Imaging acute effects of bevacizumab on tumor vascular kinetics in a preclinical orthotopic model of U251 glioma.

The effect of a human vascular endothelial growth factor antibody on the vasculature of human tumor grown in rat brain was studied. Using dynamic contrast-enhanced magnetic resonance imaging, the effects of intravenous bevacizumab (Avastin; 10 mg/kg) were examined before and at postadministration times of 1, 2, 4, 8, 12 and 24 h (N = 26; 4-5 per time point) in a rat model of orthotopic, U251 glioblastoma (GBM). The commonly estimated vascular parameters for an MR contrast agent were: (i) plasma distribution volume (vp ), (ii) forward volumetric transfer constant (Ktrans ) and (iii) reverse transfer constant (kep ). In addition, extracellular distribution volume (VD ) was estimated in the tumor (VD-tumor ), tumor edge (VD-edge ) and the mostly normal tumor periphery (VD-peri ), along with tumor blood flow (TBF), peri-tumoral hydraulic conductivity (K) and interstitial flow (Flux) and tumor interstitial fluid pressure (TIFP). Studied as % changes from baseline, the 2-h post-treatment time point began showing significant decreases in vp , VD-tumor, VD-edge and VD-peri , as well as K, with these changes persisting at 4 and 8 h in vp , K, VD-tumor, -edge and -peri (t-tests; p < 0.05-0.01). Decreases in Ktrans were observed at the 2- and 4-h time points (p < 0.05), while interstitial volume fraction (ve ; = Ktrans /kep ) showed a significant decrease only at the 2-h time point (p < 0.05). Sustained decreases in Flux were observed from 2 to 24 h (p < 0.01) while TBF and TIFP showed delayed responses, increases in the former at 12 and 24 h and a decrease in the latter only at 12 h. These imaging biomarkers of tumor vascular kinetics describe the short-term temporal changes in physical spaces and fluid flows in a model of GBM after Avastin administration.

Adaptation of laser interstitial thermal therapy for tumor ablation under MRI monitoring in a rat orthotopic model of glioblastoma.

BACKGROUND: Laser interstitial thermal therapy (LITT) under magnetic resonance imaging (MRI) monitoring is being increasingly used in cytoreductive surgery of recurrent brain tumors and tumors located in eloquent brain areas. The objective of this study was to adapt this technique to an animal glioma model. METHODS: A rat model of U251 glioblastoma (GBM) was employed. Tumor location and extent were determined by MRI and dynamic contrast-enhanced (DCE) MRI. A day after assessing tumor appearance, tumors were ablated during diffusion-weighted imaging (DWI)-MRI using a Visualase LITT system (n = 5). Brain images were obtained immediately after ablation and again at 24 h post-ablation to confirm the efficacy of tumor cytoablation. Untreated tumors served as controls (n = 3). Rats were injected with fluorescent isothiocyanate (FITC) dextran and Evans blue that circulated for 10 min after post-LITT MRI. The brains were then removed for fluorescence microscopy and histopathology evaluations using hematoxylin and eosin (H&E) and major histocompatibility complex (MHC) staining. RESULTS: All rats showed a space-occupying tumor with T2 and T1 contrast-enhancement at pre-LITT imaging. The rats that underwent the LITT procedure showed a well-demarcated ablation zone with near-complete ablation of tumor tissue and with peri-ablation contrast enhancement at 24 h post-ablation. Tumor cytoreduction by ablation as seen on MRI was confirmed by H&E and MHC staining. CONCLUSIONS: Data showed that tumor cytoablation using MRI-monitored LITT was possible in preclinical glioma models. Real-time MRI monitoring facilitated visualizing and controlling the area of ablation as it is otherwise performed in clinical applications.

Longitudinal evaluation of tumor microenvironment in rat focal brainstem glioma using diffusion and perfusion MRI.

BACKGROUND: Brainstem gliomas are aggressive and difficult to treat. Growth of these tumors may be characterized with MRI methods. PURPOSE: To visualize longitudinal changes in tumor volume, vascular leakiness, and tissue microstructure in an animal model of brainstem glioma. STUDY TYPE: Prospective animal model. ANIMAL MODEL: Male Sprague-Dawley rats (n = 9) were imaged with 9L gliosarcoma cells infused into the pontine reticular formation of the brainstem. The MRI tumor microenvironment was studied at 3 and 10 days postimplantation of tumor cells. FIELD STRENGTH/SEQUENCE: Diffusion tensor imaging (DTI) and dynamic contrast-enhanced (DCE)-MRI were performed at 4.7T using spin-echo multislice echo planar imaging and gradient echo multislice imaging, respectively. ASSESSMENT: Tumor leakiness was assessed by the forward volumetric transfer constant, Ktrans , estimated from DCE-MRI data. Tumor structure was evaluated with fractional anisotropy (FA) obtained from DTI. Tumor volumes, delineated by a T1 map, T2 -weighted image, FA, and DCE signal enhancement were compared. STATISTICAL TESTS: Changes in the assessed parameters within and across the groups (ie, rats 3 and 10 days post tumor cell implantation) were evaluated with Wilcoxon rank-sum tests. RESULTS: Day 3 tumors were visible mainly on contrast-enhanced images, while day 10 tumors were visible in both contrast-enhanced and diffusion-weighted images. Mean Ktrans at day 10 was 41% lower than at day 3 (P = 0.23). In day 10 tumors, FA was regionally lower in the tumor compared to normal tissue (P = 0.0004), and tumor volume, segmented based on FA map, was significantly smaller (P ≤ 0.05) than that obtained from other contrasts. DATA CONCLUSION: Contrast-enhanced MRI was found to be more sensitive in detecting early-stage tumor boundaries than other contrasts. Areas of the tumor outlined by DCE-MRI and DTI were significantly different. Over the observed period of tumor growth, average vessel leakiness decreased with tumor progression. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:1322-1332.

Toward a noninvasive estimate of interstitial fluid pressure by dynamic contrast-enhanced MRI in a rat model of cerebral tumor.

PURPOSE: This study demonstrates a DCE-MRI estimate of tumor interstitial fluid pressure (TIFP) and hydraulic conductivity in a rat model of glioblastoma, with validation against an invasive wick-in-needle (WIN) technique. An elevated TIFP is considered a mark of aggressiveness, and a decreased TIFP a predictor of response to therapy. METHODS: The DCE-MRI studies were conducted in 36 athymic rats (controls and posttreatment animals) with implanted U251 cerebral tumors, and with TIFP measured using a WIN method. Using a model selection paradigm and a novel application of Patlak and Logan plots to DCE-MRI data, the MRI parameters required for estimating TIFP noninvasively were estimated. Two models, a fluid-mechanical model and a multivariate empirical model, were used for estimating TIFP, as verified against WIN-TIFP. RESULTS: Using DCE-MRI, the mean estimated hydraulic conductivity (MRI-K) in U251 tumors was (2.3 ± 3.1) × 10-5 (mm2 /mmHg-s) in control studies. Significant positive correlations were found between WIN-TIFP and MRI-TIFP in both mechanical and empirical models. For instance, in the control group of the fluid-mechanical model, MRI-TIFP was a strong predictor of WIN-TIFP (R2 = 0.76, p < .0001). A similar result was found in the bevacizumab-treated group of the empirical model (R2 = 0.93, p = .014). CONCLUSION: This research suggests that MRI dynamic studies contain enough information to noninvasively estimate TIFP in this, and possibly other, tumor models, and thus might be used to assess tumor aggressiveness and response to therapy.

An adaptive model for rapid and direct estimation of extravascular extracellular space in dynamic contrast enhanced MRI studies.

Extravascular extracellular space (ve ) is a key parameter to characterize the tissue of cerebral tumors. This study introduces an artificial neural network (ANN) as a fast, direct, and accurate estimator of ve from a time trace of the longitudinal relaxation rate, ΔR1 (R1  = 1/T1 ), in DCE-MRI studies. Using the extended Tofts equation, a set of ΔR1 profiles was simulated in the presence of eight different signal to noise ratios. A set of gain- and noise-insensitive features was generated from the simulated ΔR1 profiles and used as the ANN training set. A K-fold cross-validation method was employed for training, testing, and optimization of the ANN. The performance of the optimal ANN (12:6:1, 12 features as input vector, six neurons in hidden layer, and one output) in estimating ve at a resolution of 10% (error of ±5%) was 82%. The ANN was applied on DCE-MRI data of 26 glioblastoma patients to estimate ve in tumor regions. Its results were compared with the maximum likelihood estimation (MLE) of ve . The two techniques showed a strong agreement (r = 0.82, p < 0.0001). Results implied that the perfected ANN was less sensitive to noise and outperformed the MLE method in estimation of ve .

An extended vascular model for less biased estimation of permeability parameters in DCE-T1 images.

One of the key elements in dynamic contrast enhanced (DCE) image analysis is the arterial input function (AIF). Traditionally, in DCE studies a global AIF sampled from a major artery or vein is used to estimate the vascular permeability parameters; however, not addressing dispersion and delay of the AIF at the tissue level can lead to biased estimates of these parameters. To find less biased estimates of vascular permeability parameters, a vascular model of the cerebral vascular system is proposed that considers effects of dispersion of the AIF in the vessel branches, as well as extravasation of the contrast agent (CA) to the extravascular-extracellular space. Profiles of the CA concentration were simulated for different branching levels of the vascular structure, combined with the effects of vascular leakage. To estimate the permeability parameters, the extended model was applied to these simulated signals and also to DCE-T1 (dynamic contrast enhanced T1 ) images of patients with glioblastoma multiforme tumors. The simulation study showed that, compared with the case of solving the pharmacokinetic equation with a global AIF, using the local AIF that is corrected by the vascular model can give less biased estimates of the permeability parameters (Ktrans , vp and Kb ). Applying the extended model to signals sampled from different areas of the DCE-T1 image showed that it is able to explain the CA concentration profile in both the normal areas and the tumor area, where effects of vascular leakage exist. Differences in the values of the permeability parameters estimated in these images using the local and global AIFs followed the same trend as the simulation study. These results demonstrate that the vascular model can be a useful tool for obtaining more accurate estimation of parameters in DCE studies.

A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level.

In this paper, we introduce a novel model of the brain vascular system, which is developed based on laws of fluid dynamics and vascular morphology. This model is used to address dispersion and delay of the arterial input function (AIF) at different levels of the vascular structure and to estimate the local AIF in DCE images. We developed a method based on the simplex algorithm and Akaike information criterion to estimate the likelihood of the contrast agent concentration signal sampled in DCE images belonging to different layers of the vascular tree or being a combination of different signal levels from different nodes of this structure. To evaluate this method, we tested the method on simulated local AIF signals at different levels of this structure. Even down to a signal to noise ratio of 5.5 our method was able to accurately detect the branching level of the simulated signals. When two signals with the same power level were combined, our method was able to separate the base signals of the composite AIF at the 50% threshold. We applied this method to dynamic contrast enhanced computed tomography (DCE-CT) data, and using the parameters estimated by our method we created an arrival time map of the brain. Our model corrected AIF can be used for solving the pharmacokinetic equations for more accurate estimation of vascular permeability parameters in DCE imaging studies.

Peritumoral tissue compression is predictive of exudate flux in a rat model of cerebral tumor: an MRI study in an embedded tumor.

MRI estimates of extracellular volume and tumor exudate flux in peritumoral tissue are demonstrated in an experimental model of cerebral tumor. Peritumoral extracellular volume predicted the tumor exudate flux. Eighteen RNU athymic rats were inoculated intracerebrally with U251MG tumor cells and studied with dynamic contrast enhanced MRI (DCE-MRI) approximately 18 days post implantation. Using a model selection paradigm and a novel application of Patlak and Logan plots to DCE-MRI data, the distribution volume (i.e. tissue porosity) in the leaky rim of the tumor and that in the tissue external to the rim (the outer rim) were estimated, as was the tumor exudate flow from the inner rim of the tumor through the outer rim. Distribution volume in the outer rim was approximately half that of the inner adjacent region (p < 1 × 10(-4)). The distribution volume of the outer ring was significantly correlated (R(2) = 0.9) with tumor exudate flow from the inner rim. Thus, peritumoral extracellular volume predicted the rate of tumor exudate flux. One explanation for these data is that perfusion, i.e. the delivery of blood to the tumor, was regulated by the compression of the mostly normal tissue of the tumor rim, and that the tumor exudate flow was limited by tumor perfusion.

Dynamic contrast enhanced MRI parameters and tumor cellularity in a rat model of cerebral glioma at 7 T.

PURPOSE: To test the hypothesis that a noninvasive dynamic contrast enhanced MRI (DCE-MRI) derived interstitial volume fraction (ve ) and/or distribution volume (VD ) were correlated with tumor cellularity in cerebral tumor. METHODS: T1 -weighted DCE-MRI studies were performed in 18 athymic rats implanted with U251 xenografts. After DCE-MRI, sectioned brain tissues were stained with Hematoxylin and Eosin for cell counting. Using a Standard Model analysis and Logan graphical plot, DCE-MRI image sets during and after the injection of a gadolinium contrast agent were used to estimate the parameters plasma volume (vp ), forward transfer constant (K(trans) ), ve , and VD . RESULTS: Parameter values in regions where the standard model was selected as the best model were: (mean ± S.D.): vp = (0.81 ± 0.40)%, K(trans) = (2.09 ± 0.65) × 10(-2) min(-1) , ve = (6.65 ± 1.86)%, and VD = (7.21 ± 1.98)%. The Logan-estimated VD was strongly correlated with the standard model's vp + ve (r = 0.91, P < 0.001). The parameters, ve and/or VD , were significantly correlated with tumor cellularity (r ≥ -0.75, P < 0.001 for both). CONCLUSION: These data suggest that tumor cellularity can be estimated noninvasively by DCE-MRI, thus supporting its utility in assessing tumor pathophysiology.

Intratumor distribution and test-retest comparisons of physiological parameters quantified by dynamic contrast-enhanced MRI in rat U251 glioma.

The distribution of dynamic contrast-enhanced MRI (DCE-MRI) parametric estimates in a rat U251 glioma model was analyzed. Using Magnevist as contrast agent (CA), 17 nude rats implanted with U251 cerebral glioma were studied by DCE-MRI twice in a 24 h interval. A data-driven analysis selected one of three models to estimate either (1) plasma volume (vp), (2) vp and forward volume transfer constant (K(trans)) or (3) vp, K(trans) and interstitial volume fraction (ve), constituting Models 1, 2 and 3, respectively. CA distribution volume (VD) was estimated in Model 3 regions by Logan plots. Regions of interest (ROIs) were selected by model. In the Model 3 ROI, descriptors of parameter distributions--mean, median, variance and skewness--were calculated and compared between the two time points for repeatability. All distributions of parametric estimates in Model 3 ROIs were positively skewed. Test-retest differences between population summaries for any parameter were not significant (p ≥ 0.10; Wilcoxon signed-rank and paired t tests). These and similar measures of parametric distribution and test-retest variance from other tumor models can be used to inform the choice of biomarkers that best summarize tumor status and treatment effects.

Measurement of rat brain tumor kinetics using an intravascular MR contrast agent and DCE-MRI nested model selection.

PURPOSE: Using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in a rat glioma model, and nested model selection (NMS), to compare estimates of the pharmacokinetic parameters vp , K(trans) , and ve for two different contrast agents (CAs)-gadofosveset, which reversibly binds to human serum albumin, and gadopentetate dimeglumine, which does not. MATERIALS AND METHODS: DCE-MRI studies were performed on nine Fisher 344 rats inoculated intracerebrally with 9L gliosarcoma cells using both gadofosveset and gadopentetate. The parameters vp , K(trans) , and ve were estimated using NMS. RESULTS: K(trans) estimates using gadofosveset, compared to gadopentetate, differed in their means (gadofosveset 0.025 ± 0.008 min(-1) vs. gadopentetate 0.046 ± 0.011 min(-1) ; P = 0.0039). This difference notwithstanding, the intraclass correlation coefficient (ICC) for the two estimates of K(trans) showed nearly perfect linear dependence (ICC = 0.8479 by Pearson's r). Other estimates, ve (gadofosveset 22.7 ± 4.7% vs. gadopentetate 23.6 ± 5.6%; P = 0.4258) and vp (gadofosveset 1.5 ± 0.5% vs. gadopentetate 1.6 ± 0.4%; P = 0.25), were not different in their means between the two CAs, and there was almost perfect agreement for ve (ICC = 0.8798) and substantial agreement for vp (ICC = 0.7981) between the two CAs. CONCLUSION: Estimates of K(trans) were statistically different using gadofosveset and gadopentetate, whereas ve and vp were similar with two CAs. NMS produced robust estimates of pharmacokinetic parameters using DCE-MRI that show promise as important measures of tumor physiology and microenvironment.

Probabilistic Nested Model Selection in Pharmacokinetic Analysis of DCE-MRI Data in Animal Model of Cerebral Tumor.

Purpose: Best current practice in the analysis of dynamic contrast enhanced (DCE)-MRI is to employ a voxel-by-voxel model selection from a hierarchy of nested models. This nested model selection (NMS) assumes that the observed time-trace of contrast-agent (CA) concentration within a voxel, corresponds to a singular physiologically nested model. However, admixtures of different models may exist within a voxel's CA time-trace. This study introduces an unsupervised feature engineering technique (Kohonen-Self-Organizing-Map (K-SOM)) to estimate the voxel-wise probability of each nested model. Methods: Sixty-six immune-compromised-RNU rats were implanted with human U-251N cancer cells, and DCE-MRI data were acquired from all the rat brains. The time-trace of change in the longitudinalrelaxivity Δ R 1 for all animals' brain voxels was calculated. DCE-MRI pharmacokinetic (PK) analysis was performed using NMS to estimate three model regions: Model-1: normal vasculature without leakage, Model-2: tumor tissues with leakage without back-flux to the vasculature, Model-3: tumor vessels with leakage and back-flux. Approximately two hundred thirty thousand (229,314) normalized Δ R 1 profiles of animals' brain voxels along with their NMS results were used to build a K-SOM (topology-size: 8×8, with competitive-learning algorithm) and probability map of each model. K-fold nested-cross-validation (NCV, k=10) was used to evaluate the performance of the K-SOM probabilistic-NMS (PNMS) technique against the NMS technique. Results: The K-SOM PNMS's estimation for the leaky tumor regions were strongly similar (Dice-Similarity-Coefficient, DSC=0.774 [CI: 0.731-0.823], and 0.866 [CI: 0.828-0.912] for Models 2 and 3, respectively) to their respective NMS regions. The mean-percent-differences (MPDs, NCV, k=10) for the estimated permeability parameters by the two techniques were: -28%, +18%, and +24%, for v p , K trans , and v e , respectively. The KSOM-PNMS technique produced microvasculature parameters and NMS regions less impacted by the arterial-input-function dispersion effect. Conclusion: This study introduces an unsupervised model-averaging technique (K-SOM) to estimate the contribution of different nested-models in PK analysis and provides a faster estimate of permeability parameters.

Model selection in measures of vascular parameters using dynamic contrast-enhanced MRI: experimental and clinical applications.

A review of the selection of models in dynamic contrast-enhanced MRI (DCE-MRI) is conducted, with emphasis on the balance between the bias and variance required to produce stable and accurate estimates of vascular parameters. The vascular parameters considered as a first-order model are the forward volume transfer constant K(trans) , the plasma volume fraction vp and the interstitial volume fraction ve . To illustrate the critical issues in model selection, a data-driven selection of models in an animal model of cerebral glioma is followed. Systematic errors and extended models are considered. Studies with nested and non-nested pharmacokinetic models are reviewed; models considering water exchange are considered.

Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI.

Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs' feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.

Persistent Peri-Ablation Blood-Brain Barrier Opening After Laser Interstitial Thermal Therapy for Brain Tumors.

Purpose Laser interstitial thermal therapy (LITT) is a minimally invasive, image-guided, cytoreductive procedure to treat recurrent glioblastoma. This study implemented dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) methods and employed a model selection paradigm to localize and quantify post-LITT blood-brain barrier (BBB) permeability in the ablation vicinity. Serum levels of neuron-specific enolase (NSE), a peripheral marker of increased BBB permeability, were measured. Methods Seventeen patients were enrolled in the study. Using an enzyme-linked immunosorbent assay, serum NSE was measured preoperatively, 24 hours postoperatively, and at two, eight, 12, and 16 weeks postoperatively, depending on postoperative adjuvant treatment. Of the 17 patients, four had longitudinal DCE-MRI data available, from which blood-to-brain forward volumetric transfer constant (Ktrans) data were assessed. Imaging was performed preoperatively, 24 hours postoperatively, and between two and eight weeks postoperatively. Results Serum NSE increased at 24 hours following ablation (p=0.04), peaked at two weeks, and returned to baseline by eight weeks postoperatively. Ktrans was found to be elevated in the peri-ablation periphery 24 hours after the procedure. This increase persisted for two weeks. Conclusion Following the LITT procedure, serum NSE levels and peri-ablation Ktrans estimated from DCE-MRI demonstrated increases during the first two weeks after ablation, suggesting transiently increased BBB permeability.

Characterization of the Response of 9L and U-251N Orthotopic Brain Tumors to 3D Conformal Radiation Therapy.

In a study employing MRI-guided stereotactic radiotherapy (SRS) in two orthotopic rodent brain tumor models, the radiation dose yielding 50% survival (the TCD50) was sought. Syngeneic 9L cells, or human U-251N cells, were implanted stereotactically in 136 Fischer 344 rats or 98 RNU athymic rats, respectively. At approximately 7 days after implantation for 9L, and 18 days for U-251N, rats were imaged with contrast-enhanced MRI (CE-MRI) and then irradiated using a Small Animal Radiation Research Platform (SARRP) operating at 220 kV and 13 mA with an effective energy of ∼70 keV and dose rate of ∼2.5 Gy per min. Radiation doses were delivered as single fractions. Cone-beam CT images were acquired before irradiation, and tumor volumes were defined using co-registered CE-MRI images. Treatment planning using MuriPlan software defined four non-coplanar arcs with an identical isocenter, subsequently accomplished by the SARRP. Thus, the treatment workflow emulated that of current clinical practice. The study endpoint was animal survival to 200 days. The TCD50 inferred from Kaplan-Meier survival estimation was approximately 25 Gy for 9L tumors and below 20 Gy, but within the 95% confidence interval in U-251N tumors. Cox proportional-hazards modeling did not suggest an effect of sex, with the caveat of wide confidence intervals. Having identified the radiation dose at which approximately half of a group of animals was cured, the biological parameters that accompany radiation response can be examined.

Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models.

We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, Ktrans, plasma volume fraction, vp, and extravascular, extracellular space, ve, directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, vp, Ktrans, and ve, respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches.

Model selection for DCE-T1 studies in glioblastoma.

Dynamic contrast enhanced T(1)-weighted MRI using the contrast agent gadopentetate dimeglumine (Gd-DTPA) was performed on 10 patients with glioblastoma. Nested models with as many as three parameters were used to estimate plasma volume or plasma volume and forward vascular transfer constant (K(trans)) and the reverse vascular transfer constant (k(ep)). These constituted models 1, 2, and 3, respectively. Model 1 predominated in normal nonleaky brain tissue, showing little or no leakage of contrast agent. Model 3 predominated in regions associated with aggressive portions of the tumor, and model 2 bordered model 3 regions, showing leakage at reduced rates. In the patient sample, v(p) was about four times that of white matter in the enhancing part of the tumor. K(trans) varied by a factor of 10 between the model 2 (1.9 ↔ 10(-3) min(-1)) and model 3 regions (1.9 ↔ 10(-2) min(-1)). The mean calculated interstitial space (model 3) was 5.5%. In model 3 regions, excellent curve fits were obtained to summarize concentration-time data (mean R(2) = 0.99). We conclude that the three parameters of the standard model are sufficient to fit dynamic contrast enhanced T(1) data in glioblastoma under the conditions of the experiment.

MRI estimation of gadolinium and albumin effects on water proton.

The longitudinal relaxivity on the protons of water of a Gd-chelate-albumin compound was measured at 7 T as a function of the macromolecular content of a cross-linked matrix. In agreement with previous works, the results demonstrate that the effect of gadolinium on water proton relaxivity is not constant, rising moderately with increase in the concentration of bovine serum albumin (BSA). About 35% variation in relaxivity was observed over a 0%-25% range of BSA concentrations (ℜ = 3.893 + 0.0502 × BSA [%], SE = 0.0119 and 0.1740, t = 4.215 and 22.383, p < 0.014 and 0.001).

Modeling of Look-Locker estimates of the magnetic resonance imaging estimate of longitudinal relaxation rate in tissue after contrast administration.

This paper models the behavior of the longitudinal relaxation rate of the protons of tissue water R(1) (R(1) = 1/T(1) ), measured in a Look-Locker experiment at 7 Tesla after administration of a paramagnetic contrast agent (CA). It solves the Bloch-McConnell equations for the longitudinal magnetization of the protons of water in a three-site two-exchange (3S2X) model with boundary conditions appropriate to repeated sampling of magnetization. The extent to which equilibrium intercompartmental water exchange kinetics affect monoexponential estimates of R(1) after administration of a CA in dynamic contrast enhanced experiment is described. The relation between R(1) and tissue CA concentration was calculated for CA restricted to the intravascular, or to the intravascular and extracellular compartments, by varying model parameters to mimic experimental data acquired in a rat model of cerebral tumor. The model described a nearly linear relationship between R(1) and tissue concentration of CA, but demonstrated that the apparent longitudinal relaxivity of CA depends upon tissue type. The practical consequence of this finding is that the extended Patlak plot linearizes the ΔR(1) data in tissue with leaky microvessels, accurately determines the influx rate of the CA across these microvessels, but underestimates the volume of intravascular blood water.