X-ray speckle-based dark-field imaging offers high sensitivity to sub-pixel structural features, yet its quantitative reliability in clinical and preclinical settings remains constrained by low photon flux and finite focal spot sizes. However, how hardware-induced noise and source blurring propagate through retrieval algorithms to degrade signal integrity is not fully understood. Here, we systematically evaluate algorithm robustness-quantified by signal linearity, sensitivity, and bias-under photon starvation and source blurring across two mathematically distinct frameworks: differential-based intrinsic tracking (Low-Coherence System, LCS) and patch-wise explicit tracking (X-ray Speckle-Tracking Speckle-Vector-Tracking, XST-XSVT). Our experimental results demonstrate that input speckle pattern distortions propagate through retrieval algorithms in fundamentally different ways depending on algorithm architecture. As an example, using our setup, under severe photon starvation (exposure reduced from 50 s to 1 s per mask step), derivative noise amplification in LCS causes its dark-field signal linearity and sensitivity to drop precipitously by 85.7% and 91.3%, respectively, while sharply elevating baseline bias. In contrast, XST-XSVT restricts these losses to 37.4% for linearity and 64.2% for sensitivity while maintaining a stable baseline, as its patch-wise variance calculation inherently suppresses stochastic noise. Similarly, under blur-limited conditions (expanding focal spot size from 7 μm to 50 μm), source blurring washes out the speckle pattern, directly reducing dark-field sensitivity for both LCS (by 47.8%) and XST-XSVT (by 42.6%). Beyond this shared sensitivity loss, the pattern smoothing causes the differential equations in LCS to become mathematically unstable, degrading its linearity by 5.3% and elevating baseline bias. Conversely, XST-XSVT robustly withstands the smoothed pattern, bypassing this instability to maintain linearity with a negligible 1.0% drop. This characterization establishes operational boundaries for low-power and low-coherence X-ray systems, guiding algorithm selection and framework optimization to realize quantitative dark-field imaging in preclinical and clinical applications.

We aim to investigate the impact of image and signal properties on visual attention mechanisms during a signal detection task in digital images. The application of insight yielded from this work spans many areas of digital imaging where signal or pattern recognition is involved in complex heterogenous background. We will use simulated tomographic breast images as the platform to investigate this question. While radiologists are highly effective at analyzing medical images to detect and diagnose diseases, misdiagnosis still occurs. These errors can stem from multiple sources such as the inherent properties of medical images and the accompanying signal characteristics play a pivotal role in visual search strategy while locating and identifying the disease. We selected digital breast tomosynthesis (DBT) images as a sample medical images with different breast densities and structures using digital breast phantoms (Bakic and XCAT). Two types of lesions (with distinct spatial frequency properties) were randomly inserted in the phantoms during projections to generate abnormal cases. Six non-radiologist human observers participated in observer study designed for a locating and detection of an 3-mm sphere lesion and 6-mm spicule lesion in reconstructed in-plane DBT slices. We collected eye-gaze data to estimate gaze metrics and to examine differences in visual attention mechanisms. Gaze analysis revealed that diagnostic response times were significantly longer in Bakic phantoms and high-density tissue backgrounds compared to XCAT breast phantoms (p ¡ 0.05) and lower-density backgrounds (p ¡ 0.01) respectively, indicating increased perceptual difficulty in anatomically complex scenes. Observers made longer fixations on spiculated lesions compared to spherical lesions (p ¡ 0.01), suggesting that lesion morphology modulates visual attention allocation. Across all attention stages-search, recognition, and decision-the small spherical lesion required higher contrast for successful detection, especially in the Bakic background where anatomical noise severely reduced its visibility. Error analysis showed that the majority of misdiagnosed cases (25%) occurred when the lesion was not visible, followed by decision-stage errors (11%), where the signal was seen but incorrectly judged. Conclusions:Diagnostic performance in complex visual environments is strongly constrained by later perceptual stages, with decision failures accounting for the largest proportion of errors. Lesion detectability is jointly influenced by both target morphology and background complexity, revealing a critical interaction between local signal features and global anatomical noise. Increased fixation duration on spiculated lesions suggests that visual attention is differentially engaged depending on lesion structure, potentially aiding recognition. These findings highlight the importance of perceptually informed design and training of computer aided diagnosis systems.

Understanding human visual search behavior is a fundamental problem in vision science and computer vision, with direct implications for modeling how observers allocate attention in location-unknown search tasks. In this study, we investigate the relationship between Gabor-based features and gray-level co-occurrence matrix (GLCM)-based texture features in modeling early-stage visual search behavior. Two feature-combination pipelines are proposed to integrate Gabor and GLCM features for narrowing the region of possible human fixations. The pipelines are evaluated using simulated digital breast tomosynthesis images. Results show qualitative agreement among fixation candidates predicted by the proposed pipelines and a threshold-based model observer. A strong correlation ( r = 0.765 ) is observed between GLCM mean and Gabor feature responses, indicating that these features encode related image information despite their different formulations. Eye-tracking data from human observers further suggest consistency between predicted fixation regions and early-stage gaze behavior. These findings highlight the value of combining structural and texture-based features for modeling visual search and support the development of perceptually informed observer models.

While X-ray dark-field imaging (XDFI) provides unique access to sub-resolution microstructural information, its clinical translation is often limited by the high costs and complexity of traditional grating-based systems. Speckle-based XDFI has emerged as a compelling, low-cost alternative. However, its performance under the real-world constraints of medical imaging such as limited photon flux and large focal spot sizes requires further validation. In this work, we present a feasibility study focused on the practical implementation of speckle-based XDFI for medical applications. We present a high-resolution speckle-based XDFI system utilizing a high-efficiency photon-counting detector with 55 mu m pixels. We experimentally evaluate the impact of experimental setting on dark-field signal, comparing two dark-field retrieval strategies: explicit and intrinsic speckle tracking. Using biological and clinical samples, including fish anatomy and kidney stones, we demonstrate that the explicit tracking method provides significantly higher contrast-to-noise ratio and better preserves fine details under low-dose conditions. Our findings suggest that with the correct tracking approach, speckle-based XDFI is a robust candidate for translation into standard clinical environments, providing dark-field signals with high-sensitivity without the need for complex hardware.

Continuous-wave near-infrared diffuse optical imaging relies on accurate differential pathlength factors (DPFs) for quantitative chromophore estimation. Existing DPF definitions inherit formulation-dependent limitations that can introduce large errors in modified Beer–Lambert law analyses. Using Monte Carlo simulations, we derive two distance- and property-dependent DPF models—one ideal and the other experimentally practicalhich we label as our new Inversedistance Model here). We benchmark them against existing formulations. The proposed models achieve errors below 15% across broad optical conditions, whereas conventional DPFs can exceed 100% error. The theoretical predictions are further validated using controlled phantom experiments, demonstrating improved quantitative accuracy in CW-NIR imaging.

K-edge subtraction (KES) imaging can isolate contrast-enhanced structures with high specificity, but its accuracy degrades when photon-counting detector data are noisy, spectrally distorted, and sparsely sampled. This work presents a sparse-to-dense spectral correction framework based on a transformer neural network. The model reconstructs a dense threshold scan from a small number of measured thresholds, enabling denoising and spectral interpolation in a single forward pass. Across non-iodine materials, dual-anchor training with PMMA and aluminum improves generalization relative to single-material calibration and produces corrected spectra that track theoretical behavior more closely. The iodine experiments then show the important role of task-specific calibration: a model trained without iodine suppresses noise but does not preserve the K-edge discontinuity well enough for quantitative subtraction, whereas iodine-representative calibration preserves the expected attenuation profile of a 10 wt% KI tubing phantom and supports successful unweighted K-edge subtraction. Taken together, the results show that deep learning can be an effective sparse spectral denoiser, but quantitative K-edge imaging requires material-specific training data that represent the target contrast mechanism.

Breast density is defined as the relative proportion of fibroglandular to fatty tissue, a well-established risk factor for breast cancer with dense breasts but remains difficult to assess reliably due to the subjectivity of visual inspection. Existing methods use algorithms or subjective segmentation methods to extract relative volumes of higher density regions from mammographic or tomographic images. Here we examine the feasibility of using second order texture features that can reliably discriminate breast density from mammographic images even when visual examination or segmentation-based methods may be challenging. We developed three gelatin-based phantoms with varying plastic bead concentrations were to simulate low, medium and high-density fibroglandular tissue content. The phantoms were imaged using a benchtop microfocus X-ray source coupled with a Medipix photon-counting detector operated at 50 kVp, 0.5 mA for 50s. Flat-field corrected images were quantized 128 gray levels, divided into non-overlapping ROIs of 250 x 250, 250 x 50, fullimage pixels, and analyzed using gray-level co-occurrence matrices (GLCM) computed across four directions with pixel offsets of 1-30. Among the extracted texture features, cluster shade and contrast consistently distinguished phantom densities across all quantization levels and ROI sizes. Cluster shade demonstrated particularly strong separation for 250 x 250, with high, medium, and low-density phantoms, effectively capturing gray-level asymmetry differences between density levels. Cluster Prominence and Cluster Tendency for 250 x 50 was able to discriminate the three breast density phantoms. These results highlight the potential of GLCM texture features, particularly cluster shade, Cluster Prominence and Cluster Tendency for objective and quantitative breast density characterization, offering a promising approach to overcome the limitations of subjective visual assessment in mammography screening. We will examine the benefits of these simple computational methods against existing methods that involve segmenting breast images to extract high density volumes for estimating overall breast density.

X-ray phase-contrast imaging (XPCI) enhances soft-tissue visualization by measuring phase shifts in the X-ray wavefront, while its associated dark-field (DF) modality captures ultra-small-angle scattering from sub-resolution structures. Phase contrast imaging has demonstrated strong potential for visualizing soft-tissue interfaces with low dose and high contrast, which is critical for early lesions detection, mammography, and small animal imaging. Dark-field imaging is particularly promising for lung imaging due to its sensitivity to alveolar pathological changes that are invisible in attenuation. Among laboratory-compatible methods, speckle-based and single-mask approaches are attractive for their translation potential due to simplicity. However, direct comparisons of their differential phase and dark-field sensitivity under matched conditions has not been demonstrated yet. In this study, we present a head-to-head evaluation of single-mask and speckle-based XPCI using Monte Carlo simulations and experimental measurements with controlled phantoms. Simulations were performed on a contrast-detail phantom to compare differential phase sensitivity across a range of refraction angles and feature sizes. Experimental results confirmed that both methods distinguish microstructures beyond attenuation contrast, with single-mask imaging showing higher dose efficiency. These findings provide quantitative guidance for selecting XPCI methods in compact and translational imaging systems.

Breast microcalcifications play a crucial role in the early detection of breast cancer, but current mammographic techniques struggle to differentiate between benign and potentially malignant cancers or calcifications non-invasively. We examine the role of novel phase imaging methods and new detectors for this problem. Propagation based phase imaging, speckle based and single-mask phase imaging each can offer various phase imaging signatures which can change with geometry, detector pitch and other parameters. Here we examine possibilities to distinguish between type I (calcium oxalate) and type II (hydroxyapatite) microcalcifications as well as normal vs. cancerous breast tissue. We employed a polychromatic microfocus x-ray source and two advanced photon-counting detectors: the Widepix3, based on Medipix3RX technology, and the high-resolution BrillianSe detector. Our method simultaneously acquires attenuation and ultra small angle scattering signals or dark field across multiple energy bins (10-50 keV). Phantom experiments with calcium oxalate and hydroxyapatite samples revealed distinct energy-dependent scattering profiles, particularly above 25 keV, while attenuation signals remained similar. Measurements made on breast tissue samples showed low dark field signatures at least with existing methods. Future work will combine our single mask method and high resolution detector to examine such potential in calcifications.

Background/Objectives: There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. While we use digital breast tomosynthesis (DBT) to show these effects, our results would be generally applicable to a wider range of other imaging modalities and applications. Methods: We examine factors in texture estimation methods, such as quantization, pixel distance offset, and region of interest (ROI) size, that influence the magnitudes of these readily computable and widely used image texture features (specifically Haralick’s gray level co-occurrence matrix (GLCM) textural features). Results: Our results indicate that quantization is the most influential of these parameters, as it controls the size of the GLCM and range of values. We propose a new multi-resolution normalization (by either fixing ROI size or pixel offset) that can significantly reduce quantization magnitude disparities. We show reduction in mean differences in feature values by orders of magnitude; for example, reducing it to 7.34% between quantizations of 8–128, while preserving trends. Conclusions: When combining images from multiple vendors in a common analysis, large variations in texture magnitudes can arise due to differences in post-processing methods like filters. We show that significant changes in GLCM magnitude variations may arise simply due to the filter type or strength. These trends can also vary based on estimation variables (like offset distance or ROI) that can further complicate analysis and robustness. We show pathways to reduce sensitivity to such variations due to estimation methods while increasing the desired sensitivity to patient-specific information such as breast density. Finally, we show that our results obtained from simulated DBT images are consistent with what we see when applied to clinical DBT images.

X-ray phase-contrast imaging (XPCI) and dark-field (DF) imaging can potentially provide enhanced contrast beyond attenuation by detecting phase shifts and ultra-small-angle X-ray scattering (USAXS), but most implementations rely on multiple exposures, very high magnification geometry, precision motion, or ultra-high-resolution detectors. In this study, we present a single-mask, single-exposure framework that captures attenuation, differential phase contrast (DPC), and dark-field using mask–pixel alignment technique, which encodes sub-pixel beamlet shifts and broadening into pixel-scale intensity differences on moderate-pitch detectors. We present three configurations that utilize the same hardware: the established single-mask DPC arrangement and two new configurations, DF-only and combined DF–DPC. The desired configuration is selectable by a simple positional adjustment of the mask. We also developed a unified light-transport model that describes signal formation across each of the three configurations and enables accurate single-shot retrieval. Benefiting from the mask-pixel alignment technique and the physics model, our approach eliminates the need for ultra-fine detector resolution as well as mechanical stepping with multiple exposures, thus offering a fast, compact, low-cost, low-dose, and reconfigurable path to advanced multi-contrast X-ray imaging for clinical and industrial applications. These features would also make dark-field and differential phase computed tomography significantly more practical and clinically translatable.

Optical imaging methods have the potential to overcome many of the drawbacks posed by current breast imaging modalities. Previous studies have found that mammographic compression induces different hemodynamic effects in cancerous and healthy breast tissue. This effect could be exploited in continuous-wave near-infrared spectroscopic imaging (CW-NIRS) for fast and accurate breast cancer screening. The primary issue with this approach is that breast tissue (and the cancerous mass) is displaced during the compression process, potentially introducing a considerable amount of errors and noise into the NIRS measurements with the current simple models used in estimating blood volume (and/or oxy/deoxy Hb) concentrations. In this work, we examine how these errors change with signal depth with breast compression and investigate methods to correct these based on simulations and experiments.

Phase imaging has been utilized successfully to improve soft material contrast in optical microscopy. This talk will present the challenges and opportunities in yielding phase contrast with high energy photons such as X-rays. Some of the aspects that separate phase imaging and phase retrieval in X-ray to optical domain will be highlighted. Our work on incorporating new and advanced detectors, phase imaging geometries and the corresponding light transport model to enable a translatable phase imaging for deep tissue will be presented. I will discuss potential applications of our work to advance breast screening, lung imaging, pathology, tissue margin assessment and X-ray microscopy. Full-text article not available; see video presentation

X-ray imaging, traditionally relying on attenuation contrast, struggles to differentiate materials with similar attenuation coefficients like soft tissues. X-ray phase contrast imaging (XPCI) and dark-field (DF) imaging provide enhanced contrast by detecting phase shifts and ultra-small-angle X-ray scattering (USAXS). However, they typically require complex and costly setups, along with multiple exposures to retrieve various contrast features. In this study, we introduce a novel single-mask X-ray imaging system design that simultaneously captures attenuation, differential phase contrast (DPC), and dark-field images in a single exposure. Most importantly, our proposed system design requires just a single mask alignment with relatively low-resolution detectors. Using our novel light transport models derived for these specific system designs, we show intuitive understanding of contrast formation and retrieval method of different contrast features. Our approach eliminates the need for highly coherent X-ray sources, ultra-high-resolution detectors, spectral detectors or intricate gratings. We propose three variations of the single-mask setup, each optimized for different contrast types, offering flexibility and efficiency in a variety of applications. The versatility of this single-mask approach along with the use of befitting light transport models holds promise for broader use in clinical diagnostics and industrial inspection, making advanced X-ray imaging more accessible and cost-effective.

Simulation methods in breast imaging offer advantages over clinical trials in terms of improved reproducibility, reduced need for patient exposure to radiation, increased flexibility, and more clearly defined ground truth. Simulation also allows for improved representation of anatomical variations and variations in acquisition parameters and breast positioning related to multimodality imaging. The increasing use of virtual clinical trials (VCTs) to assess breast imaging systems has introduced a demand to optimize protocols for simulation studies. This work will contribute to developing standards for evaluation tools for 3D/4D breast imaging systems and will ultimately reduce the reliance on clinical trials for emerging systems.This report reviews key aspects of VCTs, including the simulation of realistic breast anatomy, the generation of synthetic images from virtual phantoms, the use of model observers to assess imaging system performance, and methods to analyze observer outputs. Each section reviews the state of the science and recommends approaches for accomplishing tasks related to the individual aspects of VCTs. The report also reviews the experience of designing and using a simulation approach from the industrial and regulatory perspective. Finally, future steps in the development of VCTs are suggested. breast cancer imaging, evaluation of imaging systems, virtual trials

Texture analysis holds significant importance in various imaging fields due to its ability to provide statistical, structural, and intrinsic spatial information from images. In this work, we examine several first and second-order texture features on simulated and clinical DBT images. We examined some essential characteristics of texture features that show higher discriminatory potential for mass detection in digital breast tomosynthesis. We further examined the use of these texture features along with morphological features in a two stage visual search (VS) model observer for mass detection in DBT. Our preliminary results show that incorporation of texture features reduced the number of suspicious locations in the first stage of VS model. Our preliminary results with an eye tracking system and observer gaze points align well with the "search" regions predicted by either the texture aided or thresholded VS observer. In summary, we show how additing perceptually relevant texture features or a thresholding mechanism enhances our visual search observer models. Future work will examine feature selections for changing tasks.

X-ray phase contrast and dark field imaging offer significant potential to overcome the limitations of traditional absorption-based imaging by enhancing contrast for soft tissues and reducing radiation doses. These advanced imaging techniques respectively leverage phase and small-angle X-ray scattering (SAXS) information to provide superior visualization of soft materials and unresolved micro-structures. In this study, we introduce an innovative benchtop multi-contrast imaging and computed tomography (CT) system capable of simultaneously capturing absorption, differential phase, phase, and dark-field images in a single shot per projection angle. Our innovative imaging setup addresses common challenges in the field by eliminating the need for a highly coherent X-ray source, an ultra-high-resolution detector, or complexly fabricated X-ray gratings, thereby simplifying the implementation and reducing associated costs. The proposed system's versatility and efficiency make it a promising tool for various biomedical applications, offering a significant advancement in X-ray imaging technology.

Purpose: Digital phantoms are one of the key components of virtual imaging trials (VITs) that aim to assess and optimize new medical imaging systems and algorithms. However, these phantoms vary in their voxel resolution, appearance, and structural details. This study aims to examine whether and how variations between digital phantoms influence system optimization with digital breast tomosynthesis (DBT) as a chosen modality. Methods: We selected widely used and open-access digital breast phantoms generated with different methods. For each phantom type, we created an ensemble of DBT images to test acquisition strategies. Human observer localization ROC (LROC) was used to assess observer performance studies for each case. Noise power spectrum (NPS) was estimated to compare the phantom structural components. Further, we computed several gaze metrics to quantify the gaze pattern when viewing images generated from different phantom types. Results: Our LROC results show that the arc samplings for peak performance were approximately 2.5 degrees and 6 degrees in Bakic and XCAT breast phantoms respectively for 3-mm lesion detection tasks and indicate that system optimization outcomes from VITs can vary with phantom types and structural frequency components. Additionally, a significant correlation (p= 0.01) between gaze metrics and diagnostic performance suggests that gaze analysis can be used to understand and evaluate task difficulty in VITs.

Spectral capabilities of photon counting detectors (PCDs) can allow material decomposition. We recently showed multi-material decomposition using high resolution photon counting detectors (Medipix3). High-resolution photon counting spectral detectors have unique advantages and challenges. Some of the challenges arise from noise properties as well as spectral distortions. We show benefits of an empirical correction method to obtain accurate attenuation values in a spectral CT even with high resolution detectors and a combination of spectral distortions. Aided with accurate spectral correction, we show that a Gaussian mixture model assisted iterative decomposition can separate multiple materials at once. Our group has been investigating the role of image texture features in signal detection performance in tomographic images. Here we will explore utilizing variations in image texture features in spectral CT material decomposition. Along with attenuation variations for each energy bin, second order statistical texture feature variations associated with spectral data will be used to reduce the number of energy bins and imaging dose to perform multi-material decomposition. With promising preliminary results, we will show a more thorough investigation of image texture variations in spectral data to assist efficient and low dose material decomposition in spectral CT.

Photon counting detectors (PCDs) offer promising advancements in computed tomography (CT) imaging by enabling the quantification and 3D imaging of contrast agents and tissue types through multi-energy projections. However, the accuracy of these decomposition methods hinges on precise composite spectral attenuation values that one must reconstruct from spectral micro CT. Factors such as surface defects, local temperature, signal amplification, and impurity levels can cause variations in detector efficiency between pixels, leading to significant quantitative errors. In addition, some inaccuracies such as the charge-sharing effects in PCDs are amplified with a high Z sensor material and also with a smaller detector pixels that are preferred for micro CT. In this work, we propose a comprehensive approach that combines practical instrumentation and measurement strategies leading to the quantitation of multiple materials within an object in a spectral micro CT with a photon counting detector. Our Iterative Clustering Material Decomposition (ICMD) includes an empirical method for detector spectral response corrections, cluster analysis and multi-step iterative material decomposition. Utilizing a CdTe-1mm Medipix detector with a 55$\mu$m pitch, we demonstrate the quantitatively accurate decomposition of several materials in a phantom study, where the sample includes mixtures of material, soft material and K-edge materials. We also show an example of biological sample imaging and separating three distinct types of tissue in mouse: muscle, fat and bone. Our experimental results show that the combination of spectral correction and high-dimensional data clustering enhances decomposition accuracy and reduces noise in micro CT. This ICMD allows for quantitative separation of more than three materials including mixtures and also effectively separates multi-contrast agents.