Simultaneous MRI measurement of blood flow, blood volume, and capillary permeability in mammary tumors using two different contrast agents

Pixel-wise assessment of cardiovascular magnetic resonance first-pass perfusion using a cardiac phantom mimicking transmural myocardial perfusion gradients

PURPOSE

Cardiovascular magnetic resonance first-pass perfusion for the pixel-wise detection of coronary artery disease is rapidly becoming the clinical standard, yet no widely available method exists for its assessment and validation. This study introduces a novel phantom capable of generating spatially dependent flow values to enable assessment of new perfusion imaging methods at the pixel level.

METHODS

A synthetic multicapillary myocardial phantom mimicking transmural myocardial perfusion gradients was designed and manufactured with high-precision 3D printing. The phantom was used in a stationary flow setup providing reference myocardial perfusion rates and was scanned on a 3T system. Repeated first-pass perfusion MRI for physiological perfusion rates between 1 and 4 mL/g/min was performed using a clinical dual-sequence technique. Fermi function-constrained deconvolution was used to estimate pixel-wise perfusion rate maps. Phase contrast (PC)-MRI was used to obtain velocity measurements that were converted to perfusion rates for validation of reference values and cross-method comparison. The accuracy of pixel-wise maps was assessed against simulated reference maps.

RESULTS

PC-MRI indicated excellent reproducibility in perfusion rate (coefficient of variation [CoV] 2.4-3.5%) and correlation with reference values (R² = 0.985) across the full physiological range. Similar results were found for first-pass perfusion MRI (CoV 3.7-6.2%, R² = 0.987). Pixel-wise maps indicated a transmural perfusion difference of 28.8-33.7% for PC-MRI and 23.8-37.7% for first-pass perfusion, matching the reference values (30.2-31.4%).

CONCLUSION

The unique transmural perfusion pattern in the phantom allows effective pixel-wise assessment of first-pass perfusion acquisition protocols and quantification algorithms before their introduction into routine clinical use.

Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network

Quantitative DCE-MRI provides voxel-wise estimates of tracer-kinetic parameters that are valuable in the assessment of health and disease. These maps suffer from many known sources of variability. This variability is expensive to compute using current methods, and is typically not reported. Here, we demonstrate a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time. We train and use a neural network to estimate the approximate joint posterior distribution of tracer-kinetic parameters. Uncertainties are estimated for each voxel and are specific to the patient, exam, and lesion. We demonstrate the methods' ability to produce accurate tracer-kinetic maps. We compare predicted parameter ranges with uncertainties introduced by noise and by differences in post-processing in a digital reference object. The predicted parameter ranges correlate well with tracer-kinetic parameter ranges observed across different noise realizations and regression algorithms. We also demonstrate the value of this approach to differentiate significant from insignificant changes in brain tumor pharmacokinetics over time. This is achieved by enforcing consistency in resolving model singularities in the applied tracer-kinetic model.

Image-guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies

While plasma concentration kinetics has traditionally been the predictor of drug pharmacological effects, it can occasionally fail to represent kinetics at the site of action, particularly for solid tumors. This is especially true in the case of delivery of therapeutic macromolecules (drug-loaded nanomaterials or monoclonal antibodies), which can experience challenges to effective delivery due to particle size-dependent diffusion barriers at the target site. As a result, disparity between therapeutic plasma kinetics and kinetics at the site of action may exist, highlighting the importance of target site concentration kinetics in determining the pharmacodynamic effects of macromolecular therapeutic agents. Assessment of concentration kinetics at the target site has been facilitated by non-invasive in vivo imaging modalities. This allows for visualization and quantification of the whole-body disposition behavior of therapeutics that is essential for a comprehensive understanding of their pharmacokinetics and pharmacodynamics. Quantitative non-invasive imaging can also help guide the development and parameterization of mathematical models for descriptive and predictive purposes. Here, we present a review of the application of state-of-the-art imaging modalities for quantitative pharmacological evaluation of therapeutic nanoparticles and monoclonal antibodies, with a focus on their integration with mathematical models, and identify challenges and opportunities. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > in vivo Nanodiagnostics and Imaging Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Topics on quantitative liver magnetic resonance imaging

Liver magnetic resonance imaging (MRI) is subject to continuous technical innovations through advances in hardware, sequence and novel contrast agent development. In order to utilize the abilities of liver MR to its full extent and perform high-quality efficient exams, it is mandatory to use the best imaging protocol, to minimize artifacts and to select the most adequate type of contrast agent. In this article, we review the routine clinical MR techniques applied currently and some latest developments of liver imaging techniques to help radiologists and technologists to better understand how to choose and optimize liver MRI protocols that can be used in clinical practice. This article covers topics on (I) fat signal suppression; (II) diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) analysis; (III) dynamic contrast-enhanced (DCE) MR imaging; (IV) liver fat quantification; (V) liver iron quantification; and (VI) scan speed acceleration.

Estimation of cellular-interstitial water exchange in dynamic contrast enhanced MRI using two flip angles

PURPOSE

To investigate the feasibility of using multiple flip angles in dynamic contrast enhanced (DCE) MRI to reduce the uncertainty in estimation of intracellular water lifetime (τ_i ).

METHODS

Numerical simulation studies were conducted to assess the uncertainty in estimation of τ_i using dynamic contrast enhanced MRI with one or two flip angles. In vivo experiments with a murine brain tumor model were conducted at 7T using two flip angles. The in vivo data were used to compare τ_i estimation using the single-flip-angle (SFA) protocol with that using the double-flip-angle (DFA) protocol. Data analysis was conducted using the two-compartment exchange model combined with the three-site-two-exchange model for water exchange.

RESULTS

In the numerical simulation studies with a range of contrast kinetic parameters and signal-to-noise ratio = 20, the median bias of τ_i estimation decreased from 72 ms with SFA to 65 ms with DFA, and the corresponding median inter-quartile range reduced from 523 ms to 156 ms. In the in vivo studies, τ_i estimation with SFA was not successful in most voxels in the tumors, as the estimated τ_i values reached the upper limit of the parameter range (2 s). In contrast, the estimated τ_i values with DFA were mostly between 0.2 and 1.5 s and homogeneously distributed spatially across the tumor. The τ_i estimation with DFA was less sensitive to arterial input function scaling but more sensitive to pre-contrast T₁ than the other contrast kinetic parameters.

CONCLUSION

This study results demonstrate the feasibility of using multiple flip angles to encode the post-contrast time-intensity curve with different weighting of water exchange effect to reduce the uncertainty in τ_i estimation.

Spatially regularized estimation of the tissue homogeneity model parameters in DCE-MRI using proximal minimization

PURPOSE

The Tofts and the extended Tofts models are the pharmacokinetic models commonly used in dynamic contrast-enhanced MRI (DCE-MRI) perfusion analysis, although they do not provide two important biological markers, namely, the plasma flow and the permeability-surface area product. Estimates of such markers are possible using advanced pharmacokinetic models describing the vascular distribution phase, such as the tissue homogeneity model. However, the disadvantage of the advanced models lies in biased and uncertain estimates, especially when the estimates are computed voxelwise. The goal of this work is to improve the reliability of the estimates by including information from neighboring voxels.

THEORY AND METHODS

Information from the neighboring voxels is incorporated in the estimation process through spatial regularization in the form of total variation. The spatial regularization is applied on five maps of perfusion parameters estimated using the tissue homogeneity model. Since the total variation is not differentiable, two proximal techniques of convex optimization are used to solve the problem numerically.

RESULTS

The proposed algorithm helps to reduce noise in the estimated perfusion-parameter maps together with improving accuracy of the estimates. These conclusions are proved using a numerical phantom. In addition, experiments on real data show improved spatial consistency and readability of perfusion maps without considerable lowering of the quality of fit.

CONCLUSION

The reliability of the DCE-MRI perfusion analysis using the tissue homogeneity model can be improved by employing spatial regularization. The proposed utilization of modern optimization techniques implies only slightly higher computational costs compared to the standard approach without spatial regularization.

Blind deconvolution estimation of an arterial input function for small animal DCE-MRI

PURPOSE

One of the main obstacles for reliable quantitative dynamic contrast-enhanced (DCE) MRI is the need for accurate knowledge of the arterial input function (AIF). This is a special challenge for preclinical small animal applications where it is very difficult to measure the AIF without partial volume and flow artifacts. Furthermore, using advanced pharmacokinetic models (allowing estimation of blood flow and permeability-surface area product in addition to the classical perfusion parameters) poses stricter requirements on the accuracy and precision of AIF estimation. This paper addresses small animal DCE-MRI with advanced pharmacokinetic models and presents a method for estimation of the AIF based on blind deconvolution.

METHODS

A parametric AIF model designed for small animal physiology and use of advanced pharmacokinetic models is proposed. The parameters of the AIF are estimated using multichannel blind deconvolution.

RESULTS

Evaluation on simulated data show that for realistic signal to noise ratios blind deconvolution AIF estimation leads to comparable results as the use of the true AIF. Evaluation on real data based on DCE-MRI with two contrast agents of different molecular weights showed a consistence with the known effects of the molecular weight.

CONCLUSION

Multi-channel blind deconvolution using the proposed AIF model specific for small animal DCE-MRI provides reliable perfusion parameter estimates under realistic signal to noise conditions.

Two decades of dendrimers as versatile MRI agents: a tale with and without metals

Dendrimers or dendritic polymers are a class of compounds with great potential for nanomedical use. Some of their properties, including their rigidity, low polydispersity and the ease with which their surfaces can be modified make them particularly well suited for use as MRI diagnostic or theranostic agents. For the past 20 years, researchers have recognized this potential and refined dendrimer formulations to optimize these nanocarriers for a host of MRI applications, including blood pool imaging agents, lymph node imaging agents, tumor-targeted theranostic agents and cell tracking agents. This review summarizes the various types of dendrimers according to the type of MR contrast they can provide. This includes the metallic T1 , T2 and paraCEST imaging agents, and the non-metallic diaCEST and fluorinated (19 F) heteronuclear imaging agents. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Implantable Materials and Surgical Technologies > Nanomaterials and Implants.

Comparing the Performances of Magnetic Resonance Imaging Size vs Pharmacokinetic Parameters to Predict Response to Neoadjuvant Chemotherapy and Survival in Patients With Breast Cancer

PURPOSE

To compare the value of dynamic contrast-enhanced magnetic resonance imaging-pharmacokinetic (PK) parameters vs tumor volume in predicting breast cancer neoadjuvant chemotherapy response (NACR) and patient survival.

SUBJECTS AND METHODS

Sixty-six patients with locally advanced breast cancer who underwent breast MRI monitoring of NACR were retrospectively analyzed. We compared baseline transfer constant (K^trans), reflux rate contrast (k_ep), and extracellular extravascular volume fraction (v_e) with the same parameters obtained at early postchemotherapy MRI, and examined model-independent changes in time-intensity curves (maximum slope, contrast enhancement ratio, and IAUC90). Tumor size changes (tumor volume, single dimension, and Response Evaluation Criteria in Solid Tumors [RECIST]) were also analyzed. The Spearman correlation test was used to assess the association between size and PK parameters, and regression analysis to assess the association with 5-year disease-free survival.

RESULTS

Higher v_e values at baseline were associated with greater decreases in tumor size (P = 0.008). Changes in K^trans and IAUC90 were the strongest predictors of NACR. Changes in IAUC90 (P = 0.04) and RECIST (P = 0.003) were independently associated with pathologic response. The only parameter significantly associated with 5-year survival was change in RECIST (P = 0.001). However, there was a trend toward statistical significance for changes in v_e and K^trans, with greater changes associated with longer survival.

CONCLUSION

Changes in PK and dynamic contrast-enhanced magnetic resonance imaging kinetic parameters may have a role in predicting NACR in breast tumors. Although changes in K^trans and IAUC90 are helpful in predicting NACR, they do not show significant association with survival. Early RECIST size change measured by MRI remains the strongest predictor of overall patient survival.

Improved Evaluation of Antivascular Cancer Therapy Using Constrained Tracer-Kinetic Modeling for Multiagent Dynamic Contrast-Enhanced MRI

Dynamic contrast-enhanced MRI (DCE-MRI) is a promising technique for assessing the response of tumor vasculature to antivascular therapies. Multiagent DCE-MRI employs a combination of low and high molecular weight contrast agents, which potentially improves the accuracy of estimation of tumor hemodynamic and vascular permeability parameters. In this study, we used multiagent DCE-MRI to assess changes in tumor hemodynamics and vascular permeability after vascular-disrupting therapy. Multiagent DCE-MRI (sequential injection of G5 dendrimer, G2 dendrimer, and Gd-DOTA) was performed in tumor-bearing mice before, 2 and 24 hours after treatment with vascular disrupting agent DMXAA or placebo. Constrained DCE-MRI gamma capillary transit time modeling was used to estimate flow F, blood volume fraction v_b, mean capillary transit time t_c, bolus arrival time t_d, extracellular extravascular fraction v_e, vascular heterogeneity index α^-1 (all identical between agents) and extraction fraction E (reflective of permeability), and transfer constant K^trans (both agent-specific) in perfused pixels. F, v_b, and α^-1 decreased at both time points after DMXAA, whereas t_c increased. E (G2 and G5) showed an initial increase, after which, both parameters restored. K^trans (G2 and Gd-DOTA) decreased at both time points after treatment. In the control, placebo-treated animals, only F, t_c, and K^trans Gd-DOTA showed significant changes. Histologic perfused tumor fraction was significantly lower in DMXAA-treated versus control animals. Our results show how multiagent tracer-kinetic modeling can accurately determine the effects of vascular-disrupting therapy by separating simultaneous changes in tumor hemodynamics and vascular permeability.Significance: These findings describe a new approach to measure separately the effects of antivascular therapy on tumor hemodynamics and vascular permeability, which could help more rapidly and accurately assess the efficacy of experimental therapy of this class. Cancer Res; 78(6); 1561-70. ©2018 AACR.

Validation of Blood Volume Fraction Quantification with 3D Gradient Echo Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Porcine Skeletal Muscle

The purpose of this study was to assess the accuracy of fractional blood volume (v_b) estimates in low-perfused and low-vascularized tissue using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The results of different MRI methods were compared with histology to evaluate the accuracy of these methods under clinical conditions. v_b was estimated by DCE-MRI using a 3D gradient echo sequence with k-space undersampling in five muscle groups in the hind leg of 9 female pigs. Two gadolinium-based contrast agents (CA) were used: a rapidly extravasating, extracellular, gadolinium-based, low-molecular-weight contrast agent (LMCA, gadoterate meglumine) and an extracellular, gadolinium-based, albumin-binding, slowly extravasating blood pool contrast agent (BPCA, gadofosveset trisodium). LMCA data were evaluated using the extended Tofts model (ETM) and the two-compartment exchange model (2CXM). The images acquired with administration of the BPCA were used to evaluate the accuracy of v_b estimation with a bolus deconvolution technique (BD) and a method we call equilibrium MRI (EqMRI). The latter calculates the ratio of the magnitude of the relaxation rate change in the tissue curve at an approximate equilibrium state to the height of the same area of the arterial input function (AIF). Immunohistochemical staining with isolectin was used to label endothelium. A light microscope was used to estimate the fractional vascular area by relating the vascular region to the total tissue region (immunohistochemical vessel staining, IHVS). In addition, the percentage fraction of vascular volume was determined by multiplying the microvascular density (MVD) with the average estimated capillary lumen, π(d2)2, where d = 8μm is the assumed capillary diameter (microvascular density estimation, MVDE). Except for ETM values, highly significant correlations were found between most of the MRI methods investigated. In the cranial thigh, for example, the v_b medians (interquartile range, IQRs) of IHVS, MVDE, BD, EqMRI, 2CXM and ETM were v_b = 0.7(0.3)%, 1.1(0.4)%, 1.1(0.4)%, 1.4(0.3)%, 1.2(1.8)% and 0.1(0.2)%, respectively. Variances, expressed by the difference between third and first quartiles (IQR) were highest for the 2CXM for all muscle groups. High correlations between the values in four muscle groups—medial, cranial, lateral thigh and lower leg - estimated with MRI and histology were found between BD and EqMRI, MVDE and 2CXM and IHVS and ETM. Except for the ETM, no significant differences between the v_b medians of all MRI methods were revealed with the Wilcoxon rank sum test. The same holds for all muscle regions using the 2CXM and MVDE. Except for cranial thigh muscle, no significant difference was found between EqMRI and MVDE. And except for the cranial thigh and the lower leg muscle, there was also no significant difference between the v_b medians of BD and MVDE. Overall, there was good v_b agreement between histology and the BPCA MRI methods and the 2CXM LMCA approach with the exception of the ETM method. Although LMCA models have the advantage of providing excellent curve fits and can in principle determine more physiological parameters than BPCA methods, they yield more inaccurate results.

Flow versus permeability weighting in estimating the forward volumetric transfer constant (Ktrans) obtained by DCE-MRI with contrast agents of differing molecular sizes

PURPOSE

To quantify the differential plasma flow- (F_p-) and permeability surface area product per unit mass of tissue- (PS-) weighting in forward volumetric transfer constant (K^trans) estimates by using a low molecular (Gd-DTPA) versus high molecular (Gadomer) weight contrast agent in dynamic contrast enhanced (DCE) MRI.

MATERIALS AND METHODS

DCE MRI was performed using a 7T animal scanner in 14 C57BL/6J mice syngeneic for TRAMP tumors, by administering Gd-DTPA (0.9kD) in eight mice and Gadomer (35kD) in the remainder. The acquisition time was 10min with a sampling rate of one image every 2s. Pharmacokinetic modeling was performed to obtain K^trans by using Extended Tofts model (ETM). In addition, the adiabatic approximation to the tissue homogeneity (AATH) model was employed to obtain the relative contributions of F_p and PS.

RESULTS

The K^trans values derived from DCE-MRI with Gd-DTPA showed significant correlations with both PS (r²=0.64, p=0.009) and F_p (r²=0.57, p=0.016), whereas those with Gadomer were found only significantly correlated with PS (r²=0.96, p=0.0003) but not with F_p (r²=0.34, p=0.111). A voxel-based analysis showed that K^trans approximated PS (<30% difference) in 78.3% of perfused tumor volume for Gadomer, but only 37.3% for Gd-DTPA.

CONCLUSIONS

The differential contributions of F_p and PS in estimating K^trans values vary with the molecular weight of the contrast agent used. The macromolecular contrast agent resulted in K^trans values that were much less dependent on flow. These findings support the use of macromolecular contrast agents for estimating tumor vessel permeability with DCE-MRI.

Kinetic Curve Type Assessment for Classification of Breast Lesions Using Dynamic Contrast-Enhanced MR Imaging

OBJECTIVE

The aim of this study was to employ a kinetic model with dynamic contrast enhancement-magnetic resonance imaging to develop an approach that can efficiently distinguish malignant from benign lesions.

MATERIALS AND METHODS

A total of 43 patients with 46 lesions who underwent breast dynamic contrast enhancement-magnetic resonance imaging were included in this retrospective study. The distribution of malignant to benign lesions was 31/15 based on histological results. This study integrated a single-compartment kinetic model and dynamic contrast enhancement-magnetic resonance imaging to generate a kinetic modeling curve for improving the accuracy of diagnosis of breast lesions. Kinetic modeling curves of all different lesions were analyzed by three experienced radiologists and classified into one of three given types. Receiver operating characteristic and Kappa statistics were used for the qualitative method. The findings of the three radiologists based on the time-signal intensity curve and the kinetic curve were compared.

RESULTS

An average sensitivity of 82%, a specificity of 65%, an area under the receiver operating characteristic curve of 0.76, and a positive predictive value of 82% and negative predictive value of 63% was shown with the kinetic model (p = 0.017, 0.052, 0.068), as compared to an average sensitivity of 80%, a specificity of 55%, an area under the receiver operating characteristic of 0.69, and a positive predictive value of 79% and negative predictive value of 57% with the time-signal intensity curve method (p = 0.003, 0.004, 0.008). The diagnostic consistency of the three radiologists was shown by the κ-value, 0.857 (p<0.001) with the method based on the time-signal intensity curve and 0.826 (p<0.001) with the method of the kinetic model.

CONCLUSIONS

According to the statistic results based on the 46 lesions, the kinetic modeling curve method showed higher sensitivity, specificity, positive and negative predictive values as compared with the time-signal intensity curve method in lesion classification.

Models and methods for analyzing DCE-MRI: a review

PURPOSE

To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.

METHODS

DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.

RESULTS

Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.

CONCLUSIONS

Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

Assessment of vessel permeability by combining dynamic contrast-enhanced and arterial spin labeling MRI

The forward volumetric transfer constant (K(trans)), a physiological parameter extracted from dynamic contrast-enhanced (DCE) MRI, is weighted by vessel permeability and tissue blood flow. The permeability × surface area product per unit mass of tissue (PS) in brain tumors was estimated in this study by combining the blood flow obtained through pseudo-continuous arterial spin labeling (PCASL) and K(trans) obtained through DCE MRI. An analytical analysis and a numerical simulation were conducted to understand how errors in the flow and K(trans) estimates would propagate to the resulting PS. Fourteen pediatric patients with brain tumors were scanned on a clinical 3-T MRI scanner. PCASL perfusion imaging was performed using a three-dimensional (3D) fast-spin-echo readout module to determine blood flow. DCE imaging was performed using a 3D spoiled gradient-echo sequence, and the K(trans) map was obtained with the extended Tofts model. The numerical analysis demonstrated that the uncertainty of PS was predominantly dependent on that of K(trans) and was relatively insensitive to the flow. The average PS values of the whole tumors ranged from 0.006 to 0.217 min(-1), with a mean of 0.050 min(-1) among the patients. The mean K(trans) value was 18% lower than the PS value, with a maximum discrepancy of 25%. When the parametric maps were compared on a voxel-by-voxel basis, the discrepancies between PS and K(trans) appeared to be heterogeneous within the tumors. The PS values could be more than two-fold higher than the K(trans) values for voxels with high K(trans) levels. This study proposes a method that is easy to implement in clinical practice and has the potential to improve the quantification of the microvascular properties of brain tumors.

Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI

PURPOSE

One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used.

METHODS

Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested.

RESULTS

The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates.

CONCLUSION

Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup.

A novel approach to tracer-kinetic modeling for (macromolecular) dynamic contrast-enhanced MRI

PURPOSE

To develop a novel tracer-kinetic modeling approach for multi-agent dynamic contrast-enhanced MRI (DCE-MRI) that facilitates separate estimation of parameters characterizing blood flow and microvascular permeability within one individual.

METHODS

Monte Carlo simulations were performed to investigate the performance of the constrained multi-agent model. Subsequently, multi-agent DCE-MRI was performed on tumor-bearing mice (n = 5) on a 7T Bruker scanner on three measurement days, in which two dendrimer-based contrast agents having high and intermediate molecular weight, respectively, along with gadoterate meglumine, were sequentially injected within one imaging session. Multi-agent data were simultaneously fit with the gamma capillary transit time model. Blood flow, mean capillary transit time, and bolus arrival time were constrained to be identical between the boluses, while extraction fractions and washout rate constants were separately determined for each agent.

RESULTS

Simulations showed that constrained multi-agent model regressions led to less uncertainty and bias in estimated tracer-kinetic parameters compared with single-bolus modeling. The approach was successfully applied in vivo, and significant differences in the extraction fraction and washout rate constant between the agents, dependent on their molecular weight, were consistently observed.

CONCLUSION

A novel multi-agent tracer-kinetic modeling approach that enforces self-consistency of model parameters and can robustly characterize tumor vascular status was demonstrated.

Imaging biomarkers for antiangiogenic therapy in malignant gliomas

The discovery that malignant gliomas produce an excessive amount of VEGF, a key mediator of angiogenesis, has heightened interest in developing drugs that block angiogenic pathways. These antiangiogenic drugs tend to decrease vascular permeability, thereby diminishing tumor contrast enhancement independent of anti-tumor effects. This has made the determination of tumor response difficult, since contrast enhancement on post-contrast T1-weighted images is standard for assessing therapy effectiveness. In light of these unique challenges in assessing antiangiogenic therapy, new biomarkers have been proposed, based on advanced magnetic resonance techniques and PET. This article outlines the challenges associated with the evaluation of antiangiogenic therapy in malignant gliomas and describes how new imaging biomarkers can be used to better predict response.

Real-time electrical impedance variations in women with and without breast cancer

The chaotic vascular network surrounding malignant tumors leads to pulsatile blood flow patterns that differ from those in benign regions of the breast. This study aimed to determine if high-speed electrical impedance tomography (EIT) is able to detect conductivity changes associated with cyclic blood-volume changes and to gauge the potential of using these signatures to differentiate malignant from benign regions within the breast. EIT imaging of pulsating latex membranes submerged in saline baths provided initial validation of its use for tracking temporally varying conductivities. Nineteen women (10 with cancer, nine without) were imaged with EIT over the course of several heartbeats in synchrony with pulse-oximetry acquisition. Eight parameters ( rs, ϕ(rt,max), rt,max, Plow:full, Phigh:full, Plow:high) relating the conductivity images and pulse-oximeter signatures were extracted and used as a means of comparing malignant and benign regions of the breast. Significant differences between malignant and benign regions of interest were noted in seven of the eight parameters. The maximum correlation between conductivity and pulse-oximeter signals, rt,max , was observed to be the optimal discriminating parameter with a receiver operating characteristic area under the curve of 0.8 and a specificity of 81% at a sensitivity of 77%. Assessing the dynamic conductivity of breast may provide additional clinical utility to that of standard imaging modalities, but further investigation is necessary to better understand the biophysical mechanisms leading to the observed conductivity changes.

Preoperative prognostic value of dynamic contrast-enhanced MRI-derived contrast transfer coefficient and plasma volume in patients with cerebral gliomas

BACKGROUND AND PURPOSE

The prognostic value of dynamic contrast-enhanced MR imaging-derived plasma volume obtained in tumor and the contrast transfer coefficient has not been well-established in patients with gliomas. We determined whether plasma volume and contrast transfer coefficient in tumor correlated with survival in patients with gliomas in addition to other factors such as age, type of surgery, preoperative Karnofsky score, contrast enhancement, and histopathologic grade.

MATERIALS AND METHODS

This prospective study included 46 patients with a new pathologically confirmed diagnosis of glioma. The contrast transfer coefficient and plasma volume obtained in tumor maps were calculated directly from the signal-intensity curve without T1 measurements, and values were obtained from multiple small ROIs placed within tumors. Survival curve analysis was performed by dichotomizing patients into groups of high and low contrast transfer coefficient and plasma volume. Univariate analysis was performed by using dynamic contrast-enhanced parameters and clinical factors. Factors that were significant on univariate analysis were entered into multivariate analysis.

RESULTS

For all patients with gliomas, survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor (P < .05). In subgroups of high- and low-grade gliomas, survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor (P < .05). Univariate analysis showed that factors associated with lower survival were age older than 50 years, low Karnofsky score, biopsy-only versus resection, marked contrast enhancement versus no/mild enhancement, high contrast transfer coefficient, and high plasma volume obtained in tumor (P < .05). In multivariate analysis, a low Karnofsky score, biopsy versus resection in combination with marked contrast enhancement, and a high contrast transfer coefficient were associated with lower survival rates (P < .05).

CONCLUSIONS

In patients with glioma, those with a high contrast transfer coefficient have lower survival than those with low parameters.

In vivo assessment of aqueous humor dynamics upon chronic ocular hypertension and hypotensive drug treatment using gadolinium-enhanced MRI

PURPOSE

Although glaucoma treatments alter aqueous humor (AH) dynamics to lower intraocular pressure, the regulatory mechanisms of AH circulation and their contributions to the pathogenesis of ocular hypertension and glaucoma remain unclear. We hypothesized that gadolinium-enhanced magnetic resonance imaging (Gd-MRI) can visualize and assess AH dynamics upon sustained intraocular pressure elevation and pharmacologic interventions.

METHODS

Gadolinium contrast agent was systemically administered to adult rats to mimic soluble AH components entering the anterior chamber (AC) via blood-aqueous barrier. Dynamic Gd-MRI was applied to examine the signal enhancement in AC and vitreous body upon microbead-induced ocular hypertension and unilateral topical applications of latanoprost, timolol maleate, and brimonidine tartrate to healthy eyes.

RESULTS

Gadolinium signal time courses in microbead-induced hypertensive eyes possessed faster initial gadolinium uptake and higher peak signals in AC than control eyes, reflective of reduced gadolinium clearance upon microbead occlusion. Opposite trends were observed in latanoprost- and timolol-treated eyes, indicative of their respective drug actions on increased uveoscleral outflow and reduced AH production. The slowest initial gadolinium uptake but strongest peak signals were found in AC of both brimonidine-treated and untreated fellow eyes. These findings drew attention to the systemic effects of topical hypotensive drug treatment. Gadolinium leaked into the vitreous of microbead-induced hypertensive eyes and brimonidine-treated and untreated fellow eyes, suggestive of a compromise of aqueous-vitreous or blood-ocular barrier integrity.

CONCLUSIONS

Gadolinium-enhanced MRI allows spatiotemporal and quantitative evaluation of altered AH dynamics and ocular tissue permeability for better understanding the physiological mechanisms of ocular hypertension and the efficacy of antiglaucoma drug treatments.

Dynamic contrast-enhanced computed tomography to assess antitumor treatment effects: comparison of two contrast agents with different pharmacokinetics

OBJECTIVE

Dynamic contrast-enhanced computed tomography (CT) is a sensitive method to evaluate functional changes of the tumor microvasculature after antitumor therapy by monitoring the kinetics of the contrast agent (CA) passage. Therefore, the pharmacokinetic properties of the CA possess a central role: iodinated x-ray CAs are small molecules that distribute rapidly within the extravascular extracellular space, whereas larger macromolecular compounds have a prolonged vascular phase and a restricted volume of distribution. The aim of this animal study was to compare the x-ray CA iopromide and the experimental gadolinium-based dendrimeric Gadomer in the assessment of early therapy response after a single dose of the novel multikinase inhibitor regorafenib.

MATERIALS AND METHODS

For the study, an experimental GS9L rat glioma model was used. For each CA, the animals were divided randomly into a therapy (n = 8) and a placebo group (n = 4). All animals underwent a baseline CT and a second examination 24 hours after therapy with regorafenib (10 mg/kg body weight, oral) and placebo, respectively. The CAs were administered intravenously at a dosage of 0.5 g I or Gd per kg body weight and dynamic CT scans (80 kV, 160 mAseff, no table feed) of the tumor region were performed up to 404 seconds post injection (p.i.). Image evaluation was done by analyzing tumor time-density curves, the area under the curve (AUC), and the results of the 2-compartment Patlak modeling.

RESULTS

Significant differences in the time-density curves, the AUC, and the Patlak transfer constant (Ktrans) were observed 24 hours after the regorafenib therapy but not after the placebo treatment. The treatment effects visualized with iopromide were most pronounced at early time points (<100 seconds p.i.), whereas imaging with Gadomer was most effective at a later time window (300-404 seconds p.i.). Comparable reductions of the AUC to 0.69 ± 0.12 (iopromide) and 0.76 ± 0.11 (Gadomer) were found 24 hours after the therapy. A significant higher Ktrans was detected with iopromide (14.3 ± 2.7 mL per 100 mL/min) compared with Gadomer (1.8 ± 0.2 mL per 100 mL/min). However, the relative reduction in Ktrans to 67% ± 11% (iopromide) and to 68% ± 7% (Gadomer) 24 hours after the therapy was similar.

CONCLUSIONS

Dynamic contrast-enhanced CT detects early treatment effects on tumor microvasculature after a single dose of regorafenib, independently of the used CA. Gadomer showed a later optimal imaging window than iopromide did. However, the efficacy of Gadomer- and iopromide-enhanced imaging is equivalent. The results demonstrate the potential of dynamic contrast-enhanced CT using clinically available x-ray CA in the assessment of early treatment response after administration of novel antitumor therapeutic agents.

Evaluation of a novel macromolecular cascade-polymer contrast medium for dynamic contrast-enhanced MRI monitoring of antiangiogenic bevacizumab therapy in a human melanoma model

RATIONALE AND OBJECTIVES

To assess the applicability of a novel macromolecular polyethylene glycol (PEG)-core gadolinium contrast agent for monitoring early antiangiogenic effects of bevacizumab using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Athymic rats (n = 26) implanted with subcutaneous human melanoma xenografts underwent DCE-MRI at 2.0 T using two different macromolecular contrast agents. The PEG core cascade polymer PEG12,000-Gen4-(Gd-DOTA)16, designed for clinical development, was compared to the prototype, animal-only, macromolecular contrast medium (MMCM) albumin-(Gd-DTPA)35. The treatment (n = 13) and control (n = 13) group was imaged at baseline and 24 hours after a single dose of bevacizumab (1 mg) or saline to quantitatively assess the endothelial-surface permeability constant (K(PS), μL⋅min⋅100 cm(3)) and the fractional plasma volume (fPV,%), using a two-compartment kinetic model.

RESULTS

Mean K(PS) values, assessed with PEG12,000-Gen4-(Gd-DOTA)16, declined significantly (P < .05) from 29.5 ± 10 μL⋅min⋅100 cm(3) to 10.4 ± 7.8 μL⋅min⋅100 cm(3) by 24 hours after a single dose of bevacizumab. In parallel, K(PS) values quantified using the prototype MMCM albumin-(Gd-DTPA)35 showed an analogous, significant decline (P < .05) in the therapy group. No significant effects were detected on tumor vascularity or on microcirculatory parameters in the control group between the baseline and the follow-up scan at 24 hours.

CONCLUSION

DCE-MRI enhanced with the novel MMCM PEG12,000-Gen4-(Gd-DOTA)16 was able to monitor the effects of bevacizumab on melanoma xenografts within 24 hours of a single application, validated by the prototype, animal-only albumin-(Gd-DTPA)35. PEG12,000-Gen4-(Gd-DOTA)16 may be a promising candidate for further clinical development as a macromolecular blood pool contrast MRI agent.

Uncertainty in MR tracer kinetic parameters and water exchange rates estimated from T1-weighted dynamic contrast enhanced MRI

PURPOSE

The aim of this study was to assess the uncertainty in estimation of MR tracer kinetic parameters and water exchange rates in T1-weighted dynamic contrast enhanced (DCE) MRI.

METHODS

Simulated DCE-MRI data were used to assess four kinetic models; general kinetic model with a vascular compartment (GKM2), GKM2 combined with water exchange (SSM2), adiabatic approximation of the tissue homogeneity model (ATH), and ATH combined with water exchange (ATHX).

RESULTS

In GKM2 and SSM2, increase in transfer constant (K(trans)) led to underestimation of vascular volume fraction (vb), and increase in vb led to overestimation of K(trans). Such coupling between K(trans) and vb was not observed in ATH and ATHX. The precision of estimated intracellular water lifetime (τi) was substantially improved in both SSM2 and ATHX when K(trans) > 0.3 min(-1). K(trans) and vb from ATHX model had significantly smaller errors than those from ATH model (P < 0.05).

CONCLUSION

The results of this study demonstrated the feasibility of measuring τi from DCE-MRI data albeit low precision. While the inclusion of water exchange improved the accuracy of K(trans), vb, and the interstitial volume fraction estimation (ve), it lowered the precision of other kinetic model parameters within the conditions investigated in this study.

Assessment of tumor blood flow distribution by dynamic contrast-enhanced CT

A distinct feature of the tumor vasculature is its tortuosity and irregular branching of vessels, which can translate to a wider dispersion and higher variability of blood flow in the tumor. To enable tumor blood flow variability to be assessed in vivo by imaging, a tracer kinetic model that accounts for flow dispersion is developed for use with dynamic contrast-enhanced (DCE) CT. The proposed model adopts a multiple-pathway approach and allows for the quantification of relative dispersion in the blood flow distribution, which reflects flow variability in the tumor vasculature. Monte Carlo simulation experiments were performed to study the possibility of reducing the number of model parameters based on the Akaike information criterion approach and to explore possible noise and tissue conditions in which the model might be applicable. The model was used for region-of-interest analysis and to generate perfusion parameter maps for three patient DCE CT cases with cerebral tumors, to illustrate clinical applicability.

Classic models for dynamic contrast-enhanced MRI

Dynamic contrast-enhanced MRI (DCE-MRI) is a functional MRI method where T1 -weighted MR images are acquired dynamically after bolus injection of a contrast agent. The data can be interpreted in terms of physiological tissue characteristics by applying the principles of tracer-kinetic modelling. In the brain, DCE-MRI enables measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), blood-brain barrier (BBB) permeability-surface area product (PS) and the volume of the interstitium (ve ). These parameters can be combined to form others such as the volume-transfer constant K(trans) , the extraction fraction E and the contrast-agent mean transit times through the intra- and extravascular spaces. A first generation of tracer-kinetic models for DCE-MRI was developed in the early 1990s and has become a standard in many applications. Subsequent improvements in DCE-MRI data quality have driven the development of a second generation of more complex models. They are increasingly used, but it is not always clear how they relate to the models of the first generation or to the model-free deconvolution methods for tissues with intact BBB. This lack of understanding is leading to increasing confusion on when to use which model and how to interpret the parameters. The purpose of this review is to clarify the relation between models of the first and second generations and between model-based and model-free methods. All quantities are defined using a generic terminology to ensure the widest possible scope and to reveal the link between applications in the brain and in other organs.

Assessment of the interstitial fluid pressure of tumors by dynamic contrast-enhanced magnetic resonance imaging with contrast agents of different molecular weights

BACKGROUND

Cancer patients showing highly elevated interstitial fluid pressure (IFP) in the primary tumor may benefit from particularly aggressive treatment. There is some evidence that gadolinium diethylene-triamine penta-acetic acid (Gd-DTPA)-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may be a useful non-invasive method for providing information on the IFP of tumors. The purpose of this preclinical study was to investigate whether any association between DCE-MRI-derived parametric images and tumor IFP can be strengthened by using MR contrast agents with higher molecular weights than that of Gd-DTPA.

MATERIAL AND METHODS

A-07 human melanoma xenografts were used as preclinical models of human cancer. Three contrast agents were compared: Gd-DTPA (0.55 kDa), P846 (3.5 kDa), and gadomelitol (6.5 kDa). A total of 46 tumors were subjected to DCE-MRI and subsequent measurement of IFP. Parametric images of K(trans) (the volume transfer constant of the contrast agent) and v(e) (the fractional distribution volume of the contrast agent) were produced by pharmacokinetic analysis of the DCE-MRI series.

RESULTS

Significant inverse correlations were found between median K(trans) and IFP for Gd-DTPA (p = 0.0076; R(2) = 0.46; n = 14) and P846 (p = 0.0042; R(2) = 0.45; n = 16), whereas there was no correlation between median K(trans) and IFP for gadomelitol (p > 0.05; n = 16). Significant correlation between median v(e) and IFP was not found for any of the contrast agents (p > 0.05 for Gd-DTPA, P846, and gadomelitol).

CONCLUSION

K(trans) images, but not v(e) images, derived by pharmacokinetic analysis of DCE-MRI data for low-molecular-weight contrast agents may provide information on the IFP of tumors. Any association between K(trans) and IFP cannot be expected to be improved by using contrast agents with higher molecular weights than those of Gd-DTPA and P846.

High temporal resolution 3D gadolinium-enhanced dynamic MR imaging of renal tumors with pharmacokinetic modeling: preliminary observations

PURPOSE

To assess dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) tracer pharmacokinetic parameters obtained with Generalized Kinetic Model (GKM) and extended Shutter Speed Model (SSM2) in renal tumors stratified by histologic subtypes.

MATERIALS AND METHODS

In all, 24 patients with renal tumors were imaged at 1.5 T utilizing DCE-MRI with high temporal resolution (1.2 sec/temporal frame) prior to surgery. Tracer kinetic analysis was performed for the entire tumor using individualized aortic input function. GKM and SSM2 were employed to generate transfer constant (K(trans)), plasma volume, and interstitial volume. These parameters, and ΔK(trans) (K(trans)SSM2 - K(trans)GKM) were compared between tumors stratified by histologic subtype.

RESULTS

There were 25 renal tumors: 15 clear cell, 4 papillary, 3 chromophobe, and 3 oncocytoma/oncocytic subtype. K(trans)GKM was significantly higher in chromophobe compared to other subtypes (P < 0.01). Using K(trans)GKM > 1.0 min(-1), chromophobe were diagnosed with 100% sensitivity and 90.9% specificity. K(trans)SSM2 was higher than K(trans)GKM for all renal tumors except for all chromophobe and two clear cell subtype. Using K(trans)GKM > 1.0 min(-1) and Δ K(trans) < 0, chromophobe could be discriminated from other lesions with 100% accuracy.

CONCLUSION

K(trans) obtained with GKM and SSM2 analysis can potentially discriminate chromophobe from other renal lesions with high accuracy.

Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability

The tracer-kinetic models developed in the early 1990s for dynamic contrast-enhanced MRI (DCE-MRI) have since become a standard in numerous applications. At the same time, the development of MRI hardware has led to increases in image quality and temporal resolution that reveal the limitations of the early models. This in turn has stimulated an interest in the development and application of a second generation of modelling approaches. They are designed to overcome these limitations and produce additional and more accurate information on tissue status. In particular, models of the second generation enable separate estimates of perfusion and capillary permeability rather than a single parameter K(trans) that represents a combination of the two. A variety of such models has been proposed in the literature, and development in the field has been constrained by a lack of transparency regarding terminology, notations and physiological assumptions. In this review, we provide an overview of these models in a manner that is both physically intuitive and mathematically rigourous. All are derived from common first principles, using concepts and notations from general tracer-kinetic theory. Explicit links to their historical origins are included to allow for a transfer of experience obtained in other fields (PET, SPECT, CT). A classification is presented that reveals the links between all models, and with the models of the first generation. Detailed formulae for all solutions are provided to facilitate implementation. Our aim is to encourage the application of these tools to DCE-MRI by offering researchers a clearer understanding of their assumptions and requirements.

Fundamentals of tracer kinetics for dynamic contrast-enhanced MRI

Tracer kinetic methods employed for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) share common roots with earlier tracer studies involving arterial-venous sampling and other dynamic imaging modalities. This article reviews the essential foundation concepts and principles in tracer kinetics that are relevant to DCE MRI, including the notions of impulse response and convolution, which are central to the analysis of DCE MRI data. We further examine the formulation and solutions of various compartmental models frequently used in the literature. Topics of recent interest in the processing of DCE MRI data, such as the account of water exchange and the use of reference tissue methods to obviate the measurement of an arterial input, are also discussed. Although the primary focus of this review is on the tracer models and methods for T(1) -weighted DCE MRI, some of these concepts and methods are also applicable for analysis of dynamic susceptibility contrast-enhanced MRI data.

Assessment of tumor angiogenesis: dynamic contrast-enhanced MRI with paramagnetic nanoparticles compared with Gd-DTPA in a rabbit Vx-2 tumor model

The purpose of this study was to evaluate the suitability of a macromolecular MRI contrast agent (paramagnetic nanoparticles, PNs) for the characterization of tumor angiogenesis. Our aim was to estimate the permeability of PNs in developing tumor vasculature and compare it with that of a low molecular weight contrast agent (Gd-DTPA) using dynamic contrast-enhanced MRI (DCE). Male New Zealand white rabbits (n = 5) underwent DCE MRI 12-14 days after Vx-2 tumor fragments were implanted into the left hind limb. Each contrast agent (PNs followed by Gd-DTPA) was evaluated using a DCE protocol and transendothelial transfer coefficient (K(i)) maps were calculated using a two-compartment model. Two regions of interest (ROIs) were located within the tumor core and hindlimb muscle and five ROIs were placed within the tumor rim. Comparisons were performed using repeated measures analysis of variance (ANOVA). The K(i) values estimated using PNs were significantly lower than those obtained for Gd-DTPA (p = 0.018). When PNs and Gd-DTPA data were analyzed separately, significant differences were identified among tumor rim ROIs for PNs (p < 0.0001), but not for Gd-DTPA data (p = 0.34). The mean K(i) for the tumor rim was significantly greater than that of either the core or the hindlimb muscle for both contrast agents (p < 0.05 for each comparison). In summary, the extravasation of Gd-DTPA was far greater than that of PNs, suggesting that PNs can reveal regional differences in tumor vascular permeability that are not otherwise apparent with clinical contrast agents such as Gd-DTPA. These results suggest that PNs show potential for the noninvasive delineation of tumor angiogenesis.

Multi-parametric assessment of the anti-angiogenic effects of liposomal glucocorticoids

Inflammation plays a prominent role in tumor growth. Anti-inflammatory drugs have therefore been proposed as anti-cancer therapeutics. In this study, we determined the anti-angiogenic activity of a single dose of liposomal prednisolone phosphate (PLP-L), by monitoring tumor vascular function and viability over a period of one week. C57BL/6 mice were inoculated subcutaneously with B16F10 melanoma cells. Six animals were PLP-L-treated and six served as control. Tumor tissue and vascular function were probed using MRI before and at three timepoints after treatment. DCE-MRI was used to determine K(trans), v(e), time-to-peak, initial slope and the fraction of non-enhancing pixels, complemented with immunohistochemistry. The apparent diffusion coefficient (ADC), T(2) and tumor size were assessed with MRI as well. PLP-L treatment resulted in smaller tumors and caused a significant drop in K(trans) 48 h post-treatment, which was maintained until one week after drug administration. However, this effect was not sufficient to significantly distinguish treated from non-treated animals. The therapy did not affect tumor tissue viability but did prevent the ADC decrease observed in the control group. No evidence for PLP-L-induced tumor vessel normalization was found on histology. Treatment with PLP-L altered tumor vascular function. This effect did not fully explain the tumor growth inhibition, suggesting a broader spectrum of PLP-L activities.

Temporal resolution and SNR requirements for accurate DCE-MRI data analysis using the AATH model

Dynamic contrast-enhanced MRI has been used in conjunction with tracer kinetics modeling in a wide range of tissues for treatment monitoring, oncology drug development, and investigation of disease processes. Accurate measurement of model parameters relies on acquiring data with high temporal resolution and low noise, particularly for models with large numbers of free parameters, such as the adiabatic approximation to the tissue homogeneity model for separate measurements of blood flow and vessel permeability. In this simulation study, accuracy of the adiabatic approximation to the tissue homogeneity model was investigated, examining the effects of temporal resolution, noise levels, and error in the measured arterial input function. A temporal resolution of 1.5 s and high SNR (noise sd = 0.05) were found to ensure minimal bias (<5%) in all four model parameters (extraction fraction, blood flow, mean transit time, and extravascular extracellular volume), and the sampling interval can be relaxed to 6 s, if the transit time need not be measured accurately (bias becomes >10%). A 10% error in the measured height of the arterial input function first pass peak resulted in an error of at most 10% in each model parameter.

Development and evaluation of a novel method for preclinical measurement of tissue vascular volume

Identification of clinically predictive models of disposition kinetics for antibody therapeutics is an ongoing pursuit in drug development. To encourage translation of drug candidates from early research to clinical trials, clinical diagnostic agents may be used to characterize antibody disposition in physiologically relevant preclinical models. TechneScan PYP was employed to measure tissue vascular volumes (V(v)) in healthy mice. Two methods of red blood cell (RBC) labeling were compared: a direct in vivo method that is analogous to a clinical blood pool imaging protocol, and an indirect method in which radiolabeled blood was transfused from donor mice into recipient mice. The indirect method gave higher precision in RBC labeling yields, lower V(v) values in most tissues, and lower (99m)Tc uptake in kidneys and bladder by single photon emission computed tomographic (SPECT) imaging relative to the direct method. Furthermore, the relative influence of each method on the calculated area under the first 7 days of the concentration-time curve (AUC(0-7)) of an IgG in nude mice was assessed using a physiologically based pharmacokinetic model. The model was sensitive to the source of V(v) values, whether obtained from the literature or measured by either method, when used to predict experimental AUC(0-7) values for radiolabeled trastuzumab in healthy murine tissues. In summary, a novel indirect method for preclinical determination of V(v) offered higher precision in RBC labeling efficiency and lower renal uptake of (99m)Tc than the direct method. In addition, these observations emphasize the importance of obtaining accurate physiological parameter values for modeling antibody uptake.

Vascular permeability modulation at the cell, microvessel, or whole organ level: towards closing gaps in our knowledge

Multiple processes modulate net blood-to-tissue exchange in a microvascular unit in normal and pathophysiological conditions. These include mechanisms that control the number and type of microvessels perfused, the balance of adhesion and contractile forces that determine the conductance of the spaces between endothelial cells to water and solutes, the pressure and chemical potential gradients determining the driving forces through these conductive pathways, and the organization of barriers to macromolecules in the endothelial glycocalyx. Powerful methods are available to investigate these mechanisms at the levels of cultured endothelial monolayers, isolated microvessels, and the microvascular units within intact organs. Here we focus on current problems that limit the integration of our knowledge of mechanisms investigated in detail at the cellular level into a more complete understanding of modulation of blood-to-tissue exchange in whole organs when the endothelial barrier is exposed to acute and more long-term inflammatory conditions. First, we review updated methods, applicable in mouse models of vascular permeability regulation, to investigate both acute and long-term changes in permeability. Methods to distinguish tracer accumulation due to change in perfusion from real increases in extravascular accumulation are emphasized. The second part of the review compares normal and increased permeability in individually perfused venular microvessels and endothelial cell monolayers. The heterogeneity of endothelial cell phenotypes in the baseline state and after exposure to injury and inflammatory conditions is emphasized. Lastly, we review new approaches to investigation of the glycocalyx barrier properties in cultured endothelial monolayers and in whole-body investigations.

A novel estimation method for physiological parameters in dynamic contrast-enhanced MRI: application of a distributed parameter model using Fourier-domain calculations

Dynamic contrast-enhanced magnetic resonance imaging (MRI) is a promising tool in the evaluation of tumor physiology. From rapidly acquired images and a model for contrast agent pharmacokinetics, physiological parameters are derived. One pharmacokinetic model, the tissue homogeneity model, enables estimation of both blood flow and vessel permeability together with parameters that describe blood volume and extracellular extravascular volume fraction. However, studies have shown that parameter estimation with this model is unstable. Therefore, several initial guesses are needed for accurate estimates, which makes the estimation slow. In this study a new estimation algorithm for the tissue homogeneity model, based on Fourier domain calculations, was derived and implemented as a Matlab program. The algorithm was tested with Monte-Carlo simulations and the results were compared to an existing method that uses the adiabatic approximation. The algorithm was also tested on data from a metastasis in the brain. The comparison showed that the new algorithm gave more accurate results on the 2.5th and 97.5th percentile levels, for instance the error in blood volume was reduced by 21%. In addition, the time needed for the computations was reduced with a factor 25. It was concluded that the new algorithm can be used to speed up parameter estimation while accuracy can be gained at the same time.

High-resolution longitudinal assessment of flow and permeability in mouse glioma vasculature: Sequential small molecule and SPIO dynamic contrast agent MRI

The poor prognosis associated with malignant glioma is largely attributable to its invasiveness and robust angiogenesis. Angiogenesis involves host-tumor interaction and requires in vivo evaluation. Despite their versatility, few studies have used mouse glioma models with perfusion MRI approaches, and generally lack longitudinal study design. Using a micro-MRI system (8.5 Tesla), a novel dual bolus-tracking perfusion MRI strategy was implemented. Using the small molecule contrast agent Magnevist, dynamic contrast enhanced MRI was implemented in the intracranial 4C8 mouse glioma model to determine K(trans) and v(e), indices of tumor vascular permeability and cellularity, respectively. Dynamic susceptibility contrast MRI was subsequently implemented to assess both cerebral blood flow and volume, using the macromolecular superparamagnetic iron oxide, Feridex, which circumvented tumor bolus susceptibility curve distortions from first-pass extravasation. The high-resolution parametric maps obtained over 4 weeks, indicated a progression of tumor vascularization, permeability, and decreased cellularity with tumor growth. In conclusion, a comprehensive array of key parameters were reliably quantified in a longitudinal mouse glioma study. The syngeneic 4C8 intracerebral mouse tumor model has excellent characteristics for studies of glioma angiogenesis. This approach provides a useful platform for noninvasive and highly diagnostic longitudinal investigations of anti-angiogenesis strategies in a relevant orthotopic animal model.

Quantitative analysis of dynamic contrast-enhanced MR images based on Bayesian P-splines

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an important tool for detecting subtle kinetic changes in cancerous tissue. Quantitative analysis of DCE-MRI typically involves the convolution of an arterial input function (AIF) with a nonlinear pharmacokinetic model of the contrast agent concentration. Parameters of the kinetic model are biologically meaningful, but the optimization of the nonlinear model has significant computational issues. In practice, convergence of the optimization algorithm is not guaranteed and the accuracy of the model fitting may be compromised. To overcome these problems, this paper proposes a semi-parametric penalized spline smoothing approach, where the AIF is convolved with a set of B-splines to produce a design matrix using locally adaptive smoothing parameters based on Bayesian penalized spline models (P-splines). It has been shown that kinetic parameter estimation can be obtained from the resulting deconvolved response function, which also includes the onset of contrast enhancement. Detailed validation of the method, both with simulated and in vivo data, is provided.