and 2000 synthetic mammograms. Free-response receiver operating characteristic (FROC) analysis was performed to compare performance with and without the synthetic mammograms. We generally observed an improved test FROC curve when training with the synthetic images compared to training without them, and the amount of improvement depended on the number of real and synthetic images used in training. Our study shows that enlarging the training data with synthetic samples can increase the performance of deep learning systems.

Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit.

PURPOSE: It is a known fact that Monte Carlo simulations of radiation transport are computationally intensive and may require long computing times. The authors introduce a new paradigm for the acceleration of Monte Carlo simulations: The use of a graphics processing unit (GPU) as the main computing device instead of a central processing unit (CPU). METHODS: A GPU-based Monte Carlo code that simulates photon transport in a voxelized geometry with the accurate physics models from PENELOPE has been developed using the CUDATM programming model (NVIDIA Corporation, Santa Clara, CA). RESULTS: An outline of the new code and a sample x-ray imaging simulation with an anthropomorphic phantom are presented. A remarkable 27-fold speed up factor was obtained using a GPU compared to a single core CPU. CONCLUSIONS: The reported results show that GPUs are currently a good alternative to CPUs for the simulation of radiation transport. Since the performance of GPUs is currently increasing at a faster pace than that of CPUs, the advantages of GPU-based software are likely to be more pronounced in the future.

Dosimetric impact of voxel resolutions of computational human phantoms for external photon exposure.

Several research teams have developed computational phantoms in polygonal-mesh (PM) and/or Non-Uniform Rational B-Spline format, but it has not been systematically evaluated if the existing voxel phantoms are still dosimetrically valid. We created three voxel phantoms with the resolutions of 1,000, 125, and 1 mm3 and simulated the irradiation in antero-posterior geometry with photons of 0.1, 1, and 10 MeV using voxel Monte Carlo codes, and compared the energy deposition to their organs/tissues with the values from the original PM phantom using mesh Monte Carlo codes. The coefficient of variation in energy deposition overall showed about five-fold decrease as the voxel resolution increased but differences were mostly less than 5% for any voxel resolution. We conclude that PM phantoms and mesh Monte Carlo techniques may not be necessary for external photon exposure (0.1 - 10 MeV) and the existing voxel phantoms can provide enough dosimetric accuracy in those exposure conditions.

Technical Note: In silico imaging tools from the VICTRE clinical trial.

PURPOSE: In silico imaging clinical trials are emerging alternative sources of evidence for regulatory evaluation and are typically cheaper and faster than human trials. In this Note, we describe the set of in silico imaging software tools used in the VICTRE (Virtual Clinical Trial for Regulatory Evaluation) which replicated a traditional trial using a computational pipeline. MATERIALS AND METHODS: We describe a complete imaging clinical trial software package for comparing two breast imaging modalities (digital mammography and digital breast tomosynthesis). First, digital breast models were developed based on procedural generation techniques for normal anatomy. Second, lesions were inserted in a subset of breast models. The breasts were imaged using GPU-accelerated Monte Carlo transport methods and read using image interpretation models for the presence of lesions. All in silico components were assembled into a computational pipeline. The VICTRE images were made available in DICOM format for ease of use and visualization. RESULTS: We describe an open-source collection of in silico tools for running imaging clinical trials. All tools and source codes have been made freely available. CONCLUSION: The open-source tools distributed as part of the VICTRE project facilitate the design and execution of other in silico imaging clinical trials. The entire pipeline can be run as a complete imaging chain, modified to match needs of other trial designs, or used as independent components to build additional pipelines.

Monte Carlo simulation of a realistic anatomical phantom described by triangle meshes: application to prostate brachytherapy imaging.

PURPOSE: Monte Carlo codes can simulate the transport of radiation within matter with high accuracy and can be used to study medical applications of ionising radiations. The aim of our work was to develop a Monte Carlo code capable of generating projection images of the human body. In order to obtain clinically realistic images a detailed anthropomorphic phantom was prepared. These two simulation tools are intended to study the multiple applications of imaging in radiotherapy, from image guided treatments to portal imaging. METHODS: We adapted the general-purpose code PENELOPE 2006 to simulate a radiation source, an ideal digital detector, and a realistic model of the patient anatomy. The anthropomorphic phantom was developed using computer-aided design tools, and is based on the NCAT phantom. The surface of each organ is modelled using a closed triangle mesh, and the full phantom contains 330 organs and more than 5 million triangles. A novel object-oriented geometry package, which includes an octree structure to sort the triangles, has been developed to use this complex geometry with PENELOPE. RESULTS: As an example of the capabilities of the new code, projection images of the human pelvis region were simulated. Radioactive seeds were included inside the phantom's prostate. Therefore, the resulting simulated images resemble what would be obtained in a clinical procedure to assess the positioning of the seeds in a prostate brachytherapy treatment. CONCLUSIONS: The new code can produce projection images of the human body that are comparable to those obtained by a real imaging system (within the limitations of the anatomical phantom and the detector model). The simulated images can be used to study and optimise an imaging task (i.e., maximise the object detectability, minimise the delivered dose, find the optimum beam energy, etc.). Since PENELOPE can simulate radiation from 50 eV to 1 GeV, the code can also be used to simulate radiotherapy treatments and portal imaging. Using the octree data structure, the new geometry model does not significantly increase the computing time when compared to the simulation of a much simpler quadric geometry. In conclusion, we have shown that it is feasible to use PENELOPE and a complex triangle mesh geometry to simulate real medical physics applications.

Reproducing two-dimensional mammograms with three-dimensional printed phantoms.

Mammography is currently the standard imaging modality used to screen women for breast abnormalities, and, as a result, it is a tool of great importance for the early detection of breast cancer. Physical phantoms are commonly used as surrogates of breast tissue to evaluate some aspects of the performance of mammography systems. However, most phantoms do not reproduce the anatomic heterogeneity of real breasts. New fabrication technologies, such as three-dimensional (3-D) printing, have created the opportunity to build more complex, anatomically realistic breast phantoms that could potentially assist in the evaluation of mammography systems. The reproducibility and relative low cost of 3-D printed objects might also enable the development of collections of representative patient models that could be used to assess the effect of anatomical variability on system performance, hence making bench testing studies a step closer to clinical trials. The primary objective of this work is to present a simple, easily reproducible methodology to design and print 3-D objects that replicate the attenuation profile observed in real two-dimensional mammograms. The secondary objective is to evaluate the capabilities and limitations of the competing 3-D printing technologies and characterize the x-ray properties of the different materials they use. Printable phantoms can be created using the open-source code introduced, which processes a raw mammography image to estimate the amount of x-ray attenuation at each pixel, and outputs a triangle mesh object that encodes the observed attenuation map. The conversion from the observed pixel gray value to a column of printed material with equivalent attenuation requires certain assumptions and knowledge of multiple imaging system parameters, such as x-ray energy spectrum, source-to-object distance, compressed breast thickness, and average breast material attenuation. To validate the proposed methodology, x-ray projections of printed phantoms were acquired with a clinical mammography system. The quality of the printing process was evaluated by comparing the mammograms of the printed phantoms and the original mammograms used to create the phantoms. The structural similarity index and the root-mean-square error were used as objective metrics to compare the two images. A detailed description of the software, a characterization of the printed materials using x-ray spectroscopy, and an evaluation of the realism of the sample printed phantoms are presented.

Feasibility of estimating volumetric breast density from mammographic x-ray spectra using a cadmium telluride photon-counting detector.

PURPOSE: Mammographic density of glandular breast tissue has a masking effect that can reduce lesion detection accuracy and is also a strong risk factor for breast cancer. Therefore, accurate quantitative estimation of breast density is clinically important. In this study, we investigate experimentally the feasibility of quantifying volumetric breast density with spectral mammography using a CdTe-based photon-counting detector. METHODS: To demonstrate proof-of-principle, this study was carried out using the single pixel Amptek XR-100T-CdTe detector. The total number of x rays recorded by the detector from a single pencil-beam projection through 50%/50% of adipose/glandular mass fraction-equivalent phantoms was measured. Material decomposition assuming two, four, and eight energy bins was then applied to characterize the inspected phantom into adipose and glandular using log-likelihood estimation, taking into account the polychromatic source, the detector response function, and the energy-dependent attenuation. RESULTS: Measurement tests were carried out for different doses, kVp settings, and different breast sizes. For dose of 1 mGy and above, the percent relative root mean square (RMS) errors of the estimated breast density was measured below 7% for all three phantom studies. It was also observed that some decrease in RMS errors was achieved using eight energy bins. For 3 and 4 cm thick phantoms, performance at 40 and 45 kVp showed similar performance. However, it was observed that 45 kVp showed better performance for a phantom thickness of 6 cm at low dose levels due to increased statistical variation at lower photon count levels with 40 kVp. CONCLUSION: The results of the current study suggest that photon-counting spectral mammography systems using CdTe detectors have the potential to be used for accurate quantification of volumetric breast density on a pixel-to-pixel basis, with an RMS error of less than 7%.