Toward non-clinical validation of fractional flow reserve simulation software: A generic coronary artery geometry.

Medical image-based diagnostic techniques have become increasingly common in the clinic. Estimating fractional flow reserve in coronary stenoses from medical image data is among the most prominent examples. The modeling techniques used in these clinical tools require rigorous experimental validation yet there is currently no standardized, public toolset to help assess model credibility.

Credibility assessment of in silico clinical trials for medical devices.

In silico clinical trials (ISCTs) are an emerging method in modeling and simulation where medical interventions are evaluated using computational models of patients. ISCTs have the potential to provide cost-effective, time-efficient, and ethically favorable alternatives for evaluating the safety and effectiveness of medical devices. However, ensuring the credibility of ISCT results is a significant challenge.

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.

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.

The emerging regulatory roles of non-coding RNAs associated with glucose metabolism in breast cancer.

Altered energy metabolism is one of the hallmarks of tumorigenesis and essential for fulfilling the high demand for metabolic energy in a tumor through accelerating glycolysis and reprogramming the glycolysis metabolism through the Warburg effect. The dysregulated glucose metabolic pathways are coordinated not only by proteins coding genes but also by non-coding RNAs (ncRNAs) during the initiation and cancer progression. The ncRNAs are responsible for regulating numerous cellular processes under developmental and pathological conditions.

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.

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.

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).

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.

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.

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.

Fast Monte Carlo codes for occupational dosimetry in interventional radiology.

Purpose: Interventional radiology techniques cause radiation exposure both to patient and personnel. The radiation dose to the operator is usually measured with dosimeters located at specific points above or below the lead aprons. The aim of this study is to develop and validate two fast Monte Carlo (MC) codes for radiation transport in order to improve the assessment of individual doses in interventional radiology.

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

: 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.

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

A recent study reported on an 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 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.

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 procedural analytic breast and breast mass modeling algorithms followed by simulated x-ray projections of the breast models into mammographic images. 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.

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 mm 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.

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).

Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial.

Importance: Expensive and lengthy clinical trials can delay regulatory evaluation of innovative technologies, affecting patient access to high-quality medical products. Simulation is increasingly being used in product development but rarely in regulatory applications.

Objectives: To conduct a computer-simulated imaging trial evaluating digital breast tomosynthesis (DBT) as a replacement for digital mammography (DM) and to compare the results with a comparative clinical trial.

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.

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.

Stable gelatin-based phantom materials with tunable x-ray attenuation properties and 3D printability for x-ray imaging.

We report a novel method for developing gelatin-based phantom materials for transmission x-ray imaging with high stability at room temperature and tunable x-ray attenuation properties. This is achieved by efficiently cross-linking gelatin in a glycerin solution with only 10% water by volume and systematically decreasing their x-ray attenuation coefficients by doping with microbubbles that are originally designed to be used as lightweight additives for paints and crack fillers. For demonstration, we mimic breast glandular and adipose tissues by using such gelatin materials and also study the feasibility of 3D printing them based on the extrusion-based technique.

A novel physical anthropomorphic breast phantom for 2D and 3D x-ray imaging.

Purpose: Physical phantoms are central to the evaluation of 2D and 3D breast-imaging systems. Currently, available physical phantoms have limitations including unrealistic uniform background structure, large expense, or excessive fabrication time. The purpose of this work is to outline a method for rapidly creating realistic, inexpensive physical anthropomorphic phantoms for use in full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT).

Maximum-likelihood estimation of scatter components algorithm for x-ray coherent scatter computed tomography of the breast.

Coherent scatter computed tomography (CSCT) is a reconstructive x-ray imaging technique that yields the spatially resolved coherent-scatter cross section of the investigated object revealing structural information of tissue under investigation. In the original CSCT proposals the reconstruction of images from coherently scattered x-rays is done at each scattering angle separately using analytic reconstruction. In this work we develop a maximum likelihood estimation of scatter components algorithm (ML-ESCA) that iteratively reconstructs images using a few material component basis functions from coherent scatter projection data.

Monte Carlo reference data sets for imaging research: Executive summary of the report of AAPM Research Committee Task Group 195.

The use of Monte Carlo simulations in diagnostic medical imaging research is widespread due to its flexibility and ability to estimate quantities that are challenging to measure empirically. However, any new Monte Carlo simulation code needs to be validated before it can be used reliably. The type and degree of validation required depends on the goals of the research project, but, typically, such validation involves either comparison of simulation results to physical measurements or to previously published results obtained with established Monte Carlo codes.