BACKGROUND:Digital phantoms are valuable tools for evaluating small animal imaging systems. They can help optimize imaging parameters before live studies, supporting efforts to reduce animal use and refine experimental protocols. Wistar rats are widely used in preclinical imaging research due to their well-characterized biology and relevance to human disease. PURPOSE:This study develops a series of anatomically variable digital Wistar rat phantoms to support small animal imaging research. METHODS:We constructed 10 computational Wistar rat phantoms (six males, four females) with weights ranging from 188 to 474 g using high-resolution (200 µm) co-registered micro-CT and MRI data to generate detailed 3D anatomical models. The segmented organs and bones, including 35 distinct anatomical tissues, were fitted with smooth polygon mesh surfaces, ensuring flexibility for modeling motion, anatomical variation, and seamless voxelization at any resolution. To demonstrate the utility of the phantoms, we conducted a photon-counting CT (PCCT) simulation using a 279 g female model. The phantom was voxelized into material maps representing soft tissue, iodine contrast, and bone. Simulated PCCT projections were reconstructed using both analytical and iterative methods to compare image quality and accuracy of material decomposition. RESULTS:Measured body sizes and organ masses confirm that the computational phantoms capture inter-subject anatomical variation. Iterative reconstruction outperformed analytical reconstruction in the PCCT simulation, reducing noise and improving material decomposition accuracy. Root mean square error (RMSE) across the water, iodine, and calcium maps decreased from 0.17 g/mL, 2.07 mg/mL, and 11.49 mg/mL to 0.02, 0.21, and 1.98, respectively. CONCLUSIONS:The resulting anatomically variable digital phantoms will provide a valuable resource for preclinical imaging research, enabling the evaluation of imaging systems, optimization of imaging protocols, and validation of reconstruction algorithms across a representative range of anatomies. The demonstrated PCCT study highlights the potential of these phantoms for advancing virtual pre-clinical imaging trials.

Decades of x-ray CT research focuses on iterative reconstruction and denoising methods to alleviate constraints on data sampling and ionizing radiation dose. In particular, multi-channel CT imaging applications (multi-energy, dynamic) remain an active area of research because the relationships across channels (e.g. similar structures across energies, sparse differences over time) enable highly effective data undersampling and reconstruction. Now, deep learning (DL) reconstruction methods are at the forefront of CT research. A key to the success of DL reconstruction methods is their ability to learn data-specific prior information as a supplement for mathematical optimization methods and established priors. Previously, we demonstrated how the split Bregman optimization method can be combined with supervised learning and simulated CT data to enable volumetric, projection and image domain (dual domain) reconstruction of real, single-channel mouse micro-CT data. Here, we update this past work in three ways: (1) we extend the reconstruction framework to handle multi-channel, time-resolved CT data (3D + time); (2) we revise the popular Vision Transformer (ViT) architecture for compatibility with 4D image-to-image processing; and (3) we propose a network training cost function which ties supervised training in a phantom to self-supervised training in real data. We demonstrate these extensions by training an image domain ViT on undersampled MOBY mouse phantom data (36 projections/phase, 10 cardiac phases; supervised) and fully sampled, real mouse micro-CT data (900 projections/phase; self-supervised) and show that the trained network robustly regularizes the reconstruction of real mouse micro-CT when only 300 projections/phase are used for reconstruction.

BACKGROUND:The judicious use of CT in pediatric cardiac applications is warranted because young patients face the need for repeated imaging and increased lifetime cancer risk after ionizing radiation exposure. The quality of pediatric cardiac CT scans is variable because of limited protocols optimizations for pediatric patients, the common presence of metallic implants following treatment, and disparities in denoising algorithm performance between adult and pediatric scans. Two recent technological developments promise to improve the average quality of pediatric CT scans at fixed or reduced dose: clinical photon-counting CT (PCCT) and deep learning (DL) algorithms for CT image denoising. Given advancements to accommodate variable image quality, these technologies will deliver improved spatial resolution, noise performance, and contrast resolution for pediatric cardiac CT imaging. PURPOSE:To advance self-supervised DL denoising methods to accommodate variable image quality in pediatric cardiac CT data. METHODS:Starting with the popular Vision Transformer (ViT) DL architecture, two targeted architectural changes were made: (1) the multi-layer perceptrons (MLPs) were modified to allow cross-token recombination of encoded image data following attention computations (parallels patch-wise weighting and averaging in non-local means [NLM]), and (2) the network head was replaced with the equivalent of an overcomplete dictionary to perform dictionary sparse coding (SC). This modified, 3D ViT (mViT) was then trained in a dynamic fashion: the balance between data fidelity and representation sparsity was adjusted during training such that the average fidelity error remained consistent with localized estimates of image noise. To demonstrate the newly proposed method, the mViT was trained with pediatric cardiac photon-counting x-ray CT data with variable levels of image noise (NAEOTOM Alpha PCCT scanner; retrospective data from 20 patients scanned at Duke University; ages: 1-18 years; iterative reconstruction noise level in the left ventricle: 20-55 HU). Data from one patient with the highest levels of noise was reserved for validation. Testing data included Alpha data from three additional Duke patients (2 < 1 year old) and a murine cardiac PCCT data set acquired on a preclinical system. RESULTS:The validation denoising results demonstrate that SC with the mViT preserves anatomic structures relevant to the diagnosis and treatment of congenital heart defects (coronary artery origins; valve leaflets; left ventricle boundaries) while achieving similar intensity bias and lower intensity variance values than competing denoising methods (bilateral filtration [BF], NLM, dictionary SC, block matching 4D, orthogonal matching pursuit, Noise2Void). Applying the trained mViT network to preclinical PCCT demonstrated robust generalization performance to high levels of image noise (∼230 HU) and differing image contrast; however, applying the network to clinical PCCT data in younger patients (< 1 year old) demonstrated some smoothing of image details in data already heavily denoised during reconstruction. CONCLUSIONS:This work demonstrates robust, self-supervised denoising of pediatric cardiac PCCT data through data adaptation during network training based on local noise estimates. The trained network generalizes to data sets with high levels of noise and differing image contrast relative to the training data, suggesting that self-supervised fine tuning may allow the trained network to address related CT denoising problems.

This study investigates the use of a convolutional neural network to perform micro-CT perfusion quantification. Perfusion CT has demonstrated substantial benefits in human medicine, being widely used for assessing cerebral blood flow in stroke patients, evaluating myocardial perfusion in cardiac diseases, and monitoring tumor vascularity in oncology. The ability to quantify perfusion metrics such as blood flow, blood volume, and mean transit time provides valuable insights into tissue viability and function, aiding diagnosis, treatment planning, and monitoring therapeutic responses. Preclinical micro-CT perfusion imaging holds significant promise in advancing our understanding of various physiological and pathological processes in small animal models. Various methods have been developed to quantify perfusion metrics from CT data, including gamma-variate parameter fittings and deconvolution methods. However, these methods have notable drawbacks, particularly their voxel-by-voxel nature, which can introduce significant noise and variability into the perfusion maps. Deep learning is a promising alternative for perfusion analysis due to its ability to learn complex patterns and relationships from large datasets. In this work, we demonstrate a deep learning approach to perfusion quantification. The network input consists of micro-CT images at 20 timepoints of time-attenuation curves. The output of the network consists of 4 parametric maps representing the numerical parameters of a gamma variate curve. The network was able to predict idealized gamma variate curves from noisy, distorted inputs with a mean absolute percent error of less than 3.4%. When applied to real data, a significant amount of noise was present as expected; however, realistic flow in the inferior vena cava and circle of Willis was visible.

Unruptured intracranial aneurysms (UIAs) pose significant clinical challenges due to their risk of growth and rupture. Early and accurate imaging is essential for effective management and understanding their pathophysiology. This study integrates three advanced imaging modalities-Photon Counting Computed Tomography (PCCT), dynamic perfusion micro-CT, and high-resolution ex vivo vascular micro-CT with MICROFIL-to enhance UIA research using a well-established mouse model. UIAs were induced in 3-5-month-old C57BL/6J mice through angiotensin II infusion and elastase injection. In vivo PCCT imaging with a blood pool contrast agent successfully delineated the Circle of Willis, revealing vascular asymmetries in a fusiform aneurism and increased vessel diameters (similar to 15%), indicative of aneurysmal changes. Dynamic perfusion micro-CT captured temporal blood flow dynamics within the Circle of Willis, producing perfusion maps such as Time-to-Peak and Mean Transit Time, though noise reduction and improved resolution are necessary. High-resolution ex vivo imaging at 22 mu m provided detailed 3D visualizations of the vasculature, but challenges such as vessel rupture and contrast accumulation highlight the need for protocol optimization. This is the first study to demonstrate a comprehensive imaging pipeline for UIAs in preclinical models, combining anatomical, functional, and ultra-high-resolution imaging. These findings lay the groundwork for future advancements in imaging protocols and computational techniques, facilitating deeper insights into aneurysm progression and informing the development of targeted therapies.

Micro-CT imaging studies in mouse cancer models are vital for developing therapeutics. Multi-energy CT imaging with a photon-counting detector (PCD) improves material separation in cancer studies involving nanoparticle-based contrast agents and combination therapy studies involving radiation therapy and/or chemotherapy. However, achieving high quality imaging with photon-counting CT (PCCT) requires scan parameter optimization, which may not always be possible during in vivo cancer studies due to the need to limit radiation dose in live mice. In silico simulation of CT imaging allows extensive scan parameter tuning to improve image quality, but this method has not yet been adapted for mouse cancer studies. This work details our efforts towards an in silico PCCT pipeline for preclinical cancer studies that includes a digital phantom of a mouse with head and neck squamous cell carcinoma (HNSCC). We enhanced the mouse whole body (MOBY) phantom by transferring vasculature from a high-resolution mouse scan and adding a tumor model from CompuCell3D. Our PCCT simulation software models the whole imaging chain and includes a model for spectral distortion. A polynomial-based correction of the distorted spectral response was calibrated using real PCCT scans of known materials. PCCT simulations of the enhanced MOBY phantom with and without polynomial correction were compared to the ground truth using tumor metrics from material maps. Polynomial correction only improved root mean square error for 3 out of 4 known materials, suggesting a better correction is needed. Our simulations of MOBY with a tumor containing iodine and barium nanoparticles reproduced noise and material cross-contamination seen in real PCCT scans of mice with HNSCC. The polynomial correction improved the accuracy of 8 out of 10 tumor metrics across both the iodine and barium maps. Future work will focus on data-driven methods to improve the simulated spectral response, using tumor metrics for imaging parameter optimization, and confirming that image quality improvements in simulation translate to in vivo imaging.

Apolipoprotein E (APOE) genotype and nitric oxide synthase 2 (NOS2) expression are key genetic factors that influence bone health. While prior studies explored differences in bones by APOE genotype, the role of sex and NOS2 require further investigation. In this study, we acquired micro-CT scans of femurs and analyzed their characteristics in a cohort of mice with variation in age, sex, APOE genotype, and humanized NOS2 (HN) expression. Femurs were extracted from 57 mice and scanned using photon-counting micro-CT with two energy thresholds. Scan data was iteratively reconstructed and decomposed into water and calcium material maps. Trabecular and cortical femur features were computed from calcium maps of femurs using ImageJ's BoneJ plugin. Statistical analyses of femur features were applied on the entire cohort of mice and on subgroups stratified by sex, APOE genotype, and HN status. Our image quality assessment found that the calcium map has higher contrast to noise ratio than both energy channels of the iterative reconstruction. Differences in bone volume fraction by APOE genotype were found in the whole group, but this result was not reproduced in subgroups stratified by sex or HN status. Analyses in the whole group and in stratified subgroups revealed that the interaction between sex and HN status was a significant predictor of femur features and that HN females tend to have low trabecular bone content. This study illustrates the benefits of photon-counting CT for femur imaging and shows meaningful effects of age, APOE genotype, sex, HN, and their interactions on bone health.

This study aims to develop a simpler, quicker, and more effective method for ex vivo vascular imaging using VivoVist (TM), a barium-based micro-CT contrast agent, combined with an anticoagulant. VivoVist (TM) (Nanoprobes, Inc.) is a commercially available contrast agent that enables rapid and uniform vascular distribution due to its high water solubility. While previous studies demonstrated its utility for in vivo photon-counting micro-CT, this work evaluates its application in ex vivo imaging as a potential replacement for the lead-based contrast agent MicroFil. Three mice with APOE genotypes were imaged using three distinct preparation protocols. Two mice were scanned with an energy-integrating detector (EID) at 22 mu m resolution, while the third underwent imaging at 65 mu m on a photon-counting CT (PCCT) system with material decomposition capabilities. The results confirm that VivoVist (TM) provides excellent vascular contrast, enabling detailed visualization of vascular structures in key organs such as the lung, liver, heart, and brain. The material decomposition from PCCT enabled segmentation-free vascular separation, which is crucial for applications such as identifying atherosclerotic plaques. The imaging outcomes suggest that a fixative is not always necessary, highlighting the potential for a simplified, reproducible protocol. VivoVist (TM) streamlines imaging workflows by reducing procedural complexity and eliminating the extensive post-mortem steps required for MicroFil. These findings establish VivoVist (TM) as a useful alternative for ex vivo vascular imaging and highlight the synergy between VivoVist (TM) and photon-counting CT for advanced preclinical imaging studies.

This study presents the development, implementation, and testing of a 3D printed phantom with two compartments designed for dynamic micro-CT imaging using low molecular weight contrast agents. The phantom was evaluated through both optical and micro-CT imaging to assess its ability to generate and repeat various time attenuation curves (TACs). The optical tests demonstrated the phantom's capability to produce a wide variety of time contrast curves by adjusting valve positions, while maintaining a repeatable input curve. Micro-CT tests confirmed the generation of diverse time attenuation curves, although repeatability was affected by the increased viscosity of the ISOVUE contrast agent. Using a gamma variate function to model the generated TAC shapes, the phantom was able to generate attenuation starting times ranging from similar to 12-25 seconds and peak times ranging from similar to 25-60 seconds. Having patterned our design after a fully tested clinical CT perfusion phantom, this preclinical phantom promises to be a valuable tool for validating and quantifying perfusion micro-CT measurements. This work represents one of the first adaptations and implementations of a dynamic perfusion phantom for CT at the preclinical level, providing a standardized method for quality assurance in preclinical and research settings. Future research should focus on addressing the observed inconsistencies and exploring the phantom's potential in various preclinical applications.

Cardiac patch-based regenerative therapies have shown great promise in the treatment of myocardial infarction (MI). The clinical applications of patch devices, however, face major limitations mainly due to the inadequate integration of typically nonvascular implanted grafts with the recipient heart muscle tissue, the lack of patient and damage specificity, and insufficient perfusion. Here we present a new generation of cardiac patch devices with customized geometry and vasculature to closely correspond to those of the recipient heart tissue, while providing in-situ imaging properties. Incorporation of multiple computed tomography (CT) contrast agents within hydrogel bioinks enabled longitudinal and quantitative tracking of patch scaffolds both in vitro, in static versus flow culture conditions, and ex vivo using photon-counting CT (PCCT). We also investigated the cardioprotective impact of the bioprinted vascular patch in a rat model of MI. PCCT distinguished the signal from multiple contrast agents to assess perfusion within the vascular patch in vitro, as well as the surgical location, integration and degradation of patch ex vivo. Results establish a novel approach for developing vascular cardiac patches with noninvasive imaging capabilities, addressing critical challenges in monitoring patch engraftment and function, as a powerful tool for advancing translational cardiac regenerative therapies and optimizing clinical outcomes.

In preclinical studies, micro-CT is frequently employed to yield valuable anatomical insights. However, there has been an increasing need for micro-CT in extracting functional measurements such as with perfusion imaging. Perfusion imaging plays a crucial role in understanding and quantifying tissue vascular properties. This paper focuses on our development of preclinical micro-photon counting (PC)CT perfusion imaging and demonstrates quantification of perfusion metrics in a controlled fluid flow phantom experiment. For this study, we utilized a novel bioprinted perfusion phantom and a dedicated preclinical photon-counting CT (PCCT) system to estimate perfusion maps at different flow rates. A continuous water flow through the phantom was maintained by a peristaltic pump. PCCT imaging was performed during a delayed contrast injection of clinical iodinated contrast agent. Imaging was repeated under 3 different flow rates: 4, 6, and 8 mL/min. Our results demonstrate successful visualization and quantification of flow parameters by employing gamma variate curves fit to voxel measurements of temporal PCCT reconstructions as well as decomposed iodine material maps, enabling the calculation of volumetric maps for mean transit time, blood volume index, and blood flow index. Furthermore, we showcase the application of this technique in quantifying in vivo perfusion characteristics in a head and neck cancer model in a mouse. Through our research, we aim to highlight the potential of preclinical micro-PCCT perfusion imaging in advancing our understanding of tissue perfusion dynamics and its potential applications in studying various pathologies and cardiovascular conditions.

PurposeTo identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS).Materials and methodsIn a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast in vivo MRI, three-dimensional (3D) multi-contrast high-resolution ex vivo MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of <0.05 after correction for multiple comparisons were considered significant.ResultsThree features correlated with ex vivo apparent diffusion coefficient (ADC), and no features correlated with in vivo ADC. Six features demonstrated significant linear relationships with ex vivo T2*, and fifteen features correlated significantly with in vivo T2*. In both cases, nuclear Haralick texture features were the most prevalent type of feature correlated with T2*. A small group of nuclear topology features also correlated with one or both T2* contrasts, and positive trends were seen between T2* and nuclear size metrics.ConclusionRegistered multi-parametric imaging datasets can identify quantitative tissue features which contribute to UPS MR signal. T2* may provide quantitative information about nuclear morphology and pleomorphism, adding histological insights to radiological interpretation of UPS.

This study investigated the application of VivoVist™, a high-contrast micro-CT contrast agent, in spectral Photon-Counting (PC) micro-CT imaging in mouse models. With a long blood half-life, superior concentration, and reduced toxicity VivoVist, composed of barium (Ba)-based nanoparticles, offers a cost-effective solution for enhancing Computed Tomography (CT) imaging. To evaluate its efficacy, we employed an in-house developed spectral micro-CT with a photon-counting detector. VivoVist was administered through retro-orbital injection in a non-tumor-bearing C57BL/6 mouse and in two mice with MOC2 buccal tumors, with scans taken at various post-injection intervals. We used a multi-channel iterative reconstruction algorithm to provide multi-energy tomographic images with a voxel size of 125 microns or 75 microns for high-resolution scans; we performed post-reconstruction spectral decomposition with water, calcium (Ca), iodine (I), and barium (Ba) as bases. Our results revealed effective separation of Ba from I-based contrast agents with minimal cross-contamination and superior contrast enhancement for VivoVist at 39 keV. We also observed VivoVist's potential in delineating vasculature in the brain and its decreasing concentration in the blood over time post-injection, with increased uptake in the liver and spleen. We also explored the simultaneous use of VivoVist and liposomal iodinated nanoparticles in a cancer study involving radiation therapy. Our findings reveal that VivoVist, combined with radiation therapy, did not significantly increase liposomal iodine accumulation within head and neck squamous cell carcinoma tumors. In conclusion, our work confirms VivoVist's promising role in enhancing PCCT imaging and its potential in studying combination therapy, warranting further investigation into its applications in diagnostics and radiotherapy.

Background Brain region segmentation and morphometry in humanized apolipoprotein E (APOE) mouse models with a human NOS2 background (HN) contribute to Alzheimer’s disease (AD) research by demonstrating how various risk factors affect the brain. Photon-counting detector (PCD) micro-CT provides faster scan times than MRI, with superior contrast and spatial resolution to energy-integrating detector (EID) micro-CT. This paper presents a pipeline for mouse brain imaging, segmentation, and morphometry from PCD micro-CT. Methods We used brains of 26 mice from 3 genotypes (APOE22HN, APOE33HN, APOE44HN). The pipeline included PCD and EID micro-CT scanning, hybrid (PCD and EID) iterative reconstruction, and brain region segmentation using the Small Animal Multivariate Brain Analysis (SAMBA) tool. We applied SAMBA to transfer brain region labels from our new PCD CT atlas to individual PCD brains via diffeomorphic registration. Region-based and voxel-based analyses were used for comparisons by genotype and sex. Results Together, PCD and EID scanning take ~5 hours to produce images with a voxel size of 22 μm, which is faster than MRI protocols for mouse brain morphometry with voxel size above 40 μm. Hybrid iterative reconstruction generates PCD images with minimal artifacts and higher spatial resolution and contrast than EID images. Our PCD atlas is qualitatively and quantitatively similar to the prior MRI atlas and successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant volume differences in 26 regions, including parts of the entorhinal cortex and cingulate cortex. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus, a region where atrophy is associated with AD. Conclusions This work establishes a pipeline for mouse brain analysis using PCD CT, from staining to imaging and labeling brain images. Our results validate the effectiveness of the approach, setting a foundation for research on AD mouse models while reducing scanning durations.

Alzheimer's disease (AD), a prevalent neurodegenerative disorder, is influenced by an intricate mix of risk factors including age, genetics, and environmental variables. In our study, we employed mouse models with human APOE alleles and nitric oxide synthase 2, and adjusted environmental factors like diet, to replicate controlled genetic risk and innate immune response associated with AD in human subjects. We utilized a Feature Attention Graph Neural Network (FAGNN), integrating brain structural connectomes, genetic traits, environmental factors, and behavioral data, to estimate brain age. Our method demonstrated improved accuracy in age prediction over other methods and highlighted age-associated brain connections. The most impactful connections included the cingulum, striatum, corpus callosum, and hippocampus. We further investigated these findings through fractional anisotropy in different age groups of mice, and our results underlined the significance of white matter degradation in aging. Our results underscore the effectiveness of integrative graph neural networks in predicting brain age and delineating important neural connections associated with brain aging.

Recently, we have released the first open-source version of our Multi-Channel CT Reconstruction (MCR) Toolkit (https://gitlab.oit.duke.edu/dpc18/mcr-toolkit-public). The initial release of the Toolkit represents 10 years of development and provides a complete set of GPU-accelerated tools for solving multi-channel (multi-energy, time-resolved) X-ray CT reconstruction problems with support for both analytical and iterative reconstruction in common preclinical and clinical geometries. This initial version of the Toolkit (v1.0) relies on MATLAB and its MEX interface for orchestrating CT reconstruction pipelines; however, heavy reliance on MATLAB comes with licensing restrictions and limited support for deep learning augmentation of reconstruction pipelines. In this work, we detail the features of v2.0 of the MCR Toolkit which ports all the Toolkit's v1.0 features from MATLAB to the Python programming language, including the ability to perform regularized, iterative reconstruction of multi-energy photon-counting cardiac CT data. We demonstrate these new features through benchmarks which show comparable performance between our MATLAB (v1.0) and Python (v2.0) implementations of the BiCGSTAB(l) solver, following improved memory management in our Python implementation. We also demonstrate a high-level interface between v2.0 of the Toolkit and PyTorch, allowing the incorporation of a previously trained multi-energy CT denoising model, known as UnetU, directly in our multi-channel reconstruction framework. These preliminary reconstruction results show a reduction in intensity bias from 13 HU, after a single pass of the UnetU denoising model, to 7 HU after the same model is incorporated into our iterative reconstruction framework; however, some high-contrast edge features are exaggerated in the UnetU reconstruction, and the noise standard deviation increases from 21 HU to 34 HU.

Objective.This study introduces a novel desktop micro-CT scanner designed for dynamic perfusion imaging in mice, aimed at enhancing preclinical imaging capabilities with high resolution and low radiation doses.Approach.The micro-CT system features a custom-built rotating table capable of both circular and helical scans, enabled by a small-bore slip ring for continuous rotation. Images were reconstructed with a temporal resolution of 3.125 s and an isotropic voxel size of 65µm, with potential for higher resolution scanning. The system's static performance was validated using standard quality assurance phantoms. Dynamic performance was assessed with a custom 3D-bioprinted tissue-mimetic phantom simulating single-compartment vascular flow. Flow measurements ranged from 1.51to 9 ml min-1, with perfusion metrics such as time-to-peak, mean transit time, and blood flow index calculated.In vivoexperiments involved mice with different genetic risk factors for Alzheimer's and cardiovascular diseases to showcase the system's capabilities for perfusion imaging.Main Results.The static performance validation confirmed that the system meets standard quality metrics, such as spatial resolution and uniformity. The dynamic evaluation with the 3D-bioprinted phantom demonstrated linearity in hemodynamic flow measurements and effective quantification of perfusion metrics.In vivoexperiments highlighted the system's potential to capture detailed perfusion maps of the brain, lungs, and kidneys. The observed differences in perfusion characteristics between genotypic mice illustrated the system's capability to detect physiological variations, though the small sample size precludes definitive conclusions.Significance.The turn-table micro-CT system represents a significant advancement in preclinical imaging, providing high-resolution, low-dose dynamic imaging for a range of biological and medical research applications. Future work will focus on improving temporal resolution, expanding spectral capabilities, and integrating deep learning techniques for enhanced image reconstruction and analysis.

The advent of clinical photon counting x-ray CT (PCCT) has yielded gains in spatial resolution, noise performance, contrast resolution, and radiation dose management in numerous diagnostic applications. Pediatric populations, especially, stand to benefit from these gains due to their smaller size relative to adult patients and to greater concerns over lifetime cancer risk associated with ionizing radiation exposure. No where is this more applicable than in the congenital heart population who are repeatedly imaged during their care. Despite these potential advantages, limited protocol optimizations for pediatric patients, the heterogeneity of metallic implants in pediatric cardiac patients, and trade-offs between image quality and dose made by lowering kV and mAs values contribute to variable image quality in pediatric PCCT. Here, we adapt and demonstrate two deep-learning strategies for denoising pediatric cardiac PCCT data which do not require paired training data and which can be trained using data sets of varying image quality. (1) We train a CycleGAN to map CT images between high and low image quality, as gauged by signal-to-noise ratio measurements. The domain mappings are performed with vision transformers (ViTs) which adeptly preserve high spatial frequencies and enable 50% noise reduction across high and low intensity structures (enhanced vasculature, fat). (2) We adapt prior work using masked autoencoders and 2D natural images to the domain of 3D CT data. Specifically, we propose a ViT network structure and cost function which yields robust interpolation of deleted 3D CT data, similarly reducing image noise by 50% in an assessment of validation data. Comparing CycleGAN and masked autoencoder results, the CycleGAN reduces noise by identifying and attenuating high frequency features associated with noise while the masked autoencoder adeptly removes noise textures but visibly smooths high-contrast structures.

Photon Counting Detectors (PCD) have emerged as a transformative technology in CT and micro-CT imaging, offering enhanced contrast resolution and quantitative material separation in a single scan, a notable advancement from traditional energy-integrating detectors. The unique properties of bismuth tungstate (Bi2WO6) nanoparticles (NPs), hold promise in many applications, including contrast-enhanced CT imaging and photothermal therapy, especially in addressing tumor hypoxia challenges. However, despite these promising traits, the performance of PCCT imaging using Bi2WO6 NPs has not been fully explored. Our study bridges this gap by employing both simulations and real experiments. Using iterative PCCT reconstruction, we achieved significant noise reduction, from a noise standard deviation up to 786 Hounsfield Units (HU) down to 54 HU, enabling material decomposition. The dual K-edge of Bi2WO6, coupled with a precise 2:1 Bismuth to Tungsten ratio, offers a unique, quantifiable signature for PCCT imaging: the enhancement of Bi2WO6 remains largely constant over the diagnostic x-ray range (stddev: 1.24 HU/mg/mL over 25-91 keV energy thresholds, 125 kVp spectrum; iodine stddev: 11.62 HU/mg/mL). Improved separation of contrast material from intrinsic tissues promises to enhance all facets of clinical CT, including new avenues for radiation dose and metal artifact reduction. Potential new clinical applications include targeted radiation therapy, where Bi2WO6 NPs could intensify treatment efficacy and optimize chemotherapeutic delivery.

Photon-counting detectors (PCDs) represent a technological advancement in X- ray CT imaging, bringing increased spatial resolution and spectral information to imaging in medical and industrial fields. Despite their potential, a critical issue arises from dead pixel gaps between detector tiles, leading to image artifacts and a reliance on imperfect computational infilling methods. Addressing this challenge, we introduced an acquisition-based solution utilizing a custom-built micro-CT system capable of laterally shifting the PCD during scans. We acquired laterally offset projection data to fill pixel gaps in unshifted projection data. The approach's inherent robustness not only bypasses the need for traditional inpainting or interpolation algorithms but also maintains high quantitative fidelity. Our method shows a marked decrease in low-frequency ring artifacts, surpassing conventional methods in performance. With the potential to be integrated into existing systems or combined with emerging deep learning techniques, our contribution opens promising prospects for future research and applications. Ultimately, this work underscores a significant step toward enhancing image quality and diagnostic precision in X-ray CT imaging, offering a practical and innovative solution to a longstanding problem.