Optimizing pseudo-spiral sampling for abdominal DCE MRI using a digital anthropomorphic phantom.

PURPOSE: For reliable DCE MRI parameter estimation, k-space undersampling is essential to meet resolution, coverage, and signal-to-noise requirements. Pseudo-spiral (PS) sampling achieves this by sampling k-space on a Cartesian grid following a spiral trajectory. The goal was to optimize PS k-space sampling patterns for abdomin al DCE MRI. METHODS: The optimal PS k-space sampling pattern was determined using an anthropomorphic digital phantom. Contrast agent inflow was simulated in the liver, spleen, pancreas, and pancreatic ductal adenocarcinoma (PDAC). A total of 704 variable sampling and reconstruction approaches were created using three algorithms using different parametrizations to control sampling density, halfscan and compressed sensing regularization. The sampling patterns were evaluated based on image quality scores and the accuracy and precision of the DCE pharmacokinetic parameters. The best and worst strategies were assessed in vivo in five healthy volunteers without contrast agent administration. The best strategy was tested in a DCE scan of a PDAC patient. RESULTS: The best PS reconstruction was found to be PS-diffuse based, with quadratic distribution of readouts on a spiral, without random shuffling, halfscan factor of 0.8, and total variation regularization of 0.05 in the spatial and temporal domains. The best scoring strategy showed sharper images with less prominent artifacts in healthy volunteers compared to the worst strategy. Our suggested DCE sampling strategy also showed high quality DCE images in the PDAC patient. CONCLUSION: Using an anthropomorphic digital phantom, we identified an optimal PS sampling strategy for abdominal DCE MRI, and demonstrated feasibility in a PDAC patient.

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

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

Influence of scan duration on dynamic contrast -enhanced magnetic resonance imaging pharmacokinetic parameters for brain lesions.

OBJECTIVE: Gadolinium-based contrast agent needs time to leak into the extravascular-extracellular space, leak back into the vascular space, and reach an equilibrium state. For this reason, acquisition times of <10 min may cause inaccurate estimation of pharmacokinetic parameters. Since no studies have been conducted on the influence of long scan times on DCE-MRI parameters in brain tumors, the aim of this study is to investigate the variation of DCE-MRI-derived kinetic parameters as a function of acquisition time, from 5 to 10 min in brain tumors. MATERIALS AND METHODS: Fifty-two patients with histologically confirmed brain tumors were enrolled in this retrospective study, and examination at 3 T, DCE-MRI, with scan duration of 10 min, was used for retrospective generation of 6 sets of quantitative DCE-MRI maps (Ktrans, Ve and Kep) from 5 to 10 min. Features were extracted from the DCE-MRI maps in contrast enhancement (CE) volumes. Kruskal-Wallis with post-hoc correction and coefficient of variation (CoV) were used as statistical test to compare DCE-MRI maps obtained from 6 data sets. SIGNIFICANCE: p < 0.05. RESULTS: No differences in Ktrans features in CE volumes between different scan durations. Ve, Kep features in CE volumes were influenced by different data length. The highest number of significantly different Ve and Kep features in CE volumes were between 5 min and 10 min (p < 0.013), 5 min and 9 min (p < 0.044), 6 min and 10 min (p < 0.040). CoV of Kep was reduced from 5 min to 10 min, going from highly variable (CoV = 0.70) to mildly variable (CoV = 0.42). CONCLUSION: Kep and Ve were time-dependent in brain tumors, so a longer scan time is needed to obtain reliable parameter values. Ktrans was found to be time-independent, as it remains the same in all 6 acquisition times and is the only reliable parameter with short acquisition times.

Parametric maps of spatial two-tissue compartment model for prostate dynamic contrast enhanced MRI - comparison with the standard tofts model in the diagnosis of prostate cancer.

The spatial two-tissue compartment model (2TCM) was used to analyze prostate dynamic contrast enhanced (DCE) MRI data and compared with the standard Tofts model. A total of 29 patients with biopsy-confirmed prostate cancer were included in this IRB-approved study. MRI data were acquired on a Philips Achieva 3T-TX scanner. After T2-weighted and diffusion-weighted imaging, DCE data using 3D T1-FFE mDIXON sequence were acquired pre- and post-contrast media injection (0.1 mmol/kg Multihance) for 60 dynamic scans with temporal resolution of 8.3 s/image. The 2TCM has one fast ([Formula: see text] and [Formula: see text]) and one slow ([Formula: see text] and [Formula: see text]) exchanging compartment, compared with the standard Tofts model parameters (Ktrans and kep). On average, prostate cancer had significantly higher values (p < 0.01) than normal prostate tissue for all calculated parameters. There was a strong correlation (r = 0.94, p < 0.001) between Ktrans and [Formula: see text] for cancer, but weak correlation (r = 0.28, p < 0.05) between kep and [Formula: see text]. Average root-mean-square error (RMSE) in fits from the 2TCM was significantly smaller (p < 0.001) than the RMSE in fits from the Tofts model. Receiver operating characteristic (ROC) analysis showed that fast [Formula: see text] had the highest area under the curve (AUC) than any other individual parameter. The combined four parameters from the 2TCM had a considerably higher AUC value than the combined two parameters from the Tofts model. The 2TCM is useful for quantitative analysis of prostate DCE-MRI data and provides new information in the diagnosis of prostate cancer.

Dynamic 2-deoxy-D-glucose-enhanced multispectral optoacoustic tomography for assessing metabolism and vascular hemodynamics of breast cancer.

Clinical tools for measuring tumor vascular hemodynamics, such as dynamic contrast-enhanced MRI, are clinically important to assess tumor properties. Here we explored the use of multispectral optoacoustic tomography (MSOT), which has a high spatial and temporal resolution, to measure the intratumoral pharmacokinetics of a near-infrared-dye-labeled 2-Deoxyglucose, 2-DG-800, in orthotropic 2-LMP breast tumors in mice. As uptake of 2-DG-800 is dependent on both vascular properties, and glucose transporter activity - a widely-used surrogate for metabolism, we evaluate hemodynamics of 2-DG-MP by fitting the dynamic MSOT signal of 2-DG-800 into two-compartment models including the extended Tofts model (ETM) and reference region model (RRM). We showed that dynamic 2-DG-enhanced MSOT (DGE-MSOT) is powerful in acquiring hemodynamic rate constants, including Ktrans and Kep, via systemically injecting a low dose of 2-DG-800 (0.5 µmol/kg b.w.). In our study, both ETM and RRM are efficient in deriving hemodynamic parameters in the tumor. Area-under-curve (AUC) values (which correlate to metabolism), and Ktrans and Kep values, can effectively distinguish tumor from muscle. Hemodynamic parameters also demonstrated correlations to hemoglobin, oxyhemoglobin, and blood oxygen level (SO2) measurements by spectral unmixing of the MSOT data. Together, our study for the first time demonstrated the capability of DGE-MSOT in assessing vascular hemodynamics of tumors.

Diagnostic Value of DCE-MRI and Tofts Model in Children with Unilateral Hydronephrosis.

BACKGROUND: Hydronephrosis is a common condition, and the correct diagnosis of hydronephrosis is necessary to improve the early diagnosis rates of pediatric hydronephrosis. OBJECTIVE: The objective of this study is to explore and analyze the diagnostic value of dynamic contrast- enhanced magnetic resonance imaging (DCE-MRI) analyzed using the Tofts model in children with unilateral hydronephrosis. METHODS: We retrospectively selected data from 88 children with unilateral hydronephrosis treated in our hospital from September 2018 to October 2020. Routine and DCE-MR renal image indexes were collected and their pharmacokinetic variables were calculated based on the Tofts model to compare kinetic parameters of affected and normal kidney. We compared the renal parenchymal thickness and other renal function indexes in children with different degrees of hydronephrosis, and drew receiver operating characteristic (ROC) curves to evaluate the diagnostic value of this approach in children with hydronephrosis. RESULTS: The Ktrans, Kep, and Ve values in the diseased kidneys were lower than those in the normal ones (P<0.05). The thickness of the healthy renal parenchyma in children with severe hydronephrosis was higher than in children with moderate and mild hydronephrosis, but the renal parenchyma thickness and the thickness ratio of renal parenchyma on the affected side were lower than those in children with moderate and mild hydronephrosis (P<0.05). Sensitivity, specificity and accuracy of DCE-MRI and Tofts model in the diagnosis of hydronephrosis in children were higher than those of a single DCE-MRI (P<0.05). The area under the ROC curve for the DCE-MRI and Tofts model approach for the diagnosis of hydronephrosis in children was 0.789 (95% CI, 0.72-0.859), and the sensitivity and specificity were 86.36% and 71.59%, respectively. CONCLUSION: DCE-MRI and Tofts model can provide a clear picture of renal morphology, and renal function evaluation parameters. They have high sensitivity and specificity in the diagnosis of hydronephrosis in children.

Incorporating cross-voxel exchange into the analysis of dynamic contrast-enhanced imaging data: theory, simulations and experimental results.

Predictions of tumour perfusion are key determinants of drug delivery and responsiveness to therapy. Pharmacokinetic models allow for the estimation of perfusion properties of tumour tissues but many assume no dispersion associated with tracer transport away from the capillaries and through the tissue. At the level of a voxel, this translates to assuming no cross-voxel tracer exchange, often leading to the misinterpretation of derived perfusion parameters. Tofts model (TM), a compartmental model widely used in oncology, also makes this assumption. A more realistic description is required to quantify kinetic properties of tracers, such as convection and diffusion. We propose a Cross-Voxel Exchange Model (CVXM) for analysing cross-voxel tracer kinetics.In silicodatasets quantifying the roles of convection and diffusion in tracer transport (which TM ignores) were employed to investigate the interpretation of Tofts' perfusion parameters compared to CVXM. TM returned inaccurate values ofKtransandvewhere diffusive and convective mechanisms are pronounced (up to 20% and 300% error respectively). A mathematical equation, developed in this work, predicts and gives the correct physiological interpretation of Tofts've.Finally, transport parameters were derived from dynamic contrast enhanced-magnetic resonance imaging of a TS-415 human cervical carcinoma xenograft by using TM and CVXM. The latter deduced lower values ofKtransandvecompared to TM (lower by up to 63% and 76% respectively). It also allowed the detection of a diffusive flux (mean diffusivity 155µm2s-1) in the tumour tissue, as well as an increased convective flow at the periphery (mean velocity 2.3µm s-1detected). The results serve as a proof of concept establishing the feasibility of using CVXM for accurately determining transport metrics that characterize the exchange of tracer between voxels. CVXM needs to be investigated further as its parameters can be linked to the tumour microenvironment properties (permeability, pressure…), potentially leading to enhanced personalized treatment planning.

Correlation of radiomic features on dynamic contrast-enhanced magnetic resonance with microvessel density in hepatocellular carcinoma based on different models.

OBJECTIVE: To explore the correlations of radiomic features of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with microvessel density (MVD) in patients with hepatocellular carcinoma (HCC), based on single-input and dual-input two-compartment extended Tofts (SITET and DITET) models. METHODS: We compared the quantitative parameters of SITET and DITET models for DCE-MRI in 30 patients with HCC using paired sample t-tests. The correlations of SITET and DITET model parameters with CD31-MVD and CD34-MVD were analyzed using Pearson's correlation analysis. A diagnostic model of CD34-MVD was established and the diagnostic abilities of models for MVD were analyzed using receiver operating characteristic curve (ROC) analysis. RESULTS: There were significant differences between the quantitative parameters in the two kinds of models. Compared with SITET, DITET parameters showed better correlations with CD31-MVD and CD34-MVD. The Ktrans and Ve radiomics features of the DITET model showed high efficiency for predicting the level of CD34-MVD according to ROC analysis, with areas under curves of 0.83 and 0.94, respectively. CONCLUSION: Compared with SITET, the DITET model provides a better indication of the microcirculation of HCC and is thus more suitable for examining patients with HCC.

Repeatability of tumor perfusion kinetics from dynamic contrast-enhanced MRI in glioblastoma.

BACKGROUND: Dynamic contrast-enhanced MRI (DCE-MRI) parameters have been shown to be biomarkers for treatment response in glioblastoma (GBM). However, variations in analysis and measurement methodology complicate determination of biological changes measured via DCE. The aim of this study is to quantify DCE-MRI variations attributable to analysis methodology and image quality in GBM patients. METHODS: The Extended Tofts model (eTM) and Leaky Tracer Kinetic Model (LTKM), with manually and automatically segmented vascular input functions (VIFs), were used to calculate perfusion kinetic parameters from 29 GBM patients with double-baseline DCE-MRI data. DCE-MRI images were acquired 2-5 days apart with no change in treatment. Repeatability of kinetic parameters was quantified with Bland-Altman and percent repeatability coefficient (%RC) analysis. RESULTS: The perfusion parameter with the least RC was the plasma volume fraction (v p ), with a %RC of 53%. The extra-cellular extra-vascular volume fraction (v e ) %RC was 82% and 81%, for extended Tofts-Kety Model (eTM) and LTKM respectively. The %RC of the volume transfer rate constant (K trans ) was 72% for the eTM, and 82% for the LTKM, respectively. Using an automatic VIF resulted in smaller %RCs for all model parameters, as compared to manual VIF. CONCLUSIONS: As much as 72% change in K trans (eTM, autoVIF) can be attributable to non-biological changes in the 2-5 days between double-baseline imaging. Poor K trans repeatability may result from inferior temporal resolution and short image acquisition time. This variation suggests DCE-MRI repeatability studies should be performed institutionally, using an automatic VIF method and following quantitative imaging biomarkers alliance guidelines.

Convolutional neural network for accelerating the computation of the extended Tofts model in dynamic contrast-enhanced magnetic resonance imaging.

Quantitative physiological parameters can be obtained from nonlinear pharmacokinetic models, such as the extended Tofts (eTofts) model, applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). However, the computation of such nonlinear models is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for accelerating the computation of fitting eTofts model without sacrificing agreement with conventional nonlinear-least-square (NLLS) fitting. This was a retrospective study, which included 13 patients with brain glioma for training (75%) and validation (25%), and 11 patients (three glioma, four brain metastases, and four lymphoma) for testing. CAIPIRINHA-Dixon-TWIST DCE-MRI and double flip angle T1 map acquired at 3 T were used. A CNN with both local pathway and global pathway modules was designed to estimate the eTofts model parameters, the volume transfer constant (Ktrans ), blood volume fraction (vp ), and volume fraction of extracellular extravascular space (ve ), from DCE-MRI data of tumor and normal-appearing voxels. The CNN was trained on mixed dataset consisting of synthetic and patient data. The CNN result and computation speed were compared with NLLS fitting. The robustness to noise variations and generalization to brain metastases and lymphoma data were also evaluated. Statistical tests used were Student's t test on mean absolute error, concordance correlation coefficient (CCC), and normalized root mean squared error. Including global pathway modules in the CNN and training the network with mixed data significantly (p < 0.05) improved the CNN performance. Compared with NLLS fitting, CNN yields an average CCC greater than 0.986 for Ktrans , greater than 0.965 for vp , and greater than 0.948 for ve . The CNN accelerated computation speed approximately 2000 times compared to NLLS, showed robustness to noise (signal-to-noise ratio >34.42 dB), and had no significant (p > 0.21) difference applied to brain metastases and lymphoma data. In conclusion, the proposed CNN to estimate eTofts parameters showed comparable result as NLLS fitting while significantly reducing the computation time. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.

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.

Radiomic features of Pk-DCE MRI parameters based on the extensive Tofts model in application of breast cancer.

To explore radiomic features of pharmacokinetic dynamic contrast-enhanced (Pk-DCE) MRI on the extensive Tofts model to diagnose breast cancer and predict molecular phenotype. Breast lesions enrolled must undergo Pk-DCE MRI before treatment or puncture, and be identified as primary lesions by pathology. Ktrans, Kep, Ve and Vp were generated on the extensive Tofts model. Radiomic features (histogram, geometry and texture features) were extracted from parametric maps and selected by LASSO. The subjects were divided into training and validation cohort with a ratio of 4:1 to construct model in diagnosis of breast cancer. Feature analysis was made to predict the molecular phenotype. Area under curve (AUC), sensitivity, specificity and accuracy were used to evaluate radiomic features. DeLong's test was performed to compare AUC values. 228 breast lesions met the criteria were used to discrimination and 126 malignant lesions were used to study molecular phenotypes. The number of training cohort and validation cohort were 182 and 46, respectively. The AUC of Ktrans, Kep, Ve, and Vp was 0.95, 0.93, 0.89, and 0.96, and their accuracy was 85%, 89%, 89%, 94% respectively in diagnosis of breast lesions, while their AUC was 0.71 to 0.77, 0.61 to 0.68, and 0.67 to 0.74 to predict ER/PR, Her-2, and Ki-67. There was no significant difference among parameters (P > 0.05). Radiomic features based on Pk-DCE MRI have an advantage to diagnose breast cancer and less ability to predict molecular phenotypes, which are beneficial to guide clinical treatment of breast lesions in some extent.

DCE-MRI assessment of response to neoadjuvant SABR in early stage breast cancer: Comparisons of single versus three fraction schemes and two different imaging time delays post-SABR.

PURPOSE: To determine the effect of dose fractionation and time delay post-neoadjuvant stereotactic ablative radiotherapy (SABR) on dynamic contrast-enhanced (DCE)-MRI parameters in early stage breast cancer patients. MATERIALS AND METHODS: DCE-MRI was acquired in 17 patients pre- and post-SABR. Five patients were imaged 6-7 days post-21 Gy/1fraction (group 1), six 16-19 days post-21 Gy/1fraction (group 2), and six 16-18 days post-30 Gy/3 fractions every other day (group 3). DCE-MRI scans were performed using half the clinical dose of contrast agent. Changes in the surrounding tissue were quantified using a signal-enhancement threshold metric that characterizes changes in signal-enhancement volume (SEV). Tumour response was quantified using Ktrans and ve (Tofts model) pre- and post-SABR. Significance was assessed using a Wilcoxin signed-rank test. RESULTS: All group 1 and 4/6 group 2 patients' SEV increased post-SABR. All group 3 patients' SEV decreased. The mean Ktrans increased for group 1 by 76% (p = 0.043) while group 2 and 3 decreased 15% (p = 0.028) and 34% (p = 0.028), respectively. For ve, there was no significant change in Group 1 (p = 0.35). Groups 2 showed an increase of 24% (p = 0.043), and Group 3 trended toward an increase (23%, p = 0.08). CONCLUSION: Kinetic parameters measured 2.5 weeks post-SABR in both single fraction and three fraction groups were indicative of response but only the single fraction protocol led to enhancement in the surrounding tissue. Our results also suggest that DCE-MRI one-week post-SABR may be too early for response assessment, at least for single fraction SABR, whereas 2.5 weeks appears sufficiently long to minimize confounding acute effects.

Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models.

PURPOSE: The purpose of this study is to investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test. METHODS: DCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV). RESULTS: The model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (< 0.1%). In analysing the reliability of Ktrans, when considering regions with a CV < 20%, ≈ 25% of voxels were found to be stable across all patients. The remaining 75% of voxels were considered unreliable. CONCLUSIONS: The majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data.

A multi-parametric PET/MRI study of breast cancer: Evaluation of DCE-MRI pharmacokinetic models and correlation with diffusion and functional parameters.

46 patients with histologically confirmed breast cancer were enrolled and imaged with a 3T hybrid PET/MRI system, at staging. Diffusion, functional and perfusion parameters (measured by Tofts and shutter speed models) were compared. Results showed a good correlation between pharmacokinetic parameters and the SUV.

Influence of arterial input function (AIF) on quantitative prostate dynamic contrast-enhanced (DCE) MRI and zonal prostate anatomy.

PURPOSE: Reproducibility of quantitative perfusion analysis of DCE requires a standardized AIF acquisition. However, there are many different approaches for AIF assessment so that the absolute values of perfusion parameters may vary depending on the used method. This study analyzes the influence of the method of AIF determination on quantitative DCE-MRI. METHODS: In this retrospective, single-center, cohort study three different methods of AIF determination in 50 consecutive patients with multiparametric MRI of the prostate were conducted. As a reference, AIF was selected manually by defining a region of interest in an artery manually (AIFm). The second method (AIFa), based on an automated algorithm and the third, population-derived AIFp where then compared. Primary endpoint were differences in the performance of the perfusion parameters Ktrans, ve and kep regarding the AIF acquisition methods, secondary endpoints consisted of the evaluation of differences in the peripheral and transition zone of the prostate (PZ, TZ). RESULTS: In all three methods, Ktrans, ve, and kep were significantly higher in PZ than in TZ with Ktrans showing least overlapping. There were no significant differences for Ktrans determined with AIFm and AIFa (0.3 ±â€¯0.2 min-1 for PZ for both and 0.5 ±â€¯0.3 min-1 for TZ in AIFm and 0.4 ±â€¯0.3 min-1 in AIFa), while there were great differences between AIFa and AIFp and AIFm and AIFp (0.1 ±â€¯0.03 min-1 for TZ and PZ in AIFp). Spearman test demonstrated good correlation of values for Ktrans and kep in all 3 methods (ρ ≥ 0.76). AIFa showed a success rate of 98% in finding the artery. CONCLUSION: AIFa is a recommendable user-independent automatical method to determine quantitative perfusion parameters allowing an objective measurement and saving interactive time for the radiologist. AIFp may be applied as second alternative method.

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 Ktrans, while LRRM and NRRM were used to estimate Ktrans relative to muscle (RKtrans). 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 RKtrans 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 Ktrans and RKtrans 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.

MRI protocol optimization for quantitative DCE-MRI of the spine.

PURPOSE: In this study we systematically investigated different Dynamic Contrast Enhancement (DCE)-MRI protocols in the spine, with the goal of finding an optimal protocol that provides data suitable for quantitative pharmacokinetic modelling (PKM). MATERIALS AND METHODS: In 13 patients referred for MRI of the spine, DCE-MRI of the spine was performed with 2D and 3D MRI protocols on a 3T Philips Ingenuity MR system. A standard bolus of contrast agent (Dotarem - 0.2ml/kg body weight) was injected intravenously at a speed of 3ml/s. Different techniques for acceleration and motion compensation were tested: parallel imaging, partial-Fourier imaging and flow compensation. The quality of the DCE MRI images was scored on the basis of SNR, motion artefacts due to flow and respiration, signal enhancement, quality of the T1 map and of the arterial input function, and quality of pharmacokinetic model fitting to the extended Tofts model. RESULTS: Sagittal 3D sequences are to be preferred for PKM of the spine. Acceleration techniques were unsuccessful due to increased flow or motion artefacts. Motion compensating gradients failed to improve the DCE scans due to the longer echo time and the T2* decay which becomes more dominant and leads to signal loss, especially in the aorta. The quality scoring revealed that the best method was a conventional 3D gradient-echo acquisition without any acceleration or motion compensation technique. The priority in the choice of sequence parameters should be given to reducing echo time and keeping the dynamic temporal resolution below 5s. Increasing the number of acquisition, when possible, helps towards reducing flow artefacts. In our setting we achieved this with a sagittal 3D slab with 5 slices with a thickness of 4.5mm and two acquisitions. CONCLUSION: The proposed DCE protocol, encompassing the spine and the descending aorta, produces a realistic arterial input function and dynamic data suitable for PKM.

Diffusion-compensated tofts model suggests contrast leakage through aneurysm wall.

PURPOSE: The purpose of this study was to investigate the diffusional transport of contrast agent and its effects on kinetic modeling of dynamic contrast enhanced (DCE) images. METHODS: We performed simulations of our diffusion-compensated model and compared these results to human intracranial aneurysms (IAs). We derive an easy-to-use parameterization of diffusional effects that can provide an accurate estimate of diffusion corrected contrast agent leakage rates (ktrans ). Finally, we performed re-ansalysis of an existing data set to determine whether diffusion-corrected kinetic parameters improve the identification of high-risk IAs, thereby providing a new MRI-based imaging metric of IA stability based on wall integrity. RESULTS: Probability distributions of simulated versus measured data show contrast leakage away from the aneurysm wall. Parameterization of diffusional effects on ktrans showed high correlation with long-chain methods in both surrounding tissue and near the aneurysm wall (r2 = 0.91 and r2 = 0.90, respectively). Finally, size, ktrans , and ( ktrans-kDCtrans) showed significant univariate relationships with rupture risk (P < 0.05). CONCLUSIONS: We report the first evidence of diffusion-compensated permeability modeling in intracranial aneurysms and propose a parameterization of diffusional effects on ktrans . Furthermore, a comparison of measured versus simulated data suggests that contrast leakage occurs across the aneurysm wall. Magn Reson Med 78:2388-2398, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

The quantification of blood-brain barrier disruption using dynamic contrast-enhanced magnetic resonance imaging in aging rhesus monkeys with spontaneous type 2 diabetes mellitus.

Microvascular lesions of the body are one of the most serious complications that can affect patients with type 2 diabetes mellitus. The blood-brain barrier (BBB) is a highly selective permeable barrier around the microvessels of the brain. This study investigated BBB disruption in diabetic rhesus monkeys using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Multi-slice DCE-MRI was used to quantify BBB permeability. Five diabetic monkeys and six control monkeys underwent magnetic resonance brain imaging in 3 Tesla MRI system. Regions of the frontal cortex, the temporal cortex, the basal ganglia, the thalamus, and the hippocampus in the two groups were selected as regions of interest to calculate the value of the transport coefficient Ktrans using the extended Tofts model. Permeability in the diabetic monkeys was significantly increased as compared with permeability in the normal control monkeys. Histopathologically, zonula occludens protein-1 decreased, immunoglobulin G leaked out of the blood, and nuclear factor E2-related factor translocated from the cytoplasm to the nuclei. It is likely that diabetes contributed to the increased BBB permeability.