Functional imaging of tumor vasculature using iodine and gadolinium-based nanoparticle contrast agents: a comparison of spectral micro-CT using energy integrating and photon counting detectors

Noise characterization analysis of dynamic dual-energy CT and its advantage in suppressing statistical noise

Objective.Multi-energy CT conducted by photon-counting detector has a wide range of applications, especially in multiple contrast agents imaging. However, static multi-energy (SME) CT imaging suffers from higher statistical noise because of increased energy bins with static energy thresholds. Our team has proposed a dynamic dual-energy (DDE) CT detector model and the corresponding iterative reconstruction algorithm to solve this problem. However, rigorous and detailed analysis of the statistical noise characterization in this DDE CT was lacked.Approach.Starting from the properties of the Poisson random variable, this paper analyzes the noise characterization of the DDE CT and compares it with the SME CT. It is proved that the multi-energy CT projections and reconstruction images calculated from the proposed DDE CT algorithm have less statistical noise than that of the SME CT.Main results.Simulations and experiments verify that the expectations of the multi-energy CT projections calculated from DDE CT are the same as those of the SME projections. Still, the variance of the former is smaller. We further analyze the convergence of the iterative DDE CT algorithm through simulations and prove that the derived noise characterization can be realized under different CT imaging configurations.Significance.The low statistical noise characteristics demonstrate the value of DDE CT imaging technology.

To Reconstruct or Discard: A Comparison of Additive and Subtractive Charge Sharing Correction Algorithms at High and Low X-ray Fluxes

Effective X-ray photon-counting spectral imaging (x-CSI) detector design involves the optimisation of a wide range of parameters both regarding the sensor (e.g., material, thickness and pixel pitch) and electronics (e.g., signal-processing chain and count-triggering scheme). Our previous publications have looked at the role of pixel pitch, sensor thickness and a range of additive charge sharing correction algorithms (CSCAs), and in this work, we compare additive and subtractive CSCAs to identify the advantages and disadvantages. These CSCAs differ in their approach to dealing with charge sharing: additive approaches attempt to reconstruct the original event, whilst subtractive approaches discard the shared events. Each approach was simulated on data from a wide range of x-CSI detector designs (pixel pitches 100-600 µm, sensor thickness 1.5 mm) and X-ray fluxes (106-109 photons mm-2 s-1), and their performance was characterised in terms of absolute detection efficiency (ADE), absolute photopeak efficiency (APE), relative coincidence counts (RCC) and binned spectral efficiency (BSE). Differences between the two approaches were explained mechanistically in terms of the CSCA's effect on both charge sharing and pule pileup. At low X-ray fluxes, the two approaches perform similarly, but at higher fluxes, they differ in complex ways. Generally, additive CSCAs perform better on absolute metrics (ADE and APE), and subtractive CSCAs perform better on relative metrics (RCC and BSE). Which approach to use will, thus, depend on the expected operating flux and whether dose efficiency or spectral efficiency is more important for the application in mind.

Contrast-enhanced photon-counting micro-CT of tumor xenograft models

Objective. Photon-counting micro-computed tomography (micro-CT) is a major advance in small animal preclinical imaging. Small molecule- and nanoparticle-based contrast agents have been widely used to enable the differentiation of liver tumors from surrounding tissues using photon-counting micro-CT. However, there is a notable gap in the application of these market-available agents to the imaging of breast and ovarian tumors using photon-counting micro-CT. Herein, we have used photon-counting micro-CT to determine the effectiveness of these contrast agents in differentiating ovarian and breast tumor xenografts in live, intact mice.Approach. Nude mice carrying different types of breast and ovarian tumor xenografts (AU565, MDA-MB-231 and SKOV-3 human cancer cells) were injected with ISOVUE-370 (a small molecule-based agent) or Exitron Nano 12000 (a nanoparticle-based agent) and subjected to photon-counting micro-CT. To improve tumor visualization using photon-counting micro-CT, we developed a novel color visualization method, which changes color tones to highlight contrast media distribution, offering a robust alternative to traditional material decomposition methods with less computational demand.Main results. Ourin vivoexperiments confirm the effectiveness of this color visualization approach, showing distinct enhancement characteristics for each contrast agent. Qualitative and quantitative analyses suggest that Exitron Nano 12000 provides superior vasculature enhancement and better quantitative consistency across scans, while ISOVUE-370 delivers a more comprehensive tumor enhancement but with significant variance between scans due to its short blood half-time. Further, a paired t-test on mean and standard deviation values within tumor volumes showed significant differences between the AU565 and SKOV-3 tumor models with the nanoparticle-based contrast agent (p-values < 0.02), attributable to their distinct vascularity, as confirmed by immunohistochemical analysis.Significance. These findings underscore the utility of photon-counting micro-CT in non-invasively assessing the morphology and anatomy of different tumor xenografts, which is crucial for tumor characterization and longitudinal monitoring of tumor progression and response to treatments.

Accurate Reconstruction of Multiple Basis Images Directly From Dual Energy CT Data

OBJECTIVE

We develop optimization-based algorithms to accurately reconstruct multiple ( 2) basis images directly from dual-energy (DE) data in CT.

METHODS

In medical and industrial CT imaging, some basis materials such as bone, metals, and contrast agents of interest are confined often spatially within regions in the image. Exploiting this observation, we develop an optimization-based algorithm to reconstruct, directly from DE data, basis-region images from which multiple ( 2) basis images and virtual monochromatic images (VMIs) can be obtained over the entire image array.

RESULTS

We conduct experimental studies using simulated and real DE data in CT, and evaluate basis images and VMIs obtained in terms of visual inspection and quantitative metrics. The study results reveal that the algorithm developed can accurately and robustly reconstruct multiple ( 2) basis images directly from DE data.

CONCLUSIONS

The developed algorithm can yield accurate multiple ( 2) basis images, VMIs, and physical quantities of interest from DE data in CT.

SIGNIFICANCE

The work may provide insights into the development of practical procedures for reconstructing multiple basis images, VMIs, and physical quantities from DE data in applications. The work can be extended to reconstruct multiple basis images in multi-spectral or photon-counting CT.

High-resolution hybrid micro-CT imaging pipeline for mouse brain region segmentation and volumetric morphometry

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.

Edge-illumination spectral phase-contrast tomography

Following the rapid, but independent, diffusion of x-ray spectral and phase-contrast systems, this work demonstrates the first combination of spectral and phase-contrast computed tomography (CT) obtained by using the edge-illumination technique and a CdTe small-pixel (62μm) spectral detector. A theoretical model is introduced, starting from a standard attenuation-based spectral decomposition and leading to spectral phase-contrast material decomposition. Each step of the model is followed by quantification of accuracy and sensitivity on experimental data of a test phantom containing different solutions with known concentrations. An example of a micro CT application (20μm voxel size) on an iodine-perfusedex vivomurine model is reported. The work demonstrates that spectral-phase contrast combines the advantages of spectral imaging, i.e. high-Zmaterial discrimination capability, and phase-contrast imaging, i.e. soft tissue sensitivity, yielding simultaneously mass density maps of water, calcium, and iodine with an accuracy of 1.1%, 3.5%, and 1.9% (root mean square errors), respectively. Results also show a 9-fold increase in the signal-to-noise ratio of the water channel when compared to standard spectral decomposition. The application to the murine model revealed the potential of the technique in the simultaneous 3D visualization of soft tissue, bone, and vasculature. While being implemented by using a broad spectrum (pink beam) at a synchrotron radiation facility (Elettra, Trieste, Italy), the proposed experimental setup can be readily translated to compact laboratory systems including conventional x-ray tubes.

Contrast-enhanced photon-counting micro-CT of tumor xenograft models

Photon-counting micro computed tomography (micro-CT) offers new potential in preclinical imaging, particularly in distinguishing materials. It becomes especially helpful when combined with contrast agents, enabling the differentiation of tumors from surrounding tissues. There are mainly two types of contrast agents in the market for micro-CT: small molecule-based and nanoparticle-based. However, despite their widespread use in liver tumor studies, there is a notable gap in research on the application of these commercially available agents for photon-counting micro-CT in breast and ovarian tumors. Herein, we explored the effectiveness of these agents in differentiating tumor xenografts from various origins (AU565, MDA-MB-231, and SKOV-3) in nude mice, using photon-counting micro-CT. Specifically, ISOVUE-370 (a small molecule-based agent) and Exitrone Nano 12000 (a nanoparticle-based agent) were investigated in this context. To improve tumor visualization, we proposed a novel color visualization method for photon-counting micro-CT, which changes color tones to highlight contrast media distribution, offering a robust alternative to traditional material decomposition methods with less computational demand. Our in vivo experiments confirm its effectiveness, showing distinct enhancement characteristics for each contrast agent. Qualitative and quantitative analyses suggested that Exitrone Nano 12000 provides superior vasculature enhancement and better quantitative consistency across scans, while ISOVUE-370 gives more comprehensive tumor enhancement but with a significant variance between scans due to its short blood half-time. This variability leads to high sensitivity to timing and individual differences among mice. Further, a paired t-test on mean and standard deviation values within tumor volumes showed significant differences between the AU565 and SKOV-3 tumor models with the nanoparticle-based (p-values < 0.02), attributable to their distinct vascularity, as confirmed by immunohistochemistry. These findings underscore the utility of photon-counting micro-CT in non-invasively assessing the morphology and anatomy of different tumor xenografts, which is crucial for tumor characterization and longitudinal monitoring of tumor development and response to treatments.

Accuracy and Precision of Iodine Quantification in Subtracted Micro-Computed Tomography: Effect of Reconstruction and Noise Removal Algorithms

PURPOSE

To evaluate the effect of reconstruction and noise removal algorithms on the accuracy and precision of iodine concentration (CI) quantified with subtracted micro-computed tomography (micro-CT).

PROCEDURES

Two reconstruction algorithms were evaluated: a filtered backprojection (FBP) algorithm and a simultaneous iterative reconstruction technique (SIRT) algorithm. A 3D bilateral filter (BF) was used for noise removal. A phantom study evaluated and compared the image quality, and the accuracy and precision of CI in four scenarios: filtered FBP, filtered SIRT, non-filtered FBP, and non-filtered SIRT. In vivo experiments were performed in an animal model of chemically-induced mammary cancer.

RESULTS

Linear relationships between the measured and nominal CI values were found for all the scenarios in the phantom study (R2 > 0.95). SIRT significantly improved the accuracy and precision of CI compared to FBP, as given by their lower bias (adj. p-value = 0.0308) and repeatability coefficient (adj. p-value < 0.0001). Noise removal enabled a significant decrease in bias in filtered SIRT images only; non-significant differences were found for the repeatability coefficient. The phantom and in vivo studies showed that CI is a reproducible imaging parameter for all the scenarios (Pearson r > 0.99, p-value < 0.001). The contrast-to-noise ratio showed non-significant differences among the evaluated scenarios in the phantom study, while a significant improvement was found in the in vivo study when SIRT and BF algorithms were used.

CONCLUSIONS

SIRT and BF algorithms improved the accuracy and precision of CI compared to FBP and non-filtered images, which encourages their use in subtracted micro-CT imaging.

Advanced photon counting CT imaging pipeline for cardiac phenotyping of apolipoprotein E mouse models

BACKGROUND

Cardiovascular disease (CVD) is associated with the apolipoprotein E (APOE) gene and lipid metabolism. This study aimed to develop an imaging-based pipeline to comprehensively assess cardiac structure and function in mouse models expressing different APOE genotypes using photon-counting computed tomography (PCCT).

METHODS

123 mice grouped based on APOE genotype (APOE2, APOE3, APOE4, APOE knockout (KO)), gender, human NOS2 factor, and diet (control or high fat) were used in this study. The pipeline included PCCT imaging on a custom-built system with contrast-enhanced in vivo imaging and intrinsic cardiac gating, spectral and temporal iterative reconstruction, spectral decomposition, and deep learning cardiac segmentation. Statistical analysis evaluated genotype, diet, sex, and body weight effects on cardiac measurements.

RESULTS

Our results showed that PCCT offered high quality imaging with reduced noise. Material decomposition enabled separation of calcified plaques from iodine enhanced blood in APOE KO mice. Deep learning-based segmentation showed good performance with Dice scores of 0.91 for CT-based segmentation and 0.89 for iodine map-based segmentation. Genotype-specific differences were observed in left ventricular volumes, heart rate, stroke volume, ejection fraction, and cardiac index. Statistically significant differences were found between control and high fat diets for APOE2 and APOE4 genotypes in heart rate and stroke volume. Sex and weight were also significant predictors of cardiac measurements. The inclusion of the human NOS2 gene modulated these effects.

CONCLUSIONS

This study demonstrates the potential of PCCT in assessing cardiac structure and function in mouse models of CVD which can help in understanding the interplay between genetic factors, diet, and cardiovascular health.

Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients

OBJECTIVES

Evaluate a novel algorithm for noise reduction in obese patients using dual-source dual-energy (DE) CT imaging.

METHODS

Seventy-nine patients with contrast-enhanced abdominal imaging (54 women; age: 58 ± 14 years; BMI: 39 ± 5 kg/m2, range: 35-62 kg/m2) from seven DECT (SOMATOM Flash or Force) were retrospectively included (01/2019-12/2020). Image domain data were reconstructed with the standard clinical algorithm (ADMIRE/SAFIRE 2), and denoised with a comparison (ME-NLM) and a test algorithm (rank-sparse kernel regression). Contrast-to-noise ratio (CNR) was calculated. Four blinded readers evaluated the same original and denoised images (0 (worst)-100 (best)) in randomized order for perceived image noise, quality, and their comfort making a diagnosis from a table of 80 options. Comparisons between algorithms were performed using paired t-tests and mixed-effects linear modeling.

RESULTS

Average CNR was 5.0 ± 1.9 (original), 31.1 ± 10.3 (comparison; p < 0.001), and 8.9 ± 2.9 (test; p < 0.001). Readers were in good to moderate agreement over perceived image noise (ICC: 0.83), image quality (ICC: 0.71), and diagnostic comfort (ICC: 0.6). Diagnostic accuracy was low across algorithms (accuracy: 66, 63, and 67% (original, comparison, test)). The noise received a mean score of 54, 84, and 66 (p < 0.05); image quality 59, 61, and 65; and the diagnostic comfort 63, 68, and 68, respectively. Quality and comfort scores were not statistically significantly different between algorithms.

CONCLUSIONS

The test algorithm produces quantitatively higher image quality than current standard and existing denoising algorithms in obese patients imaged with DECT and readers show a preference for it.

CLINICAL RELEVANCE STATEMENT

Accurate diagnosis on CT imaging of obese patients is challenging and denoising algorithms can increase the diagnostic comfort and quantitative image quality. This could lead to better clinical reads.

KEY POINTS

• Improving image quality in DECT imaging of obese patients is important for accurate and confident clinical reads, which may be aided by novel denoising algorithms using image domain data. • Accurate diagnosis on CT imaging of obese patients is especially challenging and denoising algorithms can increase quantitative and qualitative image quality. • Image domain algorithms can generalize well and can be implemented at other institutions.

The technical development of photon-counting detector CT

Since 1971 and Hounsfield's first CT system, clinical CT systems have used scintillating energy-integrating detectors (EIDs) that use a two-step detection process. First, the X-ray energy is converted into visible light, and second, the visible light is converted to electronic signals. An alternative, one-step, direct X-ray conversion process using energy-resolving, photon-counting detectors (PCDs) has been studied in detail and early clinical benefits reported using investigational PCD-CT systems. Subsequently, the first clinical PCD-CT system was commercially introduced in 2021. Relative to EIDs, PCDs offer better spatial resolution, higher contrast-to-noise ratio, elimination of electronic noise, improved dose efficiency, and routine multi-energy imaging. In this review article, we provide a technical introduction to the use of PCDs for CT imaging and describe their benefits, limitations, and potential technical improvements. We discuss different implementations of PCD-CT ranging from small-animal systems to whole-body clinical scanners and summarize the imaging benefits of PCDs reported using preclinical and clinical systems. KEY POINTS: • Energy-resolving, photon-counting-detector CT is an important advance in CT technology. • Relative to current energy-integrating scintillating detectors, energy-resolving, photon-counting-detector CT offers improved spatial resolution, improved contrast-to-noise ratio, elimination of electronic noise, increased radiation and iodine dose efficiency, and simultaneous multi-energy imaging. • High-spatial-resolution, multi-energy imaging using energy-resolving, photon-counting-detector CT has been used in investigations into new imaging approaches, including multi-contrast imaging.

MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data

BACKGROUND

The advancement of x-ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new challenges and opportunities for clinicians and researchers. To address challenges such as dose constraints and scanning times while capitalizing on opportunities such as multi-contrast imaging and low-dose coronary angiography, these multi-channel imaging applications require a new generation of CT reconstruction tools. These new tools should exploit the relationships between imaging channels during reconstruction to set new image quality standards while serving as a platform for direct translation between the preclinical and clinical domains.

PURPOSE

We outline and demonstrate a new Multi-Channel Reconstruction (MCR) Toolkit for GPU-based analytical and iterative reconstruction of preclinical and clinical multi-energy and dynamic x-ray CT data. To promote open science, open-source distribution of the Toolkit will coincide with the release of this publication (GPL v3; gitlab.oit.duke.edu/dpc18/mcr-toolkit-public).

METHODS

The MCR Toolkit source code is implemented in C/C++ and NVIDIA's CUDA GPU programming interface, with scripting support from MATLAB and Python. The Toolkit implements matched, separable footprint CT reconstruction operators for projection and backprojection in two geometries: planar, cone-beam CT (CBCT) and 3rd generation, cylindrical multi-detector row CT (MDCT). Analytical reconstruction is performed using filtered backprojection (FBP) for circular CBCT, weighted FBP (WFBP) for helical CBCT, and cone-parallel projection rebinning followed by WFBP for MDCT. Arbitrary combinations of energy and temporal channels are iteratively reconstructed under a generalized multi-channel signal model for joint reconstruction. We solve this generalized model algebraically using the split Bregman optimization method and the BiCGSTAB(l) linear solver interchangeably for both CBCT and MDCT data. Rank-sparse kernel regression (RSKR) and patch-based singular value thresholding (pSVT) are used to regularize the energy and time dimensions, respectively. Under a Gaussian noise model, regularization parameters are estimated automatically from the input data, dramatically reducing algorithm complexity for end users. Multi-GPU parallelization of the reconstruction operators is supported to manage reconstruction times.

RESULTS

Denoising with RSKR and pSVT and post-reconstruction material decomposition are illustrated with preclinical and clinical cardiac photon-counting (PC)CT data. A digital MOBY mouse phantom with cardiac motion is used to illustrate single energy (SE), multi-energy (ME), time resolved (TR), and combined multi-energy and time-resolved (METR) helical, CBCT reconstruction. A fixed set of projection data is used across all reconstruction cases to demonstrate the Toolkit's robustness to increasing data dimensionality. Identical reconstruction code is applied to in vivo cardiac PCCT data acquired in a mouse model of atherosclerosis (METR). Clinical cardiac CT reconstruction is illustrated using the XCAT phantom and the DukeSim CT simulator, while dual-source, dual-energy CT reconstruction is illustrated for data acquired with a Siemens Flash scanner. Benchmarking results with NVIDIA RTX 8000 GPU hardware demonstrate 61%-99% efficiency in scaling computation from one to four GPUs for these reconstruction problems.

CONCLUSIONS

The MCR Toolkit provides a robust solution for temporal and spectral x-ray CT reconstruction problems and was built from the ground up to facilitate translation of CT research and development between preclinical and clinical applications.

[New contrast agents for photon-counting computed tomography]

BACKGROUND

The introduction of energy-selective photon-counting detectors into clinical practice represents the next milestone in computed tomography (CT). In addition to significantly higher resolution, these detectors allow the implicit acquisition of dual or multispectral data in a single measurement through the use of typically freely selectable thresholds. This capability reignited the interest in new contrast agents based on heavy elements, so-called high‑z elements, for clinical CT.

OBJECTIVE

The present article aims to investigate the potential suitability of different chemical elements as contrast agents and to discuss possible clinical applications, for example, K‑edge imaging or simultaneous application of different contrast agents.

CONCLUSION

First preclinical experiments as well as experiments in large animals could demonstrate potential advantages of contrast agents based on heavy elements. For example, such contrast agents promise a significant increase in image contrast compared to conventional iodine-based agents.

Micro-CT imaging of multiple K-edge elements using GaAs and CdTe photon counting detectors

Objective.To evaluate the performance of two photon-counting (PC) detectors based on different detector materials, gallium arsenide (GaAs) and cadmium telluride (CdTe), for PC micro-CT imaging of phantoms with multiple contrast materials. Another objective is to determine if combining these two detectors in the same micro-CT system can offer higher spectral performance and significant artifact reduction compared to a single detector system.Approach. We have constructed a dual-detector, micro-CT system equipped with two PCDs based on different detector materials: gallium arsenide (GaAs) and cadmium telluride (CdTe). We demonstrate the performance of these detectors for PC micro-CT imaging of phantoms with up to 5 contrast materials with K-edges spread across the x-ray spectrum ranging from iodine with a K-edge at 33.2 keV to bismuth with a K-edge at 90.5 keV. We also demonstrate the use of our system to image a mouse prepared with both iodine and bismuth contrast agents to target different biological systems.Main results.When using the same dose and scan parameters, GaAs shows increased low energy (<50 keV) spectral sensitivity and specificity compared to CdTe. However, GaAs performance at high energies suffers from spectral artifacts and has comparatively low photon counts indicating wasted radiation dose. We demonstrate that combining a GaAs-based and a CdTe-based PC detector in the same micro-CT system offers higher spectral performance and significant artifact reduction compared to a single detector system.Significance.More accurate PC micro-CT using a GaAs PCD alone or in combination with a CdTe PCD could serve for developing new contrast agents such as nanoparticles that show promise in the developing field of theranostics (therapy and diagnostics).

Approaches to Improve EPR-Based Drug Delivery for Cancer Therapy and Diagnosis

The innovative development of nanomedicine has promised effective treatment options compared to the standard therapeutics for cancer therapy. However, the efficiency of EPR-targeted nanodrugs is not always pleasing as it is strongly prejudiced by the heterogeneity of the enhanced permeability and retention effect (EPR). Targeting the dynamics of the EPR effect and improvement of the therapeutic effects of nanotherapeutics by using EPR enhancers is a vital approach to developing cancer therapy. Inadequate data on the efficacy of EPR in humans hampers the clinical translation of cancer drugs. Molecular targeting, physical amendment, or physiological renovation of the tumor microenvironment (TME) are crucial approaches for improving the EPR effect. Advanced imaging technologies for the visualization of EPR-induced nanomedicine distribution in tumors, and the use of better animal models, are necessary to enhance the EPR effect. This review discusses strategies to enhance EPR effect-based drug delivery approaches for cancer therapy and imaging technologies for the diagnosis of EPR effects. The effort of studying the EPR effect is beneficial, as some of the advanced nanomedicine-based EPR-enhancing approaches are currently undergoing clinical trials, which may be helpful to improve EPR-induced drug delivery and translation to clinics.

Imaging in translational cancer research

This review is aimed at presenting some of the recent developments in translational cancer imaging research, with a focus on novel, recently established, or soon to be established cross-sectional imaging techniques for computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET) imaging, including computational investigations based on machine-learning techniques.

Multi-material spectral photon-counting micro-CT with minimum residual decomposition and self-supervised deep denoising

Spectral micro-CT imaging with direct-detection energy discriminating photon counting detectors having small pixel size (< 100×100 µm²) is mainly hampered by: i) the limited energy resolution of the imaging device due to charge sharing effects and ii) the unavoidable noise amplification in the images resulting from basis material decomposition. In this work, we present a cone-beam micro-CT setup that includes a CdTe photon counting detector implementing a charge summing hardware solution to correct for the charge-sharing issue and an innovative image processing pipeline based on accurate modeling of the spectral response of the imaging system, an improved basis material decomposition (BMD) algorithm named minimum-residual BMD (MR-BMD), and self-supervised deep convolutional denoising. Experimental tomographic projections having a pixel size of 45×45 µm² of a plastinated mouse sample including I, Ba, and Gd small cuvettes were acquired. Results demonstrate the capability of the combined hardware and software tools to sharply discriminate even between materials having their K-Edge separated by a few keV, such as e.g., I and Ba. By evaluating the quality of the reconstructed decomposed images (water, bone, I, Ba, and Gd), the quantitative performances of the spectral system are here assessed and discussed.

Ultrahigh resolution whole body photon counting computed tomography as a novel versatile tool for translational research from mouse to man

X-ray computed tomography (CT) is a cardinal tool in clinical practice. It provides cross-sectional images within seconds. The recent introduction of clinical photon-counting CT allowed for an increase in spatial resolution by more than a factor of two resulting in a pixel size in the center of rotation of about 150 µm. This level of spatial resolution is in the order of dedicated preclinical micro-CT systems. However so far, the need for different dedicated clinical and preclinical systems often hinders the rapid translation of early research results to applications in men. This drawback might be overcome by ultra-high resolution (UHR) clinical photon-counting CT unifying preclinical and clinical research capabilities in a single machine. Herein, the prototype of a clinical UHR PCD CT (SOMATOM CounT, Siemens Healthineers, Forchheim, Germany) was used. The system comprises a conventional energy-integrating detector (EID) and a novel photon-counting detector (PCD). While the EID provides a pixel size of 0.6 mm in the centre of rotation, the PCD provides a pixel size of 0.25 mm. Additionally, it provides a quantification of photon energies by sorting them into up to four distinct energy bins. This acquisition of multi-energy data allows for a multitude of applications, e.g. pseudo-monochromatic imaging. In particular, we examine the relation between spatial resolution, image noise and administered radiation dose for a multitude of use-cases. These cases include ultra-high resolution and multi-energy acquisitions of mice administered with a prototype bismuth-based contrast agent (nanoPET Pharma, Berlin, Germany) as well as larger animals and actual patients. The clinical EID provides a spatial resolution of about 9 lp/cm (modulation transfer function at 10%, MTF_10%) while UHR allows for the acquisition of images with up to 16 lp/cm allowing for the visualization of all relevant anatomical structures in preclinical and clinical specimen. The spectral capabilities of the system enable a variety of applications previously not available in preclinical research such as pseudo-monochromatic images. Clinical ultra-high resolution photon-counting CT has the potential to unify preclinical and clinical research on a single system enabling versatile imaging of specimens and individuals ranging from mice to man.

Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden

The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on lymphocyte burden. High mutational load transplant soft tissue sarcomas were initiated in Rag2^+/− and Rag2^−/− mice to model varying lymphocyte burden. Mice received radiation therapy (20 Gy) to the tumor-bearing hind limb and were injected with a liposomal iodinated contrast agent. Five days later, animals underwent conventional micro-CT imaging using an energy integrating detector (EID) and spectral micro-CT imaging using a photon-counting detector (PCD). Tumor volumes and iodine uptakes were measured. The radiomic features (RF) were grouped into feature-spaces corresponding to EID, PCD, and spectral decomposition images. The RFs were ranked to reduce redundancy and increase relevance based on TL burden. A stratified repeated cross validation strategy was used to assess separation using a logistic regression classifier. Tumor iodine concentration was the only significantly different conventional tumor metric between Rag2^+/− (TLs present) and Rag2^−/− (TL-deficient) tumors. The RFs further enabled differentiation between Rag2^+/− and Rag2^−/− tumors. The PCD-derived RFs provided the highest accuracy (0.68) followed by decomposition-derived RFs (0.60) and the EID-derived RFs (0.58). Such non-invasive approaches could aid in tumor stratification for cancer therapy studies.

Intrinsically radiopaque biomaterial assortments: a short review on the physical principles, X-ray imageability, and state-of-the-art developments

X-ray attenuation ability, otherwise known as radiopacity of a material, could be indisputably tagged as the central and decisive parameter that produces contrast in an X-ray image. Radiopaque biomaterials are vital in the healthcare sector that helps clinicians to track them unambiguously during pre and post interventional radiological procedures. Medical imaging is one of the most powerful resources in the diagnostic sector that aids improved treatment outcomes for patients. Intrinsically radiopaque biomaterials enable themselves for visual targeting/positioning as well as to monitor their fate and further provide the radiologists with critical insights about the surgical site. Moreover, the emergence of advanced real-time imaging modalities is a boon to the contemporary healthcare systems that allow to perform minimally invasive surgical procedures and thereby reduce the healthcare costs and minimize patient trauma. X-ray based imaging is one such technologically upgraded diagnostic tool with many variants like digital X-ray, computed tomography, digital subtraction angiography, and fluoroscopy. In light of these facts, this review is aimed to briefly consolidate the physical principles of X-ray attenuation by a radiopaque material, measurement of radiopacity, classification of radiopaque biomaterials, and their recent advanced applications.

An experimental method to correct low-frequency concentric artifacts in photon counting CT

Large-area photon counting detectors (PCDs) are usually built by tiling multiple semiconductor panels that often have slightly different spectral responses to input x-rays. As a result of this spectral inconsistency, experimental PCD-CT images of large, human-sized objects may show high-frequency ring artifacts and low-frequency band artifacts. Due to the much larger width of the bands compared with the rings, the concentric artifact problem in PCD-CT images of human-sized objects cannot be adequately addressed by conventional CT ring correction methods. This work presents an experimental method to correct the concentric artifacts in PCD-CT. The method is applicable to not only energy-discriminating PCDs with multiple bins but also PCDs with only a single threshold controller. Its principle is similar to the two-step beam hardening correction method, except that the proposed method uses pixel-specific polynomial functions to address the spectral inconsistency problem across the detector plane. The pixel-specific polynomial coefficients were experimentally calibrated using 15 acrylic sheets and 6 aluminum sheets of known thicknesses. The pixel-specific polynomial functions were used to convert the measured PCD-CT projection data to acrylic- and aluminum-equivalent thicknesses that are energy-independent. The proposed method was experimentally evaluated using a human cadaver head and multiple physical phantoms: two of them contain iodine and one phantom contains dual K-edge contrast materials (gadolinium and iodine). The results show that the proposed method can effectively remove the low-frequency concentric artifacts in PCD-CT images while reducing beam hardening artifacts. In contrast, the conventional CT ring correction algorithm did not adequately address the low-frequency band artifacts. Compared with the direct material decomposition-based correction method, the proposed method not only relaxes the requirement of multi-energy bins but also generates images with lower noise and fewer concentric artifacts.

PEG-modified gadolinium nanoparticles as contrast agents for in vivo micro-CT

Vascular research is largely performed in rodents with the goal of developing treatments for human disease. Micro-computed tomography (micro-CT) provides non-destructive three-dimensional imaging that can be used to study the vasculature of rodents. However, to distinguish vasculature from other soft tissues, long-circulating contrast agents are required. In this study, we demonstrated that poly(ethylene glycol) (PEG)-coated gadolinium nanoparticles can be used as a vascular contrast agent in micro-CT. The coated particles could be lyophilized and then redispersed in an aqueous solution to achieve 100 mg/mL of gadolinium. After an intravenous injection of the contrast agent into mice, micro-CT scans showed blood pool contrast enhancements of at least 200 HU for 30 min. Imaging and quantitative analysis of gadolinium in tissues showed the presence of contrast agent in clearance organs including the liver and spleen and very low amounts in other organs. In vitro cell culture experiments, subcutaneous injections, and analysis of mouse body weight suggested that the agents exhibited low toxicity. Histological analysis of tissues 5 days after injection of the contrast agent showed cytotoxicity in the spleen, but no abnormalities were observed in the liver, lungs, kidneys, and bladder.

Advances in micro-CT imaging of small animals

PURPOSE

Micron-scale computed tomography (micro-CT) imaging is a ubiquitous, cost-effective, and non-invasive three-dimensional imaging modality. We review recent developments and applications of micro-CT for preclinical research.

METHODS

Based on a comprehensive review of recent micro-CT literature, we summarize features of state-of-the-art hardware and ongoing challenges and promising research directions in the field.

RESULTS

Representative features of commercially available micro-CT scanners and some new applications for both in vivo and ex vivo imaging are described. New advancements include spectral scanning using dual-energy micro-CT based on energy-integrating detectors or a new generation of photon-counting x-ray detectors (PCDs). Beyond two-material discrimination, PCDs enable quantitative differentiation of intrinsic tissues from one or more extrinsic contrast agents. When these extrinsic contrast agents are incorporated into a nanoparticle platform (e.g. liposomes), novel micro-CT imaging applications are possible such as combined therapy and diagnostic imaging in the field of cancer theranostics. Another major area of research in micro-CT is in x-ray phase contrast (XPC) imaging. XPC imaging opens CT to many new imaging applications because phase changes are more sensitive to density variations in soft tissues than standard absorption imaging. We further review the impact of deep learning on micro-CT. We feature several recent works which have successfully applied deep learning to micro-CT data, and we outline several challenges specific to micro-CT.

CONCLUSIONS

All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.

Toward molecular imaging using spectral photon-counting computed tomography?

Molecular imaging is a valuable tool in drug discovery and development, early screening and diagnosis of diseases, and therapy assessment among others. Although many different imaging modalities are in use today, molecular imaging with computed tomography (CT) is still challenging owing to its low sensitivity and soft tissue contrast compared with other modalities. Recent technical advances, particularly the introduction of spectral photon-counting detectors, might allow overcoming these challenges. Herein, the fundamentals and recent advances in CT relevant to molecular imaging are reviewed and potential future preclinical and clinical applications are highlighted. The review concludes with a discussion of potential future advancements of CT for molecular imaging.

Preclinical Applications of Multi-Platform Imaging in Animal Models of Cancer

In animal models of cancer, oncologic imaging has evolved from a simple assessment of tumor location and size to sophisticated multimodality exploration of molecular, physiologic, genetic, immunologic, and biochemical events at microscopic to macroscopic levels, performed noninvasively and sometimes in real time. Here, we briefly review animal imaging technology and molecular imaging probes together with selected applications from recent literature. Fast and sensitive optical imaging is primarily used to track luciferase-expressing tumor cells, image molecular targets with fluorescence probes, and to report on metabolic and physiologic phenotypes using smart switchable luminescent probes. MicroPET/single-photon emission CT have proven to be two of the most translational modalities for molecular and metabolic imaging of cancers: immuno-PET is a promising and rapidly evolving area of imaging research. Sophisticated MRI techniques provide high-resolution images of small metastases, tumor inflammation, perfusion, oxygenation, and acidity. Disseminated tumors to the bone and lung are easily detected by microCT, while ultrasound provides real-time visualization of tumor vasculature and perfusion. Recently available photoacoustic imaging provides real-time evaluation of vascular patency, oxygenation, and nanoparticle distributions. New hybrid instruments, such as PET-MRI, promise more convenient combination of the capabilities of each modality, enabling enhanced research efficacy and throughput.

Medical imaging of tissue engineering and regenerative medicine constructs

Advancement of tissue engineering and regenerative medicine (TERM) strategies to replicate tissue structure and function has led to the need for noninvasive assessment of key outcome measures of a construct's state, biocompatibility, and function. Histology based approaches are traditionally used in pre-clinical animal experiments, but are not always feasible or practical if a TERM construct is going to be tested for human use. In order to transition these therapies from benchtop to bedside, rigorously validated imaging techniques must be utilized that are sensitive to key outcome measures that fulfill the FDA standards for TERM construct evaluation. This review discusses key outcome measures for TERM constructs and various clinical- and research-based imaging techniques that can be used to assess them. Potential applications and limitations of these techniques are discussed, as well as resources for the processing, analysis, and interpretation of biomedical images.

Dual source hybrid spectral micro-CT using an energy-integrating and a photon-counting detector

Preclinical micro-CT provides a hotbed in which to develop new imaging technologies, including spectral CT using photon counting detector (PCD) technology. Spectral imaging using PCDs promises to expand x-ray CT as a functional imaging modality, capable of molecular imaging, while maintaining CT's role as a powerful anatomical imaging modality. However, the utility of PCDs suffers due to distorted spectral measurements, affecting the accuracy of material decomposition. We attempt to improve material decomposition accuracy using our novel hybrid dual-source micro-CT system which combines a PCD and an energy integrating detector. Comparisons are made between PCD-only and hybrid CT results, both reconstructed with our iterative, multi-channel algorithm based on the split Bregman method and regularized with rank-sparse kernel regression. Multi-material decomposition is performed post-reconstruction for separation of iodine (I), gold (Au), gadolinium (Gd), and calcium (Ca). System performance is evaluated first in simulations, then in micro-CT phantoms, and finally in an in vivo experiment with a genetically modified p53fl/fl mouse cancer model with Au, Gd, and I nanoparticle (NP)-based contrasts agents. Our results show that the PCD-only and hybrid CT reconstructions offered very similar spatial resolution at 10% MTF (PCD: 3.50 lp mm-1; hybrid: 3.47 lp mm-1) and noise characteristics given by the noise power spectrum. For material decomposition we note successful separation of the four basis materials. We found that hybrid reconstruction reduces RMSE by an average of 37% across all material maps when compared to PCD-only of similar dose but does not provide much difference in terms of concentration accuracy. The in vivo results show separation of targeted Au and accumulated Gd NPs in the tumor from intravascular iodine NPs and bone. Hybrid spectral micro-CT can benefit nanotechnology and cancer research by providing quantitative imaging to test and optimize various NPs for diagnostic and therapeutic applications.

Application of Nanomaterials in Biomedical Imaging and Cancer Therapy

Nanomaterials, such as nanoparticles, nanorods, nanosphere, nanoshells, and nanostars, are very commonly used in biomedical imaging and cancer therapy. They make excellent drug carriers, imaging contrast agents, photothermal agents, photoacoustic agents, and radiation dose enhancers, among other applications. Recent advances in nanotechnology have led to the use of nanomaterials in many areas of functional imaging, cancer therapy, and synergistic combinational platforms. This review will systematically explore various applications of nanomaterials in biomedical imaging and cancer therapy. The medical imaging modalities include magnetic resonance imaging, computed tomography, positron emission tomography, single photon emission computerized tomography, optical imaging, ultrasound, and photoacoustic imaging. Various cancer therapeutic methods will also be included, including photothermal therapy, photodynamic therapy, chemotherapy, and immunotherapy. This review also covers theranostics, which use the same agent in diagnosis and therapy. This includes recent advances in multimodality imaging, image-guided therapy, and combination therapy. We found that the continuous advances of synthesis and design of novel nanomaterials will enhance the future development of medical imaging and cancer therapy. However, more resources should be available to examine side effects and cell toxicity when using nanomaterials in humans.

Detection of response to tumor microenvironment-targeted cellular immunotherapy using nano-radiomics

Immunotherapies, including cell-based therapies, targeting the tumor microenvironment (TME) result in variable and delayed responses. Thus, it has been difficult to gauge the efficacy of TME-directed therapies early after administration. We investigated a nano-radiomics approach (quantitative analysis of nanoparticle contrast-enhanced three-dimensional images) for detection of tumor response to cellular immunotherapy directed against myeloid-derived suppressor cells (MDSCs), a key component of TME. Animals bearing human MDSC-containing solid tumor xenografts received treatment with MDSC-targeting human natural killer (NK) cells and underwent nanoparticle contrast-enhanced computed tomography (CT) imaging. Whereas conventional CT-derived tumor metrics were unable to differentiate NK cell immunotherapy tumors from untreated tumors, nano-radiomics revealed texture-based features capable of differentiating treatment groups. Our study shows that TME-directed cellular immunotherapy causes subtle changes not effectively gauged by conventional imaging metrics but revealed by nano-radiomics. Our work provides a method for noninvasive assessment of TME-directed immunotherapy potentially applicable to numerous solid tumors.

Metal-derived nanoparticles in tumor theranostics: Potential and limitations

Initially, metal derived nanoparticles have been used exclusively as contrasting agents in magnetic resonance imaging. Today, green routes of chemical synthesis together with numerous modifications of the core and surface gave rise to a plethora of biomedical applications of metal derived nanoparticles including tumor imaging, diagnostics, and therapy. These materials are an emerging class of tools for tumor theranostics. Nevertheless, the spectrum of clinically approved metal nanoparticles remains narrow, as the safety, specificity and efficiency still have to be improved. In this review we summarize the major directions for development and biomedical applications of metal based nanoparticles and analyze their effects on tumor cells and microenvironment. We discuss the advantages and possible limitations of metal nanoparticle-based tumor theranostics, as well as the potential strategies to improve the in vivo performance of these unique materials.

CT/MR Dual-Modality Imaging Tracking of Mesenchymal Stem Cells Labeled with a Au/GdNC@SiO2 Nanotracer in Pulmonary Fibrosis

Mesenchymal stem cells (MSCs) have shown potential as an innovative treatment for pulmonary fibrosis (PF), due to their capability to ameliorate the inflammation and moderate the deterioration of PF. The fate of the stem cells transplanted into the lung, including survival, migration, homing, and functions, however, has not been fully understood yet. In this paper, we report the development of a computed tomography/magnetic resonance (CT/MR) dual-modal nanotracer, gold/gadolinium nanoclusters overcoated with a silica shell (Au/GdNC@SiO₂), for noninvasive labeling and tracking of the transplanted human MSCs (hMSCs) in a PF model. The Au/GdNC@SiO₂ nanotracer exhibits good colloidal and chemical stability, high biocompatibility, enhanced longitudinal MR relaxivity, and superior X-ray attenuation property. The hMSCs can be effectively labeled with Au/GdNC@SiO₂, resulting in a significantly increased cellular CT/MR imaging contrast, without any obvious adverse effect on the function, including proliferation and differentiation of the labeled stem cells. Moreover, by using the Au/GdNC@SiO₂ nanotracer, the hMSCs transplanted in the lung can be tracked for 7 d via in vivo CT/MR dual-modality imaging. This work may provide an insight into the role the transplanted hMSCs play in PF therapy, thus promoting the stem cell-based regenerative medicine.

Investigation of imaging properties of novel contrast agents based on gold, silver and bismuth nanoparticles in spectral computed tomography using Monte Carlo simulation

In the present paper, some imaging properties of nanoparticles-based contrast agents including gold, bismuth, and silver were assessed and compared with conventional (iodinated) contrast agent in spectral computed tomography (CT). A spectral CT scanner with photon-counting detectors (PCD) and 6 energy bins was simulated using the Monte Carlo (MC) simulation method. The nanoparticles were designed with a diameter of 50 nm at concentrations of 2, 4, and 8 mg/ml. Water-filled cylindrical phantom was modeled with a diameter of 10 cm containing a hole with a diameter of 5 cm in its center, where was filled with contrast agents. The MC results were used to reconstruct images. Image reconstruction was accomplished with the filtered back-projection (FBP) method with hamming filter and linear interpolation method. CT number and contrast-to-noise ratio (CNR) of all studied contrast materials were calculated in spectral images. The simulations indicated that nanoparticle-based contrast agents have a higher CT number and CNR than the iodinated contrast agent at the same concentration and for all energy bins. In general, gold nanoparticles produced the highest CT number and CNR compared to silver and bismuth nanoparticles at the same concentration. However, at low energies (below 80 keV), silver nanoparticles performed similarly to gold nanoparticles and at high energies (120 keV), bismuth nanoparticles can be a good substitute for gold nanoparticles.

Spatial heterogeneity of nanomedicine investigated by multiscale imaging of the drug, the nanoparticle and the tumour environment

Genetic and phenotypic tumour heterogeneity is an important cause of therapy resistance. Moreover, non-uniform spatial drug distribution in cancer treatment may cause pseudo-resistance, meaning that a treatment is ineffective because the drug does not reach its target at sufficient concentrations. Together with tumour heterogeneity, non-uniform drug distribution causes “therapy heterogeneity”: a spatially heterogeneous treatment effect. Spatial heterogeneity in drug distribution occurs on all scales ranging from interpatient differences to intratumour differences on tissue or cellular scale. Nanomedicine aims to improve the balance between efficacy and safety of drugs by targeting drug-loaded nanoparticles specifically to tumours. Spatial heterogeneity in nanoparticle and payload distribution could be an important factor that limits their efficacy in patients. Therefore, imaging spatial nanoparticle distribution and imaging the tumour environment giving rise to this distribution could help understand (lack of) clinical success of nanomedicine. Imaging the nanoparticle, drug and tumour environment can lead to improvements of new nanotherapies, increase understanding of underlying mechanisms of heterogeneous distribution, facilitate patient selection for nanotherapies and help assess the effect of treatments that aim to reduce heterogeneity in nanoparticle distribution.

In this review, we discuss three groups of imaging modalities applied in nanomedicine research: non-invasive clinical imaging methods (nuclear imaging, MRI, CT, ultrasound), optical imaging and mass spectrometry imaging. Because each imaging modality provides information at a different scale and has its own strengths and weaknesses, choosing wisely and combining modalities will lead to a wealth of information that will help bring nanomedicine forward.

Photon-counting cine-cardiac CT in the mouse

The maturation of photon-counting detector (PCD) technology promises to enhance routine CT imaging applications with high-fidelity spectral information. In this paper, we demonstrate the power of this synergy and our complementary reconstruction techniques, performing 4D, cardiac PCD-CT data acquisition and reconstruction in a mouse model of atherosclerosis, including calcified plaque. Specifically, in vivo cardiac micro-CT scans were performed in four ApoE knockout mice, following their development of calcified plaques. The scans were performed with a prototype PCD (DECTRIS, Ltd.) with 4 energy thresholds. Projections were sampled every 10 ms with a 10 ms exposure, allowing the reconstruction of 10 cardiac phases at each of 4 energies (40 total 3D volumes per mouse scan). Reconstruction was performed iteratively using the split Bregman method with constraints on spectral rank and spatio-temporal gradient sparsity. The reconstructed images represent the first in vivo, 4D PCD-CT data in a mouse model of atherosclerosis. Robust regularization during iterative reconstruction yields high-fidelity results: an 8-fold reduction in noise standard deviation for the highest energy threshold (relative to unregularized algebraic reconstruction), while absolute spectral bias measurements remain below 13 Hounsfield units across all energy thresholds and scans. Qualitatively, image domain material decomposition results show clear separation of iodinated contrast and soft tissue from calcified plaque in the in vivo data. Quantitatively, spatial, spectral, and temporal fidelity are verified through a water phantom scan and a realistic MOBY phantom simulation experiment: spatial resolution is robustly preserved by iterative reconstruction (10% MTF: 2.8–3.0 lp/mm), left-ventricle, cardiac functional metrics can be measured from iodine map segmentations with ~1% error, and small calcifications (615 μm) can be detected during slow moving phases of the cardiac cycle. Given these preliminary results, we believe that PCD technology will enhance dynamic CT imaging applications with high-fidelity spectral and material information.

Hybrid Nano-GdF3 contrast media allows pre-clinical in vivo element-specific K-edge imaging and quantification

Computed tomography (CT) is a widely used imaging modality. Among the recent technical improvements to increase the range of detection for optimized diagnostic, new devices such as dual energy CT allow elemental discrimination but still remain limited to two energies. Spectral photon-counting CT (SPCCT) is an emerging X-ray imaging technology with a completely new multiple energy detection and high spatial resolution (200 μm). This unique technique allows detection and quantification of a given element thanks to an element-specific increase in X-ray absorption for an energy (K-band) depending on its atomic number. The main contrast media used hitherto are iodine-based compounds but the K-edge of iodine (33.2 keV) is out of the range of detection. Therefore, it is crucial to develop contrast media suitable for this advanced technology. Gadolinium, well known and used element for MRI, possess a K-edge (50.2 keV) well suited for the SPCCT modality. The use of nano-objects instead of molecular entities is pushed by the necessity of high local concentration. In this work, nano-GdF3 is validated on a clinical based prototype, to be used as efficient in vivo contrast media. Beside an extremely high stability, it presents long lasting time in the blood pool allowing perfusion imaging of small animals, without apparent toxicity.