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

BACKGROUND

Archetypical cross-β spines sharpen the boundary between functional and pathological proteins including β-amyloid, tau, α-synuclein and transthyretin are linked to many debilitating human neurodegenerative and non-neurodegenerative amyloidoses. An increased focus on development of pathogenic β-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 β-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-β motif is observed among recombinant α-synuclein fibril, Aβ-42 fibril and artificial β-sheet fibrils. We then use these β-sheet models to assess the performance of spectral small angle X-ray scattering (sSAXS) technique for detecting β-sheet structures. sSAXS showed quantitatively accurate detection of antiparallel, cross-β 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 β-sheet models mimicked the nanoscopic structural characteristics of β-sheets of fibrillar and oligomeric states of Aβ and α-synuclein based on the ATR-FTIR and SAXS data. The tunability of β-sheet proportions and shapes of structural motifs, and the low-cost of these β-sheet models can become useful test materials for evaluating β-sheet or amyloid specific biomarkers in a wide range of neurological diseases. By using the proposed synthetic β-sheet models, our study indicates that the sSAXS has potential to evaluate different stages of β-sheet-enriched structures including oligomers of pathogenic proteins.

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.

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.

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.

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).

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 imageβ-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.

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.

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.

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.

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.

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 β-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 β-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 β-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.

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 β amyloid (βA) 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 βA 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 βA were simulated to assess the method and determine the radiation dose required to obtain acceptable statistical power. For βA 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 βA in the brain within acceptable radiation dose levels.

Transverse chromatic aberration in virtual reality head-mounted displays

We demonstrate a method for measuring the transverse chromatic aberration (TCA) in a virtual reality head-mounted display. The method relies on acquiring images of a digital bar pattern and measuring the displacement of different color bars. This procedure was used to characterize the TCAs in the Oculus Go, Oculus Rift, Samsung Gear, and HTC Vive. The results show noticeable TCAs for the Oculus devices for angles larger than 5° from the center of the field of view. TCA is less noticeable in the Vive in part due to off-axis monochromatic aberrations. Finally, user measurements were conducted, which were in excellent agreement with the laboratory results.

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.

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.

DESIGN, SETTING, AND PARTICIPANTS

The simulated Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) trial was designed to replicate a clinical trial that used human patients and radiologists. Images obtained with in silico versions of DM and DBT systems via fast Monte Carlo x-ray transport were interpreted by a computational reader detecting the presence of lesions. A total of 2986 synthetic image-based virtual patients with breast sizes and radiographic densities representative of a screening population and compressed thicknesses from 3.5 to 6 cm were generated using an analytic approach in which anatomical structures are randomly created within a predefined breast volume and compressed in the craniocaudal orientation. A positive cohort contained a digitally inserted microcalcification cluster or spiculated mass.

MAIN OUTCOMES AND MEASURES

The trial end point was the difference in area under the receiver operating characteristic curve between modalities for lesion detection. The trial was sized for an SE of 0.01 in the change in area under the curve (AUC), half the uncertainty in the comparative clinical trial.

RESULTS

In this trial, computational readers analyzed 31 055 DM and 27 960 DBT cases from 2986 virtual patients with the following Breast Imaging Reporting and Data System densities: 286 (9.6%) extremely dense, 1200 (40.2%) heterogeneously dense, 1200 (40.2%) scattered fibroglandular densities, and 300 (10.0%) almost entirely fat. The mean (SE) change in AUC was 0.0587 (0.0062) (P < .001) in favor of DBT. The change in AUC was larger for masses (mean [SE], 0.0903 [0.008]) than for calcifications (mean [SE], 0.0268 [0.004]), which was consistent with the findings of the comparative trial (mean [SE], 0.065 [0.017] for masses and -0.047 [0.032] for calcifications).

CONCLUSIONS AND RELEVANCE

The results of the simulated VICTRE trial are consistent with the performance seen in the comparative trial. While further research is needed to assess the generalizability of these findings, in silico imaging trials represent a viable source of regulatory evidence for imaging devices.

Method for Adapting the Grayscale Standard Display Function to the Aging Eye

Perceptual linearity of grayscale images based on a contrast sensitivity model is a widely recognized and used standard for medical imaging visualization. This approach ensures consistency across devices and provides perception of luminance variations in direct relationship to changes in image values. We analyze the effect of aging of the human eye on the precept of linearity and demonstrate that not only the number of just-noticeable differences diminishes for older subjects but also linearity across the range of luminance values is significantly affected. While loss of JNDs is inevitable for a fixed luminance range, our findings suggest possible corrective approaches for maintaining linearity.

Small-angle X-ray scattering characteristics of mouse brain: Planar imaging measurements and tomographic imaging simulations

Small-angle x-ray scattering (SAXS) imaging can differentiate tissue types based on their nanoscale molecular structure. However, characterization of the coherent scattering cross-section profile of relevant tissues is needed to optimally design SAXS imaging techniques for a variety of biomedical applications. Reported measured nervous tissue x-ray scattering cross sections under a synchrotron source have had limited agreement. We report a set of x-ray cross-section measurements obtained from planar SAXS imaging of 1 mm thick mouse brain (APP/PS1 wild-type) coronal slices using an 8 keV laboratory x-ray source. Two characteristic peaks were found at 0.96 and 1.60 nm⁻¹ attributed to myelin. The peak intensities varied by location in the slice. We found that regions of gray matter, white matter, and corpus callosum could be segmented by their increasing intensities of myelin peaks respectively. Measured small-angle x-ray scattering cross sections were then used to define brain tissue scattering properties in a GPU-accelerated Monte Carlo simulation of SAXS computed tomography (CT) using a higher monochromatic x-ray energy (20 keV) to study design trade-offs for noninvasive in vivo SAXS imaging on a small-animal head including radiation dose, signal-to-noise ratio (SNR), and the effect of skull presence on the previous two metrics. Simulation results show the estimated total dose to the mouse head for a single SAXS-CT slice was 149.4 mGy. The pixel SNR was approximately 30.8 for white matter material whether or not a skull was present. In this early-stage proof-of-principle work, we have demonstrated our brain cross-section data and simulation tools can be used to assess optimal instrument parameters for dedicated small-animal SAXS-CT prototypes.

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.

Technical Note: Detective quantum efficiency simulation of a-Se imaging detectors using ARTEMIS

PURPOSE

This work studies the detective quantum efficiency (DQE) of a-Se-based solid state x-ray detectors for medical imaging applications using ARTEMIS, a Monte Carlo simulation tool for modeling x-ray photon, electron and charged carrier transport in semiconductors with the presence of applied electric field.

METHODS

ARTEMIS is used to model the signal formation process in a-Se. The simulation model includes x-ray photon and high-energy electron interactions, and detailed electron-hole pair transport with applied detector bias taking into account drift, diffusion, Coulomb interactions, recombination and trapping. For experimental validation, the DQE performance of prototype a-Se detectors is measured following IEC Testing Standard 62220-1-3.

RESULTS

Comparison of simulated and experimental DQE results show reasonable agreement for RQA beam qualities. Experimental validation demonstrated within 5% percentage difference between simulation and experimental DQE results for spatial frequency above 0.25 cycles/mm using uniform applied electric field for RQA beam qualities (RQA5, RQA7 and RQA9). Results include two different prototype detectors with thicknesses of 240 μm and 1 mm.

CONCLUSIONS

ARTEMIS can be used to model the DQE of a-Se detectors as a function of x-ray energy, detector thickness, and spatial frequency. The ARTEMIS model can be used to improve understanding of the physics of x-ray interactions in a-Se and in optimization studies for the development of novel medical imaging applications.

"How much realism is needed?" - the wrong question in silico imagers have been asking

PURPOSE

To discuss the use of realism as a first approximation for assessing computational imaging methods.

METHODS

Although in silico methods are increasingly becoming promising surrogates to physical experimentation for various stages of device development, their acceptance remains challenging. Realism is often considered as a first approximation for assessing computational imaging methods. However, realism is subjective and does not always ensure that key features of the methodologies reflect relevant aspects of devices of interest to imaging scientists, regulators, and medical practitioners. Moreover, in some cases (e.g., in computerized image analysis applications where human interpretation is not needed) how realistic in silico images are is irrelevant and perhaps misleading.

RESULTS

I emphasize a divergence from this methodology by providing a rationale for evaluating in silico imaging methods and tools in an objective and measurable manner.

CONCLUSIONS

Improved approaches for in silico imaging will lead to the rapid advancement and acceptance of computational techniques in medical imaging primarily but not limited to the regulatory evaluation of new imaging products.

Optimization of digital breast tomosynthesis (DBT) acquisition parameters for human observers: effect of reconstruction algorithms

We showed in our earlier work that the choice of reconstruction methods does not affect the optimization of DBT acquisition parameters (angular span and number of views) using simulated breast phantom images in detecting lesions with a channelized Hotelling observer (CHO). In this work we investigate whether the model-observer based conclusion is valid when using humans to interpret images. We used previously generated DBT breast phantom images and recruited human readers to find the optimal geometry settings associated with two reconstruction algorithms, filtered back projection (FBP) and simultaneous algebraic reconstruction technique (SART). The human reader results show that image quality trends as a function of the acquisition parameters are consistent between FBP and SART reconstructions. The consistent trends confirm that the optimization of DBT system geometry is insensitive to the choice of reconstruction algorithm. The results also show that humans perform better in SART reconstructed images than in FBP reconstructed images. In addition, we applied CHOs with three commonly used channel models, Laguerre-Gauss (LG) channels, square (SQR) channels and sparse difference-of-Gaussian (sDOG) channels. We found that LG channels predict human performance trends better than SQR and sDOG channel models for the task of detecting lesions in tomosynthesis backgrounds. Overall, this work confirms that the choice of reconstruction algorithm is not critical for optimizing DBT system acquisition parameters.

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. The former is complicated due to nuances of experimental conditions and uncertainty, while the latter is challenging due to typical graphical presentation and lack of simulation details in previous publications. In addition, entering the field of Monte Carlo simulations in general involves a steep learning curve. It is not a simple task to learn how to program and interpret a Monte Carlo simulation, even when using one of the publicly available code packages. This Task Group report provides a common reference for benchmarking Monte Carlo simulations across a range of Monte Carlo codes and simulation scenarios. In the report, all simulation conditions are provided for six different Monte Carlo simulation cases that involve common x-ray based imaging research areas. The results obtained for the six cases using four publicly available Monte Carlo software packages are included in tabular form. In addition to a full description of all simulation conditions and results, a discussion and comparison of results among the Monte Carlo packages and the lessons learned during the compilation of these results are included. This abridged version of the report includes only an introductory description of the six cases and a brief example of the results of one of the cases. This work provides an investigator the necessary information to benchmark his/her Monte Carlo simulation software against the reference cases included here before performing his/her own novel research. In addition, an investigator entering the field of Monte Carlo simulations can use these descriptions and results as a self-teaching tool to ensure that he/she is able to perform a specific simulation correctly. Finally, educators can assign these cases as learning projects as part of course objectives or training programs.

Effect of color visualization and display hardware on the visual assessment of pseudocolor medical images

PURPOSE

Even though the use of color in the interpretation of medical images has increased significantly in recent years, the ad hoc manner in which color is handled and the lack of standard approaches have been associated with suboptimal and inconsistent diagnostic decisions with a negative impact on patient treatment and prognosis. The purpose of this study is to determine if the choice of color scale and display device hardware affects the visual assessment of patterns that have the characteristics of functional medical images.

METHODS

Perfusion magnetic resonance imaging (MRI) was the basis for designing and performing experiments. Synthetic images resembling brain dynamic-contrast enhanced MRI consisting of scaled mixtures of white, lumpy, and clustered backgrounds were used to assess the performance of a rainbow ("jet"), a heated black-body ("hot"), and a gray ("gray") color scale with display devices of different quality on the detection of small changes in color intensity. The authors used a two-alternative, forced-choice design where readers were presented with 600 pairs of images. Each pair consisted of two images of the same pattern flipped along the vertical axis with a small difference in intensity. Readers were asked to select the image with the highest intensity. Three differences in intensity were tested on four display devices: a medical-grade three-million-pixel display, a consumer-grade monitor, a tablet device, and a phone.

RESULTS

The estimates of percent correct show that jet outperformed hot and gray in the high and low range of the color scales for all devices with a maximum difference in performance of 18% (confidence intervals: 6%, 30%). Performance with hot was different for high and low intensity, comparable to jet for the high range, and worse than gray for lower intensity values. Similar performance was seen between devices using jet and hot, while gray performance was better for handheld devices. Time of performance was shorter with jet.

CONCLUSIONS

Our findings demonstrate that the choice of color scale and display hardware affects the visual comparative analysis of pseudocolor images. Follow-up studies in clinical settings are being considered to confirm the results with patient images.

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.

Monte Carlo X-ray transport simulation of small-angle X-ray scattering instruments using measured sample cross sections

Small-angle X-ray scattering (SAXS) has recently been proposed as a novel noninvasivein vivomolecular imaging technique to characterize molecular interactions deep within the body using high-contrast probes. This article describes a detailed Monte Carlo X-ray transport simulation technique that utilizes user-provided cross sections to describe X-ray interaction in virtual samples and explore SAXS instrument design choices. The accuracy of the simulation code is validated with sample material cross sections derived from analytical models and empirical measurements of a homogeneous spherical gold nanoparticle (GNP) monomer, a dimer and heterogeneous mixtures of the two in aqueous solution. Analytical and measured scattering profiles from these samples were converted to cross sections using an absolute water standard. Our Monte Carlo estimates of the fraction of dimers from analytically derived and empirically derived cross sections are strongly correlated, with less than 1.5 and 16% error, respectively, to the expected concentration of monomer and dimer species. In addition, a variety of monoenergetic X-ray beams were simulated to investigate coherent scatteringversusradiation dose for a range of sample sizes. For GNP spheres in aqueous solution, the energy range that produces the most coherent scattering at the detector per deposited energy was between 31 and 49 keV for a sample thickness of 1 mm to 10 cm. The method described here for the detailed simulation of SAXS using measured and modeled cross sections will enable instrumentation optimization forin vivomolecular imaging applications.

Small-angle X-ray scattering method to characterize molecular interactions: Proof of concept

Characterizing biomolecular interactions is crucial to the understanding of biological processes. Existing characterization methods have low spatial resolution, poor specificity, and some lack the capability for deep tissue imaging. We describe a novel technique that relies on small-angle X-ray scattering signatures from high-contrast molecular probes that correlate with the presence of biomolecular interactions. We describe a proof-of-concept study that uses a model system consisting of mixtures of monomer solutions of gold nanoparticles (GNPs) as the non-interacting species and solutions of GNP dimers linked with an organic molecule (dimethyl suberimidate) as the interacting species. We report estimates of the interaction fraction obtained with the proposed small-angle X-ray scattering characterization method exhibiting strong correlation with the known relative concentration of interacting and non-interacting species.