Contrast Agent Diffusion Research Articles

Sparse-View Spectral CT Reconstruction and Material Decomposition Based on Multi-Channel SGM.

In medical applications, the diffusion of contrast agents in tissue can reflect the physiological function of organisms, so it is valuable to quantify the distribution and content of contrast agents in the body over a period. Spectral CT has the advantages of multi-energy projection acquisition and material decomposition, which can quantify K-edge contrast agents. However, multiple repetitive spectral CT scans can cause excessive radiation doses. Sparse-view scanning is commonly used to reduce dose and scan time, but its reconstructed images are usually accompanied by streaking artifacts, which leads to inaccurate quantification of the contrast agents. To solve this problem, an unsupervised sparse-view spectral CT reconstruction and material decomposition algorithm based on the multi-channel score-based generative model (SGM) is proposed in this paper. First, multi-energy images and tissue images are used as multi-channel input data for SGM training. Secondly, the organism is multiply scanned in sparse views, and the trained SGM is utilized to generate multi-energy images and tissue images driven by sparse-view projections. After that, a material decomposition algorithm using tissue images generated by SGM as prior images for solving contrast agent images is established. Finally, the distribution and content of the contrast agents are obtained. The comparison and evaluation of this method are given in this paper, and a series of mouse scanning experiments are carried out to verify the effectiveness of the method.

Exploring the potential of Physics-Informed Neural Networks to extract vascularization data from DCE-MRI in the presence of diffusion

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used to assess tissue vascularization, particularly in oncological applications. However, the most widely used pharmacokinetic (PK) models do not account for contrast agent (CA) diffusion between neighboring voxels, which can limit the accuracy of the results, especially in cases of heterogeneous tumors. To address this issue, previous works have proposed algorithms that incorporate diffusion phenomena into the formulation. However, these algorithms often face convergence problems due to the ill-posed nature of the problem.In this work, we present a new approach to fitting DCE-MRI data that incorporates CA diffusion by using Physics-Informed Neural Networks (PINNs). PINNs can be trained to fit measured data obtained from DCE-MRI while ensuring the mass conservation equation from the PK model. We compare the performance of PINNs to previous algorithms on different 1D cases inspired by previous works from literature. Results show that PINNs retrieve vascularization parameters more accurately from diffusion-corrected tracer-kinetic models. Furthermore, we demonstrate the robustness of PINNs compared to other traditional algorithms when faced with noisy or incomplete data. Overall, our results suggest that PINNs can be a valuable tool for improving the accuracy of DCE-MRI data analysis, particularly in cases where CA diffusion plays a significant role.

Triple contrast computed tomography reveals site-specific biomechanical differences in the human knee joint-A proof of concept study.

Cartilage and synovial fluid are challenging to observe separately in native computed tomography (CT). We report the use of triple contrast agent (bismuth nanoparticles [BiNPs], CA4+, and gadoteridol) to image and segment cartilage in cadaveric knee joints with a clinical CT scanner. We hypothesize that BiNPs will remain in synovial fluid while the CA4+ and gadoteridol will diffuse into cartilage, allowing (1) segmentation of cartilage, and (2) evaluation of cartilage biomechanical properties based on contrast agent concentrations. To investigate these hypotheses, triple contrast agent was injected into both knee joints of a cadaver (N = 1), imaged with a clinical CT at multiple timepoints during the contrast agent diffusion. Knee joints were extracted, imaged with micro-CT (µCT), and biomechanical properties of the cartilage surface were determined by stress-relaxation mapping. Cartilage was segmented and contrast agent concentrations (CA4+ and gadoteridol) were compared with the biomechanical properties at multiple locations (n = 185). Spearman's correlation between cartilage thickness from clinical CT and reference µCT images verifies successful and reliable segmentation. CA4+ concentration is significantly higher in femoral than in tibial cartilage at 60 min and further timepoints, which corresponds to the higher Young's modulus observed in femoral cartilage. In this pilot study, we show that (1) large BiNPs do not diffuse into cartilage, facilitating straightforward segmentation of human knee joint cartilage in a clinical setting, and (2) CA4+ concentration in cartilage reflects the biomechanical differences between femoral and tibial cartilage. Thus, the triple contrast agent CT shows potential in cartilage morphology and condition estimation in clinical CT.

Contrast-enhanced ultrasonography of the placental barrier; the protective umbrella of the fetus during pregnancy.

Ultrasonography is the preferred technique to evaluate the status of maternal and fetal health during pregnancy. Non-obstetric acute or chronic conditions occurring during pregnancy must be diagnosed as early as possible to permit timely and necessary treatment for the sake of maternal and fetal health. The purpose of this study was to evaluate the safety and value of contrast-enhanced ultrasonography (CEUS) during pregnancy. This prospective study included 14 pregnant women requiring pregnancy termination and six healthy pregnant women. The 14 pregnant women requiring pregnancy termination underwent CEUS prior to surgery to investigate the pattern of contrast agent diffusion. The six healthy pregnant women did not undergo CEUS. The structure of placentae with and without contrast agent injection were also compared by light microscopy. CEUS analysis failed to identify any signs of contrast agents in the umbilical cord blood and fetus. There were no obvious changes in the morphology of placentae with and without contrast agent injection under light microscope. CEUS identified the need for early treatment in one pregnant woman with an ovarian tumor. Due to the protective effect of the placental barrier on the fetus, CEUS during pregnancy may represent a safe form of imaging technology that can provide valuable information for the diagnosis of non-obstetric acute or chronic disorders and to guide thefuture treatment of pregnant women.

A model established for predicting natural pregnancy possibility based on the imaging characteristics of 4-dimensional hysterosalpingo-contrast sonography.

The incidence of infertility is increasing, more than 30% of them having related abnormal tubal patency. Four-dimensional (4D) hysterosalpingo-contrast sonography (HyCoSy) overcomes the shortcomings of 3D HyCoSy in the diagnosis of tubal patency, showing high specificity and accuracy. In addition, 4D HyCoSy discards iodine allergy and X-ray radiation and possesses easy-operating, contributing to good acceptance in clinical practice. However, there is no research to explore the imaging standards related to the possibility of natural pregnancy after 4D HyCoSy. If a predictive model of postoperative natural pregnancy was established using the analysis of clinical data combined with imaging characteristics of 4D HyCoSy of patients with tubal factor infertility, clinical decision-making can be wisely guided in the future. This study aims to establish a predictive model of natural pregnancy after 4D HyCoSy based on clinical data and imaging characteristics of patients with tubal factor in fertility. A retrospective study was conducted for patients who were diagnosed with tubal factor infertility in Hunan Guangxiu Hospital from February 2017 to May 2018. The patients ought to possess complete 4D HyCoSy imaging data and at least one-side-unobstructed fallopian tube. General clinical data and imaging data were collected. Pregnancy outcome was followed up till 3 months after 4D HyCoSy. According to pregnancy outcome, patients were divided into a pregnancy group and a non-pregnancy group. Binary logistic regression was used to analyze the correlation between various variables and natural pregnancy after 4D HyCoSy. The variables with significant difference (P<0.05) in single-factor logistic regression were included in the natural pregnancy probability prediction model. The classification accuracy was further verified with 10-fold cross-validation. A total of 1 085 patients with clinically suspected tubal factor infertility who met the requirements and followed the doctors' prescription were collected. Clinical characteristics (age and duration of infertility) and 4D HyCoSy imaging characteristics (thickness of endometrium from the 3rd to the 7th day after the end of menstruation, visualization of the left fallopian tube, the diffusion of contrast agent around the left ovary, and the diffusion of contrast agent around the right ovary) were independent predictors for natural pregnancy 3 months after 4D HyCoSy. A natural pregnancy probability prediction model was established with the area under the curve (AUC) verified by the 10-fold cross-validation all greater than 0.75, and the best AUC was 0.868. The Q value obtained by the prediction model was the probability of natural pregnancy, and the cutoff value was 0.5. When the Q value was greater than 0.5, it was recommended to attempt natural pregnancy for 3 months, and when the Q value was less than 0.5, in vitro fertilization was adviced. A predictive model for the evaluating probability of natural pregnancy in women with tubal factor infertility after 4D HyCoSy is successfully established based on the analysis for clinical data and imaging characteristics. This model shows a great potential in assisting clinical decision making.

A Finite Element Based Optimization Algorithm to Include Diffusion into the Analysis of DCE-MRI

DCE-MRI is a widly used technique to obtain a quantitative description of the tumour vasculature. Contrast agent diffusion has a great impact on the extraction of this descriptors. This work presents a new approach based on the FEM to account for this phenomenon. The proposed model outperforms the state-of-art models.

Effects of human articular cartilage constituents on simultaneous diffusion of cationic and nonionic contrast agents.

Contrast‐enhanced computed tomography is an emerging diagnostic technique for osteoarthritis. However, the effects of increased water content, as well as decreased collagen and proteoglycan concentrations due to cartilage degeneration, on the diffusion of cationic and nonionic agents, are not fully understood. We hypothesize that for a cationic agent, these variations increase the diffusion rate while decreasing partition, whereas, for a nonionic agent, these changes increase both the rate of diffusion and partition. Thus, we examine the diffusion of cationic and nonionic contrast agents within degraded tissue in time‐ and depth‐dependent manners. Osteochondral plugs (N = 15, d = 8 mm) were extracted from human cadaver knee joints, immersed in a mixture of cationic CA4+ and nonionic gadoteridol contrast agents, and imaged at multiple time‐points, using the dual‐contrast method. Water content, and collagen and proteoglycan concentrations were determined using lyophilization, infrared spectroscopy, and digital densitometry, respectively. Superficial to mid (0%‐60% depth) cartilage CA4+ partitions correlated with water content (R < −0.521, P < .05), whereas in deeper (40%‐100%) cartilage, CA4+ correlated only with proteoglycans (R > 0.671, P < .01). Gadoteridol partition correlated inversely with collagen concentration (0%‐100%, R < −0.514, P < .05). Cartilage degeneration substantially increased the time for CA4+ compared with healthy tissue (248 ± 171 vs 175 ± 95 minute) to reach the bone‐cartilage interface, whereas for gadoteridol the time (111 ± 63 vs 179 ± 163 minute) decreased. The work clarifies the diffusion mechanisms of two different contrast agents and presents depth and time‐dependent effects resulting from articular cartilage constituents. The results will inform the development of new contrast agents and optimal timing between agent administration and joint imaging.

Study on the Effect of Contrast Agent on Biofilms and Their Visualization in Porous Substrate Using X-ray μCT

Investigation of biofilms and visualization using non-destructive imaging techniques like X-ray μCT has recently gained interest. Biofilms are congregations of microorganisms that attach to surfaces and comprise of microbial cells embedded in extracellular polymeric substances (EPS). They are ubiquitous entities that are commonly found in any non-sterile setting and have direct implications on human health. Methods to visualize them in-situ are highly needed to understand their behaviour (attachment and detachment) inside a substrate. Contrast-enhanced X-ray μCT is a 3D imaging technique that is capable of visualising objects that have very low attenuation contrast. The use of contrast agents in X-ray μCT has been an evolving process, however, the possible toxic effect of these chemical compounds against biofilms has not been studied in detail. In this study, we focus on the toxic effect of contrast agents and study the diffusion and drainage of contrast agents in biofilms. We propose using water-soluble potassium bromide (KBr) as a suitable contrast agent for enhancement of the attenuation coefficient of a monoculture of Pseudomonas fluorescens biofilms inside a porous substrate. At the given concentration, KBr proved to be less bactericidal compared to other commonly used contrast agents and at 5% w/v concentration we were able to clearly distinguish between the biofilm and the porous substrate.

Diffusion of charged and uncharged contrast agents in equine mandibular condylar cartilage is not affected by an increased level of sugar-induced collagen crosslinking

Nutrition of articular cartilage relies mainly on diffusion and convection of solutes through the interstitial fluid due to the lack of blood vessels. The diffusion is controlled by two factors: steric hindrance and electrostatic interactions between the solutes and the matrix components. Aging comes with changes in the cartilage structure and composition, which can influence the diffusion. In this study, we treated fibrocartilage of mandibular condyle with ribose to induce an aging-like effect by accumulating collagen crosslinks. The effect of steric hindrance or electrostatic forces on the diffusion was analyzed using either charged (Hexabrix) or uncharged (Visipaque) contrast agents. Osteochondral plugs from young equine mandibular condyles were treated with 500 mM ribose for 7 days. The effect of crosslinking on mechanical properties was then evaluated via dynamic indentation. Thereafter, the samples were exposed to contrast agents and imaged using contrast-enhanced computed tomography (CECT) at 18 different time points up to 48 h to measure their diffusion. Normalized concentration of contrast agents in the cartilage and contrast agent diffusion flux, as well as the content of crosslink level (pentosidine), water, collagen, and glycosaminoglycan (GAG) were determined. Ribose treatment significantly increased the pentosidine level (from 0.01 to 7.6 mmol/mol collagen), which resulted in an increase in tissue stiffness (~1.5 fold). Interestingly, the normalized concentration and diffusion flux did not change after the induction of an increased level of pentosidine either for Hexabrix or Visipaque. The results of this study strongly suggest that sugar-induced collagen crosslinking in TMJ condylar cartilage does not affect the diffusion properties.

Contrast-enhanced ultrasound tractography for 3D vascular imaging of the prostate

Diffusion tensor tractography (DTT) enables visualization of fiber trajectories in soft tissue using magnetic resonance imaging. DTT exploits the anisotropic nature of water diffusion in fibrous structures to identify diffusion pathways by generating streamlines based on the principal diffusion vector. Anomalies in these pathways can be linked to neural deficits. In a different field, contrast-enhanced ultrasound is used to assess anomalies in blood flow with the aim of locating cancer-induced angiogenesis. Like water diffusion, blood flow and transport of contrast agents also shows a principal direction; however, this is now determined by the local vasculature. Here we show how the tractographic techniques developed for magnetic resonance imaging DTT can be translated to contrast-enhanced ultrasound, by first estimating contrast flow velocity fields from contrast-enhanced ultrasound acquisitions, and then applying tractography. We performed 4D in-vivo contrast-enhanced ultrasound of three human prostates, proving the feasibility of the proposed approach with clinically acquired datasets. By comparing the results to histopathology after prostate resection, we observed qualitative agreement between the contrast flow tracts and typical markers of cancer angiogenic microvasculature: higher densities and tortuous geometries in tumor areas. The method can be used in-vivo using a standard contrast-enhanced ultrasound protocol, opening up new possibilities in the area of vascular characterization for cancer diagnostics.

Design and construction of an innovative brain phantom prototype for MRI.

The purpose of this project was to construct a physical brain phantom for MRI, mimicking structure and T1 relaxation properties of white matter (WM) and gray matter (GM). The phantom design comprised 2 compartments, 1 resembling the WM and 1 resembling the GM. Their T1 relaxation times, as assessed using an inversion recovery turbo spin echo sequence, were reproduced using an agar gel doped with contrast agent (CA) and their folding patterns were simulated through a molding-casting procedure using 3D-printed casts and flexible silicone molds. Three versions of the assembling procedure were adopted to build: Phantom1 without any separation; Phantom2 with a varnish layer; and Phantom3 with a thin wax layer between the compartments. Phantom1 was characterized by an immediate diffusion of CA between the 2 compartments. Phantom2 and Phantom3, instead, showed relaxation times and shape comparable with the target ones identified in a healthy control subject (WM: 754 ± 40 ms; GM: 1277 ± 96 ms). Moreover, both compartments revealed intact gyri and sulci. However, the diffusion of CA made Phantom2 stable only for a short period of time. Phantom3 showed stability within a time window of several days but the wax layer between the WM and GM was visible in the MRI. Structural and intensity properties of the constructed phantoms are useful in evaluating and validating steps from image acquisition to image processing. Moreover, the described constructing procedure and its modular design make it adjustable to a variety of applications.

The effects of intravoxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains.

Quantitative evaluation of dynamic contrast enhanced MRI (DCE-MRI) allows for estimating perfusion, vessel permeability, and tissue volume fractions by fitting signal intensity curves to pharmacokinetic models. These compart mental models assume rapid equilibration of contrast agent within each voxel. However, there is increasing evidence that this assumption is violated for small molecular weight gadolinium chelates. To evaluate the error introduced by this invalid assumption, we simulated DCE-MRI experiments with volume fractions computed from entire histological tumor cross-sections obtained from murine studies. A 2D finite element model of a diffusion-compensated Tofts-Kety model was developed to simulate dynamic T1 signal intensity data. Digitized histology slices were segmented into vascular (vp ), cellular and extravascular extracellular (ve ) volume fractions. Within this domain, Ktrans (the volume transfer constant) was assigned values from 0 to 0.5 min-1 . A representative signal enhancement curve was then calculated for each imaging voxel and the resulting simulated DCE-MRI data analyzed by the extended Tofts-Kety model. Results indicated parameterization errors of -19.1% ± 10.6% in Ktrans , -4.92% ± 3.86% in ve , and 79.5% ± 16.8% in vp for use of Gd-DTPA over 4 tumor domains. These results indicate a need for revising the standard model of DCE-MRI to incorporate a correction for slow diffusion of contrast agent. Magn Reson Med 80:330-340, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Assessment of micronecrotic tumor tissue using dynamic contrast-enhanced magnetic resonance imaging

Compartmental models for evaluation of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) datasets assume a homogeneous interstitital volume distribution and homogeneous contrast agent (CA) distribution within each compartment, neglecting effects of CA diffusion within the compartments. When necrotic or micronecrotic tumor tissue is present, these assumptions may no longer be valid. Therefore, the present study investigates the validity of three compartmental models in assessing tumors with necrotic components.The general diffusion equation for inhomogeneous tissue was used to simulate the extravasation of a low-molecular-weight contrast agent from a feeding vessel into the interstitial space. The simulated concentration-time curves were evaluated using the extended Tofts model, a parallel 3-compartment model, and a sequential 3-compartment model.The extended Tofts model overestimated the interstitial volume fraction by a median of 6.9% resp. 10.0% and the parallel 3-compartment model by 8.6% resp. 15.5%, while the sequential 3-compartment model overestimated it by 0.2% resp. underestimated it by 18.8% when simulating a mean vessel distance of 100μm resp. 150μm. Overall, the sequential 3-compartment model provided more reliable results both for the total fractional interstitial volume and for the interstitial subcompartments.

Abstract A23: A finite element model of perfusion and diffusion within tumors based on dynamic contrast enhanced magnetic resonance imaging

Abstract Quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data provides estimates of physiological parameters that characterize tissue volume fractions, blood flow, and vascular permeability. While it provides a reasonable description of well-vascularized tissue, the standard models do not include the effects of intra-voxel diffusion, which is hypothesized to play a significant role in distributing contrast agent in poorly vascularized voxels1. This mechanism has been studied in silico using simulated tissue regions, but the results have yet to be tested with experimental data. We hypothesize that explicit incorporation of the contrast agent diffusion into the standard models is required to accurately model delivery and retention of the contrast agent within poorly vascularized regions. To investigate the effect of the diffusion of contrast agent within a tumor, nude athymic mice are subcutaneously implanted with BT474 cancer cells, and tumors develop for 4-6 weeks prior to performing DCE-MRI. Following imaging, the tumors are extracted, sectioned, and stained for vascularity (CD31) and viability/cellularity (H&amp;E). Stained central slice sections were digitized in high resolution, segmented in MATLAB (Natick, MA), and a finite element model (FEM) was developed. A population arterial input function (AIF)2 serves as the source of contrast agent delivery into the FEM at the vascular boundaries, while the diffusion equation distributes the contrast agent through the extracellular space. The distribution of contrast agent over time within an imaging voxel is converted from concentration to MR signal intensity, and fit to the extended Tofts model3, providing estimates of vascular permeability and perfusion (Ktrans), extravascular extracellular volume fraction (ve), and vascular volume fraction (vp). These values are then compared to the true tissue volume fractions, determined by tissue segmentation, and an assigned (reasonable) set of Ktrans values. In order to verify the accuracy of the histologically segmented FEM, the simulated DCE-MRI signal is compared to in vivo experimental DCE-MRI scans of the same tumor. Comparison is completed by registering DCE-MRI voxels to simulated voxel domains, and directly measuring the difference between simulated and experimental signal intensities. Using a Ktrans of 0.4 min-1 and a diffusion constant of 3 x 10-5 mm2/s, the extended Tofts model poorly predicts tissue properties, with predicted Ktrans errors ranging from -95% to 68%, ve errors ranging -80% to 13%, and vp errors ranging 32% to 381%. Notably, necrotic regions of the tumor are subject to the highest level of error in the Tofts approximation of these parameters. Each of the maximum absolute prediction errors listed above occur in voxels with a vp value below 0.5% and ve above 80%. In contrast, the Tofts model can more accurately predict vp when the true value is above 5%, and most accurately predicts ve when its true value is below 70%. These results indicate that the lack of a diffusion term in the extended Tofts model can lead to significant errors in the estimates of physiological properties of tumor tissue in vivo. This is especially notable in regions with both a low vp and high ve, where the tissue is necrotic and poorly perfused. These results call for the inclusion of a diffusive term in the analysis of DCE-MRI in order to accurately model and understand contrast agent perfusion in a tumor. By including such a term, the diagnostic and predictive power of Ktrans ¸ vp, and ve, could be further improved. Our ongoing efforts for this research include further refining histology segmentation and model validation by comparing both simulated and measured DCE-MRI signal intensities. 1. Barnes, et al. PLoS One . 2014;9:e108726 2. Loveless, et al. Magn Reson Med . 2012; 67:226-236. 3. Tofts &amp; Kermode. Magn Reson Med . 1991;17:357-367. Citation Format: Ryan T. Woodall, Stephanie L. Eldridge, Anna G. Sorace, Thomas E. Yankeelov. A finite element model of perfusion and diffusion within tumors based on dynamic contrast enhanced magnetic resonance imaging. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A23.

GPU-based computational modeling of magnetic resonance imaging of vascular structures

Magnetic resonance imaging (MRI) is one of the most important diagnostic tools in modern medicine. Since it is a high-cost and highly-complex imaging modality, computational models are frequently built to enhance its understanding as well as to support further development. However, such models often have to be simplified to complete simulations in a reasonable time. Thus, the simulations with high spatial/temporal resolutions, with any motion consideration (like blood flow) and/or with 3D objects usually call for using parallel computing environments. In this paper, we propose to use graphics processing units (GPUs) for fast simulations of MRI of vascular structures. We apply a CUDA environment which supports general purpose computation on GPU (GPGPU). The data decomposition strategy is applied and thus the parts of each virtual object are spread over the GPU cores. The GPU cores are responsible for calculating the influence of blood flow behavior and MRI events after successive time steps. In the proposed approach, different data layouts, memory access patterns, and other memory improvements are applied to efficiently exploit GPU resources. Computational performance is thoroughly validated for various vascular structures and different NVIDIA GPUs. Results show that MRI simulations can be accelerated significantly thanks to GPGPU. The proposed GPU-based approach may be easily adopted in the modeling of other flow related phenomena like perfusion, diffusion or transport of contrast agents.

Solute Transport of Negatively Charged Contrast Agents Across Articular Surface of Injured Cartilage.

Solute transport through the extracellular matrix (ECM) is crucial to chondrocyte metabolism. Cartilage injury affects solute transport in cartilage due to alterations in ECM structure and solute-matrix interactions. Therefore, cartilage injury may be detected by using contrast agent-based clinical imaging. In the present study, effects of mechanical injury on transport of negatively charged contrast agents in cartilage were characterized. Using cartilage plugs injured by mechanical compression protocol, effective partition coefficients and diffusion fluxes of iodine- and gadolinium-based contrast agents were measured using high resolution microCT imaging. For all contrast agents studied, effective diffusion fluxes increased significantly, particularly at early times during the diffusion process (38 and 33% increase after 4min, P<0.05 for iodine and Gd-DTPA; and 76% increase after 10min for diatrizoate, P<0.05). Effective partition coefficients were unaffected in mechanically injured cartilage. Mechanical injury reduced PG content and collagen integrity in cartilage superficial zone. This study suggests that alterations in contrast agent diffusion flux, a non-equilibrium transport parameter, provides a more sensitive indicator for assessment of cartilage matrix integrity than partition coefficient and the equilibrium distribution of solute. These findings may help in developing clinical methods of contrast agent-based imaging to detect cartilage injury.

MR imaging, targeting and characterization of pulmonary fibrosis using intra-tracheal administration of gadolinium-based nanoparticles.

Idiopathic pulmonary fibrosis is a devastating disease. Animal models are critical to develop new diagnostic approaches. We investigate here whether the application of an ultra-short echo time MRI sequence combined with the intra-tracheal administration of Gd-based nanoparticles can help to visualize and characterize pulmonary fibrosis in mice. 21 mice were imaged. Treated mice were administered bleomycin. MRI was used for longitudinal detection of bleomycin-induced lung injury from Day 1 up to Day 60. On Day 30, all mice received nanoparticles and MR images were acquired. A signal enhancement of 120% and 50% in fibrotic lesions and healthy tissues respectively was obtained. A twofold increase of contrast-to-noise ratio between fibrotic and healthy tissue was also observed, leading to a more accurate delineation of the extent of fibrosis. The elimination time constant of the nanoparticles was 54% higher in fibrotic lesions. Bleomycin-induced lung injury can be monitored using MRI. Intra-tracheal administration of Gd-based nanoparticles enabled us to enhance fibrotic tissue in lungs but also to extract imaging biomarkers that quantify elimination and diffusion of contrast agents and can characterize fibrotic tissue. The added value of MRI associated with pulmonary administration of contrast agents is key to better understand the lung fibrotic process and monitor drug response in pre-clinical studies, which will be valuable for translational applications. Copyright © 2016 John Wiley & Sons, Ltd.

Cationic Contrast Agent Diffusion Differs Between Cartilage and Meniscus.

Contrast enhanced computed tomography (CECT) is a non-destructive imaging technique used for the assessment of composition and structure of articular cartilage and meniscus. Due to structural and compositional differences between these tissues, diffusion and distribution of contrast agents may differ in cartilage and meniscus. The aim of this study is to determine the diffusion kinematics of a novel iodine based cationic contrast agent (CA2+) in cartilage and meniscus. Cylindrical cartilage and meniscus samples (d = 6 mm, h ≈ 2 mm) were harvested from healthy bovine knee joints (n = 10), immersed in isotonic cationic contrast agent (20 mgI/mL), and imaged using a micro-CT scanner at 26 time points up to 48 h. Subsequently, normalized X-ray attenuation and contrast agent diffusion flux, as well as water, collagen and proteoglycan (PG) contents in the tissues were determined. The contrast agent distributions within cartilage and meniscus were different. In addition, the normalized attenuation and diffusion flux were higher (p < 0.05) in cartilage. Based on these results, diffusion kinematics vary between cartilage and meniscus. These tissue specific variations can affect the interpretation of CECT images and should be considered when cartilage and meniscus are assessed simultaneously.

Estimation of Systematic and Spatially Correlated Components of Random Signals from Repeated Measurements: Application to Contrast Enhanced Computer Tomography Measurements

Observational errors can be categorized into two groups: random noise, which is altered every time when measurement is repeated, and a systematic temporally invariant error. In this paper, we propose a method to estimate the covariance structure for systematic error and random noise using a small number of repeated measurements of the signal. We model the systematic and random components as stationary Gaussian random fields and use the Bayesian approach to estimate the spatial covariance functions of these components simultaneously. The study is motivated by an application related to the diagnosis of joint diseases using contrast enhanced computer tomography (CT) measurements. The noise in the measured contrast agent concentration profiles within cartilage tissue is strongly spatially correlated and includes a systematic component. Since the estimates are significantly sensitive to all errors in modeling, the systematic error and random noise components should be characterized for reliable estimation. The method proposed in this paper can be used to construct approximative covariance matrices for the observational noise using a small number of CT measurements. The capabilities of the proposed method are demonstrated using numerical simulations and also real CT data corresponding to measurements of contrast agent diffusion profile in cartilage.

Detection of Contrast Agents: Plane Wave Versus Focused Transmission.

Ultrasound contrast agent (UCA) imaging provides a cost-effective diagnostic tool to assess tissue perfusion and vascular pathologies. However, excessive transmission (TX) levels may negatively impact both uniform diffusion and survival rates of contrast agents, limiting their density and thus their echogenicity. Contrast detection methods with both high sensitivity and low-contrast destruction rate are thus essential to maintain diagnostic capabilities. Plane-wave TX with a high number of compounding angles has been suggested to produce good quality images at pressure levels that do not destroy UCA. In this paper, we performed a quantitative evaluation of detection efficacy of flowing UCA with either traditional focused scanning or ultrafast plane-wave imaging. Amplitude modulation (AM) at nondestructive pressure levels was implemented in the ULA-OP ultrasound research platform. The influence of the number of compounding angles, peak-negative pressure, and flow speed on the final image quality was investigated. Results show that the images obtained by compounding multiple angled plane waves offer a greater contrast (up to a 12-dB increase) with respect to focused AM. This increase is attributed mainly to noise reduction caused by the coherent summation in the compounding step. Additionally, we show that highly sensitive detection is already achieved with a limited compounding number ( ), thus suggesting the feasibility of continuous contrast monitoring at a high frame rate. This capability is essential to properly detect contrast agents flowing at high speed, as an excessive angle compounding is shown to be destructive for the contrast signal, as the UCA motion quickly causes loss of correlation between consecutive echoes.