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

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.

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.

A computational model of glioma reveals opposing, stiffness-sensitive effects of leaky vasculature and tumor growth on tissue mechanical stress and porosity

A biphasic computational model of a growing, vascularized glioma within brain tissue was developed to account for unique features of gliomas, including soft surrounding brain tissue, their low stiffness relative to brain tissue, and a lack of draining lymphatics. This model is the first to couple nonlinear tissue deformation with porosity and tissue hydraulic conductivity to study the mechanical interaction of leaky vasculature and solid growth in an embedded glioma. The present model showed that leaky vasculature and elevated interstitial fluid pressure produce tensile stress within the tumor in opposition to the compressive stress produced by tumor growth. This tensile effect was more pronounced in softer tissue and resulted in a compressive stress concentration at the tumor rim that increased when tumor was softer than host. Aside from generating solid stress, fluid pressure-driven tissue deformation decreased the effective stiffness of the tumor while growth increased it, potentially leading to elevated stiffness in the tumor rim. A novel prediction of reduced porosity at the tumor rim was corroborated by direct comparison with estimates from our in vivo imaging studies. Antiangiogenic and radiation therapy were simulated by varying vascular leakiness and tissue hydraulic conductivity. These led to greater solid compression and interstitial pressure in the tumor, respectively, the former of which may promote tumor infiltration of the host. Our findings suggest that vascular leakiness has an important influence on in vivo solid stress, stiffness, and porosity fields in gliomas given their unique mechanical microenvironment.

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 (v_p ), (ii) forward volumetric transfer constant (K^trans ) and (iii) reverse transfer constant (k_ep ). In addition, extracellular distribution volume (V_D ) was estimated in the tumor (V_D-tumor ), tumor edge (V_D-edge ) and the mostly normal tumor periphery (V_D-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 v_p , V_D-tumor, V_D-edge and V_D-peri , as well as K, with these changes persisting at 4 and 8 h in v_p , K, V_{D-tumor, -edge} and _-peri (t-tests; p < 0.05-0.01). Decreases in K^trans were observed at the 2- and 4-h time points (p < 0.05), while interstitial volume fraction (v_e ; = K^trans /k_ep ) 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.

Computational Modeling of Interstitial Fluid Pressure and Velocity in Head and Neck Cancer Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging: Feasibility Analysis

We developed and tested the feasibility of computational fluid modeling (CFM) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for quantitative estimation of interstitial fluid pressure (IFP) and velocity (IFV) in patients with head and neck (HN) cancer with locoregional lymph node metastases. Twenty-two patients with HN cancer, with 38 lymph nodes, underwent pretreatment standard MRI, including DCE-MRI, on a 3-Tesla scanner. CFM simulation was performed with the finite element method in COMSOL Multiphysics software. The model consisted of a partial differential equation (PDE) module to generate 3D parametric IFP and IFV maps, using the Darcy equation and Ktrans values (min⁻¹, estimated from the extended Tofts model) to reflect fluid influx into tissue from the capillary microvasculature. The Spearman correlation (ρ) was calculated between total tumor volumes and CFM estimates of mean tumor IFP and IFV. CFM-estimated tumor IFP and IFV mean ± standard deviation for the neck nodal metastases were 1.73 ± 0.39 (kPa) and 1.82 ± 0.9 × (10⁻⁷ m/s), respectively. High IFP estimates corresponds to very low IFV throughout the tumor core, but IFV rises rapidly near the tumor boundary where the drop in IFP is precipitous. A significant correlation was found between pretreatment total tumor volume and CFM estimates of mean tumor IFP (ρ = 0.50, P = 0.004). Future studies can validate these initial findings in larger patients with HN cancer cohorts using CFM of the tumor in concert with DCE characterization, which holds promise in radiation oncology and drug-therapy clinical trials.

Measurements of cerebral blood volume using quantitative susceptibility mapping, R2 * relaxometry, and ferumoxytol-enhanced MRI

Ferumoxytol-enhanced MRI holds potential for the non-invasive assessment of vascular architecture using estimates of cerebral blood volume (CBV). Ferumoxytol specifically enables steady-state imaging with extended acquisition times, for substantial improvements in resolution and contrast-to-noise ratio. With such data, quantitative susceptibility mapping (QSM) can be used to obtain images of local tissue magnetic susceptibility and hence estimate the increase in blood susceptibility after administration of a contrast agent, which in turn can be correlated to tissue CBV. Here, we explore the use of QSM for CBV estimation and compare it with R₂ * (1/T₂ *)-based results. Institutional review board approval was obtained, and all subjects provided written informed consent. For this prospective study, MR images were acquired on a 3.0 T scanner in 19 healthy subjects using a multiple-echo T₂ *-weighted sequence. Scanning was performed before and after the administration of two doses of ferumoxytol (1 mg FE/kg and 4 mg FE/kg). Different QSM approaches were tested on numerical phantom simulations. Results showed that the accuracy of magnetic susceptibility measurements improved with increasing image resolution and decreasing vascular density. In vivo changes in magnetic susceptibility were measured after the administration of ferumoxytol utilizing QSM, and significantly higher QSM-based CBV was measured in gray matter compared with white matter. QSM- and R₂ *-based CBV estimates correlated well, with similar average values, but a larger variance was found in QSM-based estimates.

R2* Relaxation Affects Pharmacokinetic Analysis of Dynamic Contrast-Enhanced MRI in Cancer and Underestimates Treatment Response at 7 T

Effective transverse relaxivity of gadolinium-based contrast agents is often neglected in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Here, we assess time and tissue dependence of R2* enhancement and its impact on pharmacokinetic parameter quantification and treatment monitoring. Multiecho DCE-MRI was performed at 7 T on mice bearing subcutaneous TOV-21G human ovarian cancer xenografts (n = 8) and on the transgenic adenocarcinoma of the mouse prostate (TRAMP) model (n = 7). Subsequently, the TOV-21G tumor-bearing mice were treated with bevacizumab and rescanned 2 days later. Pharmacokinetic analysis (extended Tofts model) was performed using either the first echo signal only (standard single-echo DCE-MRI) or the estimated signal at TE = 0 derived from exponential fitting of R2* relaxation (R2*-corrected). Neglecting R2* enhancement causes underestimation of Gd-DOTA concentration (peak enhancement underestimated by 9.4%-16% in TOV-21G tumors and 13%-20% in TRAMP prostates). Median K^trans and v_e were underestimated in every mouse (TOV-21G K^trans: 11%-19%, TOV-21G v_e: 5.3%-8.9%; TRAMP K^trans: 8.6%-19%, TRAMP v_e: 12%-21%). Bevacizumab treatment reduced K^trans in all TOV-21G tumors after 48 hours. Treatment effect was significantly greater in all tumors after R2* correction (median change of -0.050 min^-1 in R2*-corrected K^trans vs. -0.037 min^-1 in uncorrected K^trans). R2* enhancement in DCE-MRI is both time- and tissue-dependent and may not be negligible at 7 T in tissue with high K^trans. This has consequences for the use of K^trans and other DCE-MRI parameters as biomarkers, because treatment effect size can be underestimated when R2* enhancement is neglected.

Linearization improves the repeatability of quantitative dynamic contrast-enhanced MRI

PURPOSE

The purpose of this study was to compare the repeatabilities of the linear and nonlinear Tofts and reference region models (RRM) for dynamic contrast-enhanced MRI (DCE-MRI).

MATERIALS AND METHODS

Simulated and experimental DCE-MRI data from 12 rats with a flank tumor of C6 glioma acquired over three consecutive days were analyzed using four quantitative and semi-quantitative DCE-MRI metrics. The quantitative methods used were: 1) linear Tofts model (LTM), 2) non-linear Tofts model (NTM), 3) linear RRM (LRRM), and 4) non-linear RRM (NRRM). The following semi-quantitative metrics were used: 1) maximum enhancement ratio (MER), 2) time to peak (TTP), 3) initial area under the curve (iauc64), and 4) slope. LTM and NTM were used to estimate K^trans, while LRRM and NRRM were used to estimate K^trans relative to muscle (R^Ktrans). Repeatability was assessed by calculating the within-subject coefficient of variation (wSCV) and the percent intra-subject variation (iSV) determined with the Gage R&R analysis.

RESULTS

The iSV for R^Ktrans using LRRM was two-fold lower compared to NRRM at all simulated and experimental conditions. A similar trend was observed for the Tofts model, where LTM was at least 50% more repeatable than the NTM under all experimental and simulated conditions. The semi-quantitative metrics iauc64 and MER were as equally repeatable as K^trans and R^Ktrans estimated by LTM and LRRM respectively. The iSV for iauc64 and MER were significantly lower than the iSV for slope and TTP.

CONCLUSION

In simulations and experimental results, linearization improves the repeatability of quantitative DCE-MRI by at least 30%, making it as repeatable as semi-quantitative metrics.

Reproducibility and relative stability in magnetic resonance imaging indices of tumor vascular physiology over a period of 24h in a rat 9L gliosarcoma model

PURPOSE

The objective was to study temporal changes in tumor vascular physiological indices in a period of 24h in a 9L gliosarcoma rat model.

METHODS

Fischer-344 rats (N=14) were orthotopically implanted with 9L cells. At 2weeks post-implantation, they were imaged twice in a 24h interval using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Data-driven model-selection-based analysis was used to segment tumor regions with varying vascular permeability characteristics. The region with the maximum number of estimable parameters of vascular kinetics was chosen for comparison across the two time points. It provided estimates of three parameters for an MR contrast agent (MRCA): i) plasma volume (v_p), ii) forward volumetric transfer constant (K^trans) and interstitial volume fraction (v_e, ratio of K^trans to reverse transfer constant, k_ep). In addition, MRCA extracellular distribution volume (V_D) was estimated in the tumor and its borders, along with tumor blood flow (TBF) and peritumoral MRCA flux. Descriptors of parametric distributions were compared between the two times. Tumor extent was examined by hematoxylin and eosin (H&E) staining. Picrosirus red staining of secreted collagen was performed as an additional index for 9L cells.

RESULTS

Test-retest differences between population summaries for any parameter were not significant (paired t and Wilcoxon signed rank tests). Bland-Altman plots showed no apparent trends between the differences and averages of the test-retest measures for all indices. The intraclass correlation coefficients showed moderate to almost perfect reproducibility for all of the parameters, except v_p. H&E staining showed tumor infiltration in parenchyma, perivascular space and white matter tracts. Collagen staining was observed along the outer edges of main tumor mass.

CONCLUSION

The data suggest the relative stability of these MR indices of tumor microenvironment over a 24h duration in this gliosarcoma model.

DCE-MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro-fuzzy-based model and nested model selection technique

This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimated by the model selection (MS) technique in dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data analysis, and patient age. DCE-MRI investigations of 33 treatment-naïve patients with GBM were studied. Using the modified Tofts model and MS technique, the following physiologically nested models were constructed: Model 1, no vascular leakage (normal tissue); Model 2, leakage without efflux; Model 3, leakage with bidirectional exchange (influx and efflux). For each patient, the PK parameters of the three models were estimated as follows: blood plasma volume (v_p ) for Model 1; v_p and volume transfer constant (K^trans ) for Model 2; v_p , K^trans and rate constant (k_ep ) for Model 3. Using Cox regression analysis, the best combination of the estimated PK parameters, together with patient age, was identified for the design and training of ANFIS. A K-fold cross-validation (K = 33) technique was employed for training, testing and optimization of ANFIS. Given the survival time distribution, three classes of survival were determined and a confusion matrix for the correct classification fraction (CCF) of the trained ANFIS was estimated as an accuracy index of ANFIS's performance. Patient age, k_ep and v_e (K^trans /k_ep ) of Model 3, and K^trans of Model 2, were found to be the most effective parameters for training ANFIS. The CCF of the trained ANFIS was 84.8%. High diagonal elements of the confusion matrix (81.8%, 90.1% and 81.8% for Class 1, Class 2 and Class 3, respectively), with low off-diagonal elements, strongly confirmed the robustness and high performance of the trained ANFIS for predicting the three survival classes. This study confirms that DCE-MRI PK analysis, combined with the MS technique and ANFIS, allows the construction of a DCE-MRI-based fuzzy integrated predictor for the prediction of the survival of patients with GBM.

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

Extravascular extracellular space (v_e ) 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 v_e 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 v_e at a resolution of 10% (error of ±5%) was 82%. The ANN was applied on DCE-MRI data of 26 glioblastoma patients to estimate v_e in tumor regions. Its results were compared with the maximum likelihood estimation (MLE) of v_e . 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 v_e .

Evaluation of Precision in Optoacoustic Tomography for Preclinical Imaging in Living Subjects

Optoacoustic tomography (OT) is now widely used in preclinical imaging; however, the precision (repeatability and reproducibility) of OT has yet to be determined. Methods: We used a commercial small-animal OT system. Measurements in stable phantoms were used to independently assess the impact of system variables on precision (using coefficient of variation, COV), including acquisition wavelength, rotational position, and frame averaging. Variables due to animal handling and physiology, such as anatomic placement and anesthesia conditions, were then assessed in healthy nude mice using the left kidney and spleen as reference organs. Temporal variation was assessed by repeated measurements over hours and days both in phantoms and in vivo. Sensitivity to small-molecule dyes was determined in phantoms and in vivo; precision was assessed in vivo using IRDye800CW. Results: OT COV in a stable phantom was less than 2.8% across all wavelengths over 30 d. The factors with the greatest impact on signal repeatability in phantoms were rotational position and user experience, both of which still resulted in a COV of less than 4% at 700 nm. Anatomic region-of-interest size showed the highest variation, at 12% and 18% COV in the kidney and spleen, respectively; however, functional SO2 measurements based on a standard operating procedure showed an exceptional reproducibility of less than 4% COV. COV for repeated injections of IRDye800CW was 6.6%. Sources of variability for in vivo data included respiration rate, degree of user experience, and animal placement. Conclusion: Data acquired with our small-animal OT system were highly repeatable and reproducible across subjects and over time. Therefore, longitudinal OT studies may be performed with high confidence when our standard operating procedure is followed.

A continuous-infusion dynamic MRI model at 3.0 Tesla for the serial quantitative evaluation of microvascular proliferation in an animal model of glioblastoma multiforme

PURPOSE

To develop a continuous-infusion dynamic MRI technique to characterize tumor-associated microvascular proliferation (MVP) in a rat brain model of glioblastoma multiforme.

METHODS

The proposed model assumes effects due to tumor-associated MVP (eg, vascular permeability, K^trans ; intravascular plasma fraction, v_p ) cannot be individually separated and solves for a single parameter (k^vasc ) that quantifies the T₁ -weighted contrast enhancement from dynamic images acquired during continuous contrast agent (CA) infusion. Untreated C6 tumor-bearing animals (N = 6) were serially imaged on postoperative days (PODs) 14 and 18 with a 3 Tesla clinical scanner utilizing a dynamic spatial and temporal resolution of 0.38 × 0.38 × 1.5 mm³ and 3.47 s, respectively.

RESULTS

An association was present between PODs 14 and 18 for median tumor k^vasc (Pearson's r = 0.94, P = 0.0052) and CA concentration ([CA], derived from pre- and postcontrast R₁ maps; r = 0.94, P = 0.0054). On POD 18, there was a voxel-based association between k^vasc and [CA] within each tumor (0.45 < r < 0.82, P < 0.001). However, voxel-based subregions demonstrated a reduced association between k^vasc and [CA] (N = 5; -0.08 < r < 0.22, P > 0.05) or an inverse association (N = 1; r = -0.28, P = 0.001), indicating differences between locations of vascular permeability and subsequent CA pooling in tumors.

CONCLUSION

The continuous-infusion method may provide a quantitative measure for characterizing and monitoring tumor-associated MVP. Magn Reson Med 78:1824-1838, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Acute Temporal Changes of MRI-Tracked Tumor Vascular Parameters after Combined Anti-angiogenic and Radiation Treatments in a Rat Glioma Model: Identifying Signatures of Synergism

In this study we used magnetic resonance imaging (MRI) biomarkers to monitor the acute temporal changes in tumor vascular physiology with the aim of identifying the vascular signatures that predict response to combined anti-angiogenic and radiation treatments. Forty-three athymic rats implanted with orthotopic U-251 glioma cells were studied for approximately 21 days after implantation. Two MRI studies were performed on each animal, pre- and post-treatment, to measure tumor vascular parameters. Two animal groups received treatment comprised of Cilengitide, an anti-angiogenic agent and radiation. The first group received a subcurative regimen of Cilengitide 1 h before irradiation, while the second group received a curative regimen of Cilengitide 8 h before irradiation. Cilengitide was given as a single dose (4 mg/kg; intraperitoneal) after the pretreatment MRI study and before receiving a 20 Gy radiation dose. After irradiation, the post-treatment MRI study was performed at selected time points: 2, 4, 8 and 12 h (n = ≥5 per time point). Significant changes in vascular parameters were observed at early time points after combined treatments in both treatment groups (1 and 8 h). The temporal changes in vascular parameters in the first group (treated 1 h before exposure) resembled a previously reported pattern associated with radiation exposure alone. Conversely, in the second group (treated 8 h before exposure), all vascular parameters showed an initial response at 2-4 h postirradiation, followed by an apparent lack of response at later time points. The signature time point to define the "synergy" of Cilengitide and radiation was 4 h postirradiation. For example, 4 h after combined treatments using a 1 h separation (which followed the subcurative regimen), tumor blood flow was significantly decreased, nearly 50% below baseline (P = 0.007), whereas 4 h after combined treatments using an 8 h separation (which followed the curative regimen), tumor blood flow was only 10% less than baseline. Comparison between the first and second groups further revealed that most other vascular parameters were maximally different 4 h after combined treatments. In conclusion, the data are consistent with the assertion that the delivery of radiation at the vascular normalization time window of Cilengitide improves radiation treatment outcome. The different vascular responses after the different delivery times of combined treatments in light of the known tumor responses under similar conditions would indicate that timing has a crucial influence on treatment outcome and long-term survival. Tracking acute changes in tumor physiology after monotherapy or combined treatments appears to aid in identifying the beneficial timing for administration, and perhaps has predictive value. Therefore, judicial timing of treatments may result in optimal treatment response.

Cell Treatment for Stroke in Type Two Diabetic Rats Improves Vascular Permeability Measured by MRI

Treatment of stroke with bone marrow stromal cells (BMSC) significantly enhances brain remodeling and improves neurological function in non-diabetic stroke rats. Diabetes is a major risk factor for stroke and induces neurovascular changes which may impact stroke therapy. Thus, it is necessary to test our hypothesis that the treatment of stroke with BMSC has therapeutic efficacy in the most common form of diabetes, type 2 diabetes mellitus (T2DM). T2DM was induced in adult male Wistar rats by administration of a high fat diet in combination with a single intraperitoneal injection (35mg/kg) of streptozotocin. These rats were then subjected to 2h of middle cerebral artery occlusion (MCAo). T2DM rats received BMSC (5x10⁶, n = 8) or an equal volume of phosphate-buffered saline (PBS) (n = 8) via tail-vein injection at 3 days after MCAo. MRI was performed one day and then weekly for 5 weeks post MCAo for all rats. Compared with vehicle treated control T2DM rats, BMSC treatment of stroke in T2DM rats significantly (p<0.05) decreased blood-brain barrier disruption starting at 1 week post stroke measured using contrast enhanced T₁-weighted imaging with gadopentetate, and reduced cerebral hemorrhagic spots starting at 3 weeks post stroke measured using susceptibility weighted imaging, although BMSC treatment did not reduce the ischemic lesion volumes as demarcated by T₂ maps. These MRI measurements were consistent with histological data. Thus, BMSC treatment of stroke in T2DM rats initiated at 3 days after stroke significantly reduced ischemic vascular damage, although BMSC treatment did not change infarction volume in T2DM rats, measured by MRI.

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.

Effect of T2* correction on contrast kinetic model analysis using a reference tissue arterial input function at 7 T

OBJECTIVES

We aimed to investigate the effect of T2* correction on estimation of kinetic parameters from T1-weighted dynamic contrast enhanced (DCE) MRI data when a reference-tissue arterial input function (AIF) is used.

MATERIALS AND METHODS

DCE-MRI data were acquired from seven mice with 4T1 mouse mammary tumors using a double gradient echo sequence at 7 T. The AIF was estimated from a region of interest in the muscle. The extended Tofts model was used to estimate pharmacokinetic parameters in the enhancing part of the tumor, with and without T2* correction of the lesion and AIF. The parameters estimated with T2* correction of both the AIF and lesion time-intensity curve were assumed to be the reference standard.

RESULTS

For the whole population, there was significant difference (p < 0.05) in transfer constant (K(trans)) between T2* corrected and not corrected methods, but not in interstitial volume fraction (ve). Individually, no significant differences were found in K(trans) and ve of four and six tumors, respectively, between the T2* corrected and not corrected methods. In contrast, K(trans) was significantly underestimated, if the T2* correction was not used, in other tumors for which the median K(trans) was larger than 0.4 min(-1).

CONCLUSION

T2* effect on tumors with high K(trans) may not be negligible in kinetic model analysis, even if AIF is estimated from reference tissue where the concentration of contrast agent is relatively low.

MRI-Tracked Tumor Vascular Changes in the Hours after Single-Fraction Irradiation

The purpose of this study was to characterize changes in tumor vascular parameters hours after a single radiation exposure in an orthotopic brain tumor model. U-251 human brain tumors were established intracerebrally in rat brains, and tumor blood flow, forward volume transfer constant (K(trans)) and interstitial volume fraction (v(e)) were measured using magnetic resonance imaging (MRI). Tumors were exposure to a single stereotactic radiation treatment of 20 Gy. Vascular parameters were assessed one additional time between 2 and 24 h after irradiation. After the second MRI session, brain tissue histology was examined for gross changes and apoptosis. In separate studies, cerebral blood flow was measured in nonimplanted controls before radiation exposure and 2 and 24 h after 20 Gy irradiation, and in implanted rats before radiation exposure and at 2 and 24 h after 6 Gy irradiation. Significant changes were observed in tumor-bearing rat brains in the hours after 20 Gy irradiation. Two hours after 20 Gy irradiation, tumor blood flow decreased nearly 80% and ve decreased by 30%. At 4 h, the K(trans) increased by 30% over preirradiation values. Extensive vacuolization and an increase in apoptosis were evident histologically in rats imaged 2 h after irradiation. Between 8 and 12 h after irradiation, all vascular parameters including blood flow returned to near preirradiation values. One day after irradiation, tumor blood flow was elevated 40% over preirradiation values, and other vascular parameters, including K(trans) and ve, were 20-40% below preirradiation values. In contrast, changes in vascular parameters observed in the normal brain 2 or 24 h after 20 Gy irradiation were not significantly different from preirradiation values. Also, tumor blood flow appeared to be unchanged at 2 h after 6 Gy irradiation, with a small increase observed at 24 h, unlike the tumor blood flow changes after 20 Gy irradiation. Large and significant changes in vascular parameters were observed hours after 20 Gy irradiation using noninvasive MRI techniques. It is hypothesized that cellular swelling hours after a high dose of radiation, coinciding with vacuolization, led to a decrease in tumor blood flow and v(e). Four hours after radiation exposure, K(trans) increased in concert with an increase in tumor blood flow. Vascular permeability normalized, 24 h after 20 Gy irradiation, as characterized by a decrease in K(trans). Vascular parameters did not change significantly in the normal brain after 20 Gy irradiation or in the tumor-bearing brain after 6 Gy irradiation.