Open-Source Data Analysis Tool for Spectral Small-Angle X-ray Scattering Using Spectroscopic Photon-Counting Detector.

Spectral small-angle X-ray scattering (sSAXS) is a powerful technique for material characterization from thicker samples by capturing elastic X-ray scattering data in angle- and energy-dispersive modes at small angles. This approach is enabled by the use of a 2D spectroscopic photon-counting detector that provides energy and position information of scattered photons when a sample is irradiated by a polychromatic X-ray beam. Here, we describe an open-source tool with a graphical interface for analyzing sSAXS data obtained from a 2D spectroscopic photon-counting detector with a large number of energy bins. The tool takes system geometry parameters and raw detector data to output 1D scattering patterns and a 2D spatially-resolved scattering map in the energy range of interest. We validated these features using data from samples of caffeine powder with well-known scattering peaks. This open-source tool will facilitate sSAXS data analysis for various material characterization applications.

Proceedings Virtual Imaging Trials in Medicine 2024.

This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday.

In situtumor model for longitudinal in silico imaging trials.

Objective.In this article, we introduce a computational model for simulating the growth of breast cancer lesions accounting for the stiffness of surrounding anatomical structures.Approach.In our model, ligaments are classified as the most rigid structures while the softer parts of the breast are occupied by fat and glandular tissues As a result of these variations in tissue elasticity, the rapidly proliferating tumor cells are met with differential resistance. It is found that these cells are likely to circumvent stiffer terrains such as ligaments, instead electing to proliferate preferentially within the more yielding confines of the breast's soft topography. By manipulating the interstitial tumor pressure in direct proportion to the elastic constants of the tissues surrounding the tumor, this model thus creates the potential for realizing a database of unique lesion morphology sculpted by the distinctive topography of each local anatomical infrastructure. We modeled the growth of simulated lesions within volumes extracted from fatty breast models, developed by Graffet alwith a resolution of 50µm generated with the open-source and readily available Virtual Imaging Clinical Trials for Regulatory Evaluation (VICTRE) imaging pipeline. To visualize and validate the realism of the lesion models, we leveraged the imaging component of the VICTRE pipeline, which replicates the siemens mammomat inspiration mammography system in a digital format. This system was instrumental in generating digital mammogram (DM) images for each breast model containing the simulated lesions.Results.By utilizing the DM images, we were able to effectively illustrate the imaging characteristics of the lesions as they integrated with the anatomical backgrounds. Our research also involved a reader study that compared 25 simulated DM regions of interest (ROIs) with inserted lesions from our models with DM ROIs from the DDSM dataset containing real manifestations of breast cancer. In general the simulation time for the lesions was approximately 2.5 hours, but it varied depending on the lesion's local environment.Significance.The lesion growth model will facilitate and enhance longitudinal in silico trials investigating the progression of breast cancer.

Synthetic ß-sheets mimicking fibrillar and oligomeric structures for evaluation of spectral X-ray scattering technique for biomarker quantification.

BACKGROUND: Archetypical cross-ß spines sharpen the boundary between functional and pathological proteins including ß-amyloid, tau, α-synuclein and transthyretin are linked to many debilitating human neurodegenerative and non-neurodegenerative amyloidoses. An increased focus on development of pathogenic ß-sheet specific fluid and imaging structural biomarkers and conformation-specific monoclonal antibodies in targeted therapies has been recently observed. Identification and quantification of pathogenic oligomers remain challenging for existing neuroimaging modalities. RESULTS: We propose two artificial ß-sheets which can mimic the nanoscopic structural characteristics of pathogenic oligomers and fibrils for evaluating the performance of a label free, X-ray based biomarker detection and quantification technique. Highly similar structure with elliptical cross-section and parallel cross-ß motif is observed among recombinant α-synuclein fibril, Aß-42 fibril and artificial ß-sheet fibrils. We then use these ß-sheet models to assess the performance of spectral small angle X-ray scattering (sSAXS) technique for detecting ß-sheet structures. sSAXS showed quantitatively accurate detection of antiparallel, cross-ß artificial oligomers from a tissue mimicking environment and significant distinction between different oligomer packing densities such as diffuse and dense packings. CONCLUSION: The proposed synthetic ß-sheet models mimicked the nanoscopic structural characteristics of ß-sheets of fibrillar and oligomeric states of Aß and α-synuclein based on the ATR-FTIR and SAXS data. The tunability of ß-sheet proportions and shapes of structural motifs, and the low-cost of these ß-sheet models can become useful test materials for evaluating ß-sheet or amyloid specific biomarkers in a wide range of neurological diseases. By using the proposed synthetic ß-sheet models, our study indicates that the sSAXS has potential to evaluate different stages of ß-sheet-enriched structures including oligomers of pathogenic proteins.

Proceedings of the NHLBI Workshop on Artificial Intelligence in Cardiovascular Imaging: Translation to Patient Care.

Artificial intelligence (AI) promises to revolutionize many fields, but its clinical implementation in cardiovascular imaging is still rare despite increasing research. We sought to facilitate discussion across several fields and across the lifecycle of research, development, validation, and implementation to identify challenges and opportunities to further translation of AI in cardiovascular imaging. Furthermore, it seemed apparent that a multidisciplinary effort across institutions would be essential to overcome these challenges. This paper summarizes the proceedings of the National Heart, Lung, and Blood Institute-led workshop, creating consensus around needs and opportunities for institutions at several levels to support and advance research in this field and support future translation.

Objective Image Quality Optimization in Augmented Reality Using Spatial Frequency Domain Models.

Augmented reality (AR) blends the digital and physical worlds by overlapping a virtual image onto the see-through physical environment. However, contrast reduction and noise superposition in an AR head-mounted display (HMD) can substantially limit image quality and human perceptual performance in both the digital and physical spaces. To assess image quality in AR, we performed human and model observer studies for various imaging tasks with targets placed in the digital and physical worlds. A target detection model was developed for the complete AR system including the optical see-through. Target detection performance using different observer models developed in the spatial frequency domain was compared with the human observer results. The non-prewhitening model with eye filter and internal noise results closely track human perception performance as measured by the area under the receiver operating characteristic curve (AUC), especially for tasks with high image noise. The AR HMD non-uniformity limits the low-contrast target (less than 0.02) observer performance for low image noise. In augmented reality conditions, the detectability of a target in the physical world is reduced due to the contrast reduction by the overlaid AR display image (AUC less than 0.87 for all the contrast levels evaluated). We propose an image quality optimization scheme to optimize the AR display configurations to match observer detection performance for targets in both the digital and physical worlds. The image quality optimization procedure is validated using both simulation and bench measurements of a chest radiography image with digital and physical targets for various imaging configurations.

Spatiotemporal image quality of virtual reality head mounted displays.

Virtual reality (VR) head mounted displays (HMDs) require both high spatial resolution and fast temporal response. However, methods to quantify the VR image quality in the spatiotemporal domain when motion exists are not yet standardized. In this study, we characterize the spatiotemporal capabilities of three VR devices: the HTC VIVE, VIVE Pro, and VIVE Pro 2 during smooth pursuit. A spatiotemporal model for VR HMDs is presented using measured spatial and temporal characteristics. Among the three evaluated headsets, the VIVE Pro 2 improves the display temporal performance using a fast 120 Hz refresh rate and pulsed emission with a small duty cycle of 5%. In combination with a high pixel resolution beyond 2 k [Formula: see text] 2 k per eye, the VIVE Pro 2 achieves an improved spatiotemporal performance compared to the VIVE and VIVE Pro in the high spatial frequency range above 8 cycles per degree during smooth pursuit. The result demonstrates that reducing the display emission duty cycle to less than 20% is beneficial to mitigate motion blur in VR HMDs. Frame rate reduction (e.g., to below 60 Hz) of the input signal compared to the display refresh rate of 120 Hz yields replicated shadow images that can affect the image quality under motion. This work supports the regulatory science research efforts in development of testing methods to characterize the spatiotemporal performance of VR devices for medical use.

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.

Evaluation Challenges for the Application of Extended Reality Devices in Medicine.

Augmented and virtual reality devices are being actively investigated and implemented for a wide range of medical uses. However, significant gaps in the evaluation of these medical devices and applications hinder their regulatory evaluation. Addressing these gaps is critical to demonstrating the devices' safety and effectiveness. We outline the key technical and clinical evaluation challenges discussed during the US Food and Drug Administration's public workshop, "Medical Extended Reality: Toward Best Evaluation Practices for Virtual and Augmented Reality in Medicine" and future directions for evaluation method development. Evaluation challenges were categorized into several key technical and clinical areas. Finally, we highlight current efforts in the standards communities and illustrate connections between the evaluation challenges and the intended uses of the medical extended reality (MXR) devices. Participants concluded that additional research is needed to assess the safety and effectiveness of MXR devices across the use cases.

Beam orientation optimization for coherent X-ray scattering from distributed deep targets.

BACKGROUND: Amyloid deposits in the temporal and frontal lobes in patients with Alzheimer's disease make them potential targets to aid in early diagnosis. Recently, spectral small-angle X-ray scattering techniques have been proposed for interrogating deep targets such as amyloid plaques. RESULTS: We describe an optimization approach for the orientation of beams for deep target characterization. The model predicts the main features of scattering profiles from targets with varying shape, size and location. We found that increasing target size introduced additional smearing due to location uncertainty, and incidence angle affected the scattering profile by altering the path length or effective target size. For temporal and frontal lobe targets, beam effectiveness varied up to 2 orders of magnitude. CONCLUSIONS: Beam orientation optimization might allow for patient-specific optimal paths for improved signal characterization.

In silico imaging clinical trials: cheaper, faster, better, safer, and more scalable.

Imaging clinical trials can be burdensome and often delay patient access to novel, high-quality medical devices. Tools for in silico imaging trials have significantly improved in sophistication and availability. Here, I describe some of the principal advantages of in silico imaging trials and enumerate five lessons learned during the design and execution of the first all-in silico virtual imaging clinical trial for regulatory evaluation (the VICTRE study).

Color Rendering in Medical Extended-Reality Applications.

Cross-platform development of medical applications in extended-reality (XR) head-mounted displays (HMDs) often relies on game engines with rendering capabilities currently not standardized in the context of medical visualizations. Many aspects of the visualization pipeline including the characterization of color have yet to be consistently defined across rendering models and platforms. We examined the transfer of color properties from digital objects, through the rendering and image processing steps, to the RGB values sent to the display device. Five rendering pipeline configurations within the Unity engine were evaluated using 24 digital color patches. In the second experiment, the same configurations were evaluated with a tissue slide sample image. Measurements of the change in color associated with each configuration were characterized using the CIE 1976 color difference ([Formula: see text]). We found that the distribution of [Formula: see text] for the first experiment ranges from zero, as in the case using an Unlit Shader, to 25.97, as in the case using default configurations. The default Unity configuration consistently returned the highest [Formula: see text] across all 24 colors and also the largest range of color differences. In the second experiment, [Formula: see text]E ranged from 7.49 to 34.18. The Unlit configuration resulted in the highest [Formula: see text] in three of four selected pixels in the tissue sample image. Changes in color image properties associated with texture import settings were then evaluated in a third experiment using the TG18-QC test pattern. Differences in pixel values were found in all nine of the investigated texture import settings. The findings provide an initial characterization of color transfer and a basis for future work on standardization, consistency, and optimization of color in medical XR applications.

Label-free X-ray estimation of brain amyloid burden.

Amyloid plaque deposits in the brain are indicative of Alzheimer's and other diseases. Measurements of brain amyloid burden in small animals require laborious post-mortem histological analysis or resource-intensive, contrast-enhanced imaging techniques. We describe a label-free method based on spectral small-angle X-ray scattering with a polychromatic beam for in vivo estimation of brain amyloid burden. Our findings comparing 5XFAD versus wild-type mice correlate well with histology, showing promise for a fast and practical in vivo technique.

Practical application of AAPM Report 270 in display quality assurance: A report of Task Group 270.

Published in January 2019, AAPM Report 270 provides an update to the recommendations of the AAPM's "TG18" report. Report 270 provides new definitions of display types, updated testing patterns, and revised performance standards for the modern, flat-panel displays used as part of medical image acquisition and review. The focus of the AAPM report is on consistent image quality and appearance, and how to establish a quality assurance program to achieve those two goals. This work highlights some of the key takeaways of AAPM Report 270 and makes comparisons with existing recommendations from other references. It also provides guidance for establishing a display quality assurance program for different-sized institutions. Finally, it describes future challenges for display quality assurance and what work remains.

Feasibility of a label-free X-ray method to estimate brain amyloid load in small animals.

BACKGROUND: Amyloid plaque in the brain is associated with a wide range of neurodegenerative diseases such as Alzheimer's and Parkinson's and defined as aggregates of amyloid fibrils rich in ß-sheet structures. NEW METHOD: We report a label-free method based on small-angle X-ray scattering (SAXS) to estimate amyloid load in an intact mouse head with skull. The method is based on recording and analyzing the X rays elastically scattered from the ß-sheets of amyloid plaques in a mouse head at angles smaller than 10° and energies between 30 and 45 keV. The method is demonstrated by acquiring the spectral SAXS data of an amyloid model and an excised head from a wild-type mouse for 600 s. RESULTS: We captured the distinct scattering peaks of the amyloid plaques at momentum transfer (q) of 6 and 13 nm-1 associated with ß-sheet structure. We first show linear correlation between the mass fraction of the amyloid target and the area under the peak (AUP) of the scattering curve. We report results for estimating amyloid load in a fixed mouse head by recovering the characteristic scattering signal from the amyloid target situated at various locations. The coefficient of variation in the amyloid load estimate is found to be less than 10%. COMPARISON WITH EXISTING METHODS: There are no previously described label-free X-ray methods for the estimation of amyloid load in an intact head. CONCLUSIONS: We demonstrated the feasibility of a label-free method based on SAXS to potentially estimate brain amyloid in small animals.

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.

Small-angle X-ray scattering characterization of a [Formula: see text]-amyloid model in phantoms.

OBJECTIVE: We present a method to prepare an amyloid model at scalable quantities for phantom studies to evaluate small-angle x-ray scattering systems for amyloid detection. Two amyloid models were made from a plasma protein with and without heating. Both models mimic the [Formula: see text]-sheet structure of the [Formula: see text]-amyloid ([Formula: see text]) plaques in Alzheimer's disease. Amyloid detection is based on the distinct peaks in the scattering signature of the [Formula: see text]-sheet structure. We characterized the amyloid models using a spectral small-angle x-ray scattering (sSAXS) prototype with samples in a plastic syringe and within a cylindrical polymethyl methacrylate (PMMA) phantom. RESULTS: sSAXS data show that we can detect the scattering peaks characteristic of amyloid [Formula: see text]-sheet structure in both models around 6 and 13 [Formula: see text]. The [Formula: see text] model prepared without heating provides a stronger signal in the PMMA phantom. The methods described can be used to prepare models in sufficiently large quantities and used in samples with different packing density to assess the performance of [Formula: see text] quantification systems.

Identification of amyloid plaques in the brain using an x-ray photon-counting strip detector.

Brain aggregates of ß amyloid (ßA) protein plaques have been widely recognized as associated with many neurodegenerative diseases, and their identification can assist in the early diagnosis of Alzheimer's disease. We investigate the feasibility of using a spectral x-ray coherent scatter system with a silicon strip photon-counting detector for identifying brain ßA protein plaques. This approach is based on differences in the structure of amyloid, white and grey matter in the brain. We simulated an energy- and angular-dispersive X-ray diffraction system with an x-ray pencil beam and Silicon strip sensor, energy-resolving detectors. The polychromatic beam is geometrically focused toward a region of interest in the brain. First, the open-source MC-GPU code for Monte Carlo transport was modified to accommodate the detector model. Second, brain phantoms with and without ßA were simulated to assess the method and determine the radiation dose required to obtain acceptable statistical power. For ßA targets of 3, 4 and 5 mm sizes in a 15-cm brain model, the required incident exposure was about 0.44 mR from a 60 kVp tungsten spectrum and 3.5 mm of added aluminum filtration. The results suggest that the proposed x-ray coherent scatter technique enables the use of high energy x-ray spectra and therefore has the potential to be used for accurate in vivo detection and quantification of ßA in the brain within acceptable radiation dose levels.

Feasibility of imaging amyloid in the brain using small-angle x-ray scattering.

Small-angle x-ray scattering (SAXS) imaging may have the potential to imageß-amyloid plaquesin vivoin the brain without tracers for assessment of Alzheimer's disease (AD). We use a laboratory SAXS system for planar imaging of AD model and control mouse brains slices to detect regions with high density of amyloid plaques. These regions were validated with histology methods. Using Monte Carlo techniques, we simulate SAXS computed tomography (SAXS-CT) system to study the potential of selectively differentiating amyloid targets in mouse and human head phantoms with detailed anatomy. We found contrast between amyloid and brain tissue at smallq(below 0.8 nm-1) in the neocortex region of the transgenic brain slices as supported by histology. We observed similar behavior through planar SAXS imaging of an amyloid-like fibril deposit with a 0.8 mm diameter at a known location on a wild type mouse brain. In our SAXS-CT simulations, we found that 33-keV x rays provide increase plaque visibility in the mouse head for targets of at least 0.1 mm in diameter, while in the human head, 70-keV x rays were capable of detecting plaques as small as 2 mm. To increase radiation efficiency, we used a weighted-sum image visualization approach allowing the dose deposited by 70-keV x rays per SAXS-CT slice of the human head to be reduced by a factor of 10 to 71 mGy for gray matter and 63 mGy for white matter. The findings suggest that a dedicated SAXS-CT system forin vivoamyloid imaging in small animals and humans can be successfully developed with further system optimization to detect regions with amyloid plaques in the brain with a safe level of radiation dose.