T1-weighted Dynamic Contrast-enhanced MRI Research Articles

Radiomics Nomogram Based on Dual-Sequence MRI for Assessing Ki-67 Expression in Breast Cancer.

Radiomics has been extensively applied in predicting Ki-67 in breast cancer (BC). However, this is often confined to the exploration of a single sequence, without considering the varying sensitivity and specificity among different sequences. To develop a nomogram based on dual-sequence MRI derived radiomic features combined with clinical characteristics for assessing Ki-67 expression in BC. Retrospective. 227 females (average age, 51 years) with 233 lesions and pathologically confirmed BC, which were divided into the training set (n = 163) and test set (n = 70). 3.0-T, T1-weighted dynamic contrast-enhanced MRI (DCE-MRI) and apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (EPI sequence). The regions of interest were manually delineated on ADC and DCE-MRI sequences. Three radiomics models of ADC, DCE-MRI, and dsMRI (combined ADC and DCE-MRI sequences) were constructed by logistic regression and the radiomics score (Radscore) of the best model was calculated. The correlation between Ki-67 expression and clinical characteristics such as receptor status, axillary lymph node (ALN) metastasis status, ADC value, and time signal intensity curve was analyzed, and the clinical model was established. The Radscore was combined with clinical predictors to construct a nomogram. The independent sample t-test, Mann-Whitney U test, Chi-squared test, Interclass correlation coefficients (ICCs), single factor analysis, least absolute shrinkage and selection operator (LASSO), logistic regression, receiver operating characteristics, Delong test, Hosmer_Lemeshow test, calibration curve, decision curve. A P-value <0.05 was considered statistically significant. In the test set, the prediction efficiency of the dsMRI model (AUC = 0.862) was higher than ADC model (AUC = 0.797) and DCE-MRI model (AUC = 0.755). With the inclusion of estrogen receptor (ER) and ALN metastasis, the nomogram displayed quality improvement (AUC = 0.876), which was superior to the clinical model (AUC = 0.787) and radiomics model. The nomogram based on dsMRI radiomic features and clinical characteristics may be able to assess Ki-67 expression in BC. 3 TECHNICAL EFFICACY: Stage 3.

MRI-Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning.

Deep learning (DL) have been reported feasible in breast MRI. However, the effectiveness of DL method in mpMRI combinations for breast cancer detection has not been well investigated. To implement a DL method for breast cancer classification and detection using feature extraction and combination from multiple sequences. Retrospective. A total of 569 local cases as internal cohort (50.2 ± 11.2 years; 100% female), divided among training (218), validation (73) and testing (278); 125 cases from a public dataset as the external cohort (53.6 ± 11.5 years; 100% female). T1-weighted imaging and dynamic contrast-enhanced MRI (DCE-MRI) with gradient echo sequences, T2-weighted imaging (T2WI) with spin-echo sequences, diffusion-weighted imaging with single-shot echo-planar sequence and at 1.5-T. A convolutional neural network and long short-term memory cascaded network was implemented for lesion classification with histopathology as the ground truth for malignant and benign categories and contralateral breasts as healthy category in internal/external cohorts. BI-RADS categories were assessed by three independent radiologists as comparison, and class activation map was employed for lesion localization in internal cohort. The classification and localization performances were assessed with DCE-MRI and non-DCE sequences, respectively. Sensitivity, specificity, area under the curve (AUC), DeLong test, and Cohen's kappa for lesion classification. Sensitivity and mean squared error for localization. A P-value <0.05 was considered statistically significant. With the optimized mpMRI combinations, the lesion classification achieved an AUC = 0.98/0.91, sensitivity = 0.96/0.83 in the internal/external cohorts, respectively. Without DCE-MRI, the DL-based method was superior to radiologists' readings (AUC 0.96 vs. 0.90). The lesion localization achieved sensitivities of 0.97/0.93 with DCE-MRI/T2WI alone, respectively. The DL method achieved high accuracy for lesion detection in the internal/external cohorts. The classification performance with a contrast agent-free combination is comparable to DCE-MRI alone and the radiologists' reading in AUC and sensitivity. 3. Stage 2.

Intraobserver and Interobserver Reproducibility of Breast Diffusion-Weighted Imaging Quantitative Parameters: Readout-Segmented vs. Single-Shot Echo-Planar Imaging.

The recommended technique for breast diffusion-weighted imaging (DWI) acquisitions is not sufficiently standardized in clinical practice. To investigate the intraobserver and interobserver reproducibility of DWI measurements, diffusion-kurtosis imaging (DKI) parameters, and image quality evaluation in breast lesions between single-shot echo-planar imaging (ss-EPI) and readout-segmented echo-planar imaging (rs-EPI). Prospective. A total of 295 women with 209 malignant and 86 benign breast lesions. A 3-T; fat-saturated T2-weighted MR imaging (T2WI); multi-b-value DWI with both ss-EPI and rs-EPI readouts; T1-weighted dynamic contrast-enhanced MRI (DCE-MRI). Mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC) values were measured for each lesion on ss-EPI and rs-EPI, respectively. Image quality was visually evaluated regarding image sharpness, geometric distortion, lesion conspicuity, visualization of anatomic structures, and overall quality. Quantitative and qualitative analyses were performed twice with a time interval of 2 weeks. Intraobserver and interobserver reproducibility wereevaluated using intra-class correlation coefficients (ICC), within-subject coefficient of variation (wCV), and Bland-Altman plots. MK, MD, and ADC quantitative parameters for breast lesions showed excellent intraobserver and interobserver reproducibility, with ICCs >0.75 and wCV values ranging from 2.51% to 7.08% for both sequences. The wCV values in both intraobserver and interobserver measurements were higher in the ss-EPI sequence (3.63%-7.08%) than that of the rs-EPI sequence (2.51%-3.62%). The wCV values differed in subgroups with different histopathological types of lesions, breast density, lesion morphology, and lesion sizes, respectively. Furthermore, rs-EPI (ICCs, 0.76-0.97; wCV values, 2.41%-6.04%) had better intraobserver and interobserver reproducibility than ss-EPI (ICCs, 0.54-0.90; wCV values, 6.18%-13.69%) with regard to image quality. Compared to the ss-EPI, the rs-EPI sequence showed higher intraobserver and interobserver reproducibility for quantitative diffusion-related parameters and image quality assessments measured in breast DWI and DKI. 2. Stage 2.

T1-weighted Dynamic Contrast-enhanced MRI to Differentiate Nonneoplastic and Malignant Vertebral Body Lesions in the Spine.

Background Dynamic contrast agent-enhanced (DCE) perfusion MRI may help differentiate between nonneoplastic and malignant lesions in the spine. Purpose To investigate the correlation between fractional plasma volume (Vp), a parameter derived from DCE perfusion MRI, and histopathologic diagnosis for spinal lesions. Materials and Methods In this retrospective study, patients who underwent DCE perfusion MRI and lesion biopsy between May 2015 and May 2018 were included. Inclusion criteria were short time interval (<30 days) between DCE perfusion MRI and biopsy, DCE perfusion MRI performed before biopsy, and DCE perfusion MRI performed at the same spine level as biopsy. Exclusion criteria were prior radiation treatment on vertebrae of interest, poor DCE perfusion MRI quality, nondiagnostic biopsy, and extensive spinal metastasis or prior kyphoplasty. One hundred thirty-four lesions were separated into a nonneoplastic group (n = 51) and a malignant group (n = 83) on the basis of histopathologic analysis. Two investigators manually defined regions of interest in the vertebrae. DCE perfusion MRI parameter Vp was calculated by using the Tofts pharmacokinetic two-compartment model. Vp was quantified, normalized to adjacent normal vertebrae, and compared between the two groups. A Mann-Whitney U test and receiver operating characteristic analysis was performed to verify the difference in Vp between the nonneoplastic and malignant groups. Reproducibility was assessed by calculating the Cohen κ coefficient. Results One hundred patients (mean age, 65 years ± 11 [standard deviation]; 52 men) were evaluated. Vp was lower in nonneoplastic lesions versus malignant lesions (1.6 ± 1.3 vs 4.2 ± 3.0, respectively; P < .001). The sensitivity of Vp was 93% (77 of 83; 95% confidence interval [CI]: 85%, 97%), specificity was 78% (40 of 51; 95% CI: 65%, 89%), and area under the receiver operating characteristic curve was 0.88 (95% CI: 0.82, 0.95). Cohen κ coefficient suggested substantial agreement in both intra- (κ = 0.72) and interreader (κ = 0.70) reproducibility. Conclusion This study indicated that dynamic contrast agent-enhanced perfusion MRI parameter, fractional plasma volume, was able to differentiate between nonneoplastic spinal lesions and malignant lesions. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Haller in this issue.

A Radiomics nomogram for predicting bone metastasis in newly diagnosed prostate cancer patients

PurposeTo establish and validate a radiomics nomogram for predicting bone metastasis (BM) in patients with newly diagnosed prostate cancer (PCa). MethodOne-hundred and sixteen patients (training cohort: n = 81; validation cohort: n = 35) who underwent prostate MR imaging and confirmed by pathology with newly diagnosed PCa from January 2014 to January 2019 were enrolled. Radiomic features were extracted from diffusion-weighted, axial T2-weighted fat suppression, and dynamic contrast-enhanced T1-weighted MRI of each patient. Dimension reduction, feature selection, and radiomics feature construction were performed using the least absolute shrinkage and selection operator (LASSO) regression. Combined with independent clinical risk factors, a multivariate logistic regression model was used to establish a radiomics nomogram. Nomogram calibration and discrimination were evaluated in training cohort and verified in the validation cohort. Finally, the clinical usefulness of the nomogram was estimated through decision curve analysis (DCA). ResultsRadiomics signature consisting of 12 selected features was significantly correlated with bone status (P < 0.001 for both training and validation sets). The radiomics nomogram combined a radiomics signature from multiparametric MR images with independent clinic risk factors. The model showed good discrimination and calibration in the training cohort (AUC 0.93, 95% CI, 0.86 to 0.99) and the validation cohort (AUC 0.92, 95% CI, 0.84 to 0.99). DCA also demonstrated the clinical use of the radiomics model. ConclusionThe radiomics nomogram, which incorporates the multiparametric MRI-based radiomics signature and clinical risk factors, can be conveniently used to promote individualized prediction of BM in patients with newly diagnosed PCa.

Dynamic contrast-enhanced magnetic resonance imaging perfusion characteristics in meningiomas treated with resection and adjuvant radiosurgery.

OBJECTIVEThere is a need for advanced imaging biomarkers to improve radiation treatment planning and response assessment. T1-weighted dynamic contrast-enhanced perfusion MRI (DCE MRI) allows quantitative assessment of tissue perfusion and blood-brain barrier dysfunction and has entered clinical practice in the management of primary and secondary brain neoplasms. The authors sought to retrospectively investigate DCE MRI parameters in meningiomas treated with resection and adjuvant radiation therapy using volumetric segmentation.METHODSA retrospective review of more than 300 patients with meningiomas resected between January 2015 and December 2018 identified 14 eligible patients with 18 meningiomas who underwent resection and adjuvant radiotherapy. Patients were excluded if they did not undergo adjuvant radiation therapy or DCE MRI. Demographic and clinical characteristics were obtained and compared to DCE perfusion metrics, including mean plasma volume (vp), extracellular volume (ve), volume transfer constant (Ktrans), rate constant (kep), and wash-in rate of contrast into the tissue, which were derived from volumetric analysis of the enhancing volumes of interest.RESULTSThe mean patient age was 64 years (range 49-86 years), and 50% of patients (7/14) were female. The average tumor volume was 8.07 cm3 (range 0.21-27.89 cm3). The median Ki-67 in the cohort was 15%. When stratified by median Ki-67, patients with Ki-67 greater than 15% had lower median vp (0.02 vs 0.10, p = 0.002), and lower median wash-in rate (1.27 vs 4.08 sec-1, p = 0.04) than patients with Ki-67 of 15% or below. Logistic regression analysis demonstrated a statistically significant, moderate positive correlation between ve and time to progression (r = 0.49, p < 0.05). Furthermore, there was a moderate positive correlation between Ktrans and time to progression, which approached, but did not reach, statistical significance (r = 0.48, p = 0.05).CONCLUSIONSThis study demonstrates a potential role for DCE MRI in the preoperative characterization and stratification of meningiomas, laying the foundation for future prospective studies incorporating DCE as a biomarker in meningioma diagnosis and treatment planning.

Accuracy and precision of quantitative DCE-MRI parameters: How should one estimate contrast concentration?

IntroductionPharmacokinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) data are sensitive to acquisition and post-processing techniques, which makes it difficult to compare results obtained using different methods. In particular, one of the most important factors affecting estimation of model parameters is how to convert MRI signal intensities to contrast agent concentration. The purpose of our study was to quantitatively compare a linear signal-to-concentration conversion (LC) as an approximation and a non-linear conversion (NLC) based on the MRI signal equation, in terms of the accuracy and precision of the pharmacokinetic parameters in T1-weighted DCE-MRI. Materials and methodsNumerical simulation studies were conducted to compare LC and NLC in terms of the accuracy and precision in contrast kinetic parameter estimation, and to evaluate their dependency on flip angle (FA), pre-contrast T1 (T10) and arterial input function (AIF). In addition, the effect of the conversion method on the diagnostic accuracy was evaluated with 36 breast lesions (19 benign and 17 malignant). ResultsThe transfer rate (Ktrans) estimated using LC and measured AIF (mAIF) were up to 38% higher than the true Ktrans values, while the LC Ktrans estimates with the presumed AIF (pAIF) were up to 7% lower than the true Ktrans values, when FA = 45°. When using a small FA, such as 12°, the LC Ktrans with pAIF had least sensitivity to the error in T10 compared to the Ktrans estimated using LC with mAIF, and NLC with pAIF or mAIF. The breast DCE-MRI study showed that both LC and NLC Ktrans were significantly different (p < 0.05) between the malignant and benign lesions. The effect size between benign and malignant values as measured by Cohen's d was 1.06 for LC Ktrans and 1.02 for NLC Ktrans. ConclusionThe present study results show that, when precontrast T1 measurement is not available and a low FA is used for DCE-MRI, the uncertainty in the contrast kinetic parameter estimation can be reduced by using the LC method with pAIF, without compromising the diagnostic accuracy.

Differentiating Atypical Hemangiomas and Metastatic Vertebral Lesions: The Role of T1-Weighted Dynamic Contrast-Enhanced MRI.

Vertebral hemangiomas are benign vascular lesions that are almost always incidentally found in the spine. Their classic typical hyperintense appearance on T1- and T2-weighted MR images is diagnostic. Unfortunately, not all hemangiomas have the typical appearance, and they can mimic metastases on routine MR imaging. These are generally referred to as atypical hemangiomas and can result in misdiagnosis and ultimately additional imaging, biopsy, and unnecessary costs. Our objective was to assess the utility of dynamic contrast-enhanced MR imaging perfusion in distinguishing vertebral atypical hemangiomas and malignant vertebral metastases. We hypothesized that permeability and vascular density will be increased in metastases compared with atypical hemangiomas. Consecutive patients from 2011 to 2015 with confirmed diagnoses of atypical hemangiomas and spinal metastases from breast and lung carcinomas with available dynamic contrast-enhanced MR imaging were analyzed. Time-intensity curves were qualitatively compared among the groups. Perfusion parameters, plasma volume, and permeability constant were quantified using an extended Tofts 2-compartment pharmacokinetic model. Statistical significance was tested using the Mann-Whitney U test. Qualitative inspection of dynamic contrast-enhanced MR imaging time-intensity curves demonstrated differences in signal intensity and morphology between metastases and atypical hemangiomas. Quantitative analysis of plasma volume and permeability constant perfusion parameters showed significantly higher values in metastatic lesions compared with atypical hemangiomas (P < .001). Our data demonstrate that plasma volume and permeability constant perfusion parameters and qualitative inspection of contrast-enhancement curves can be used to differentiate atypical hemangiomas from vertebral metastatic lesions. This work highlights the benefits of adding perfusion maps to conventional sequences to improve diagnostic accuracy.

Early Changes in Tumor Perfusion from T1-Weighted Dynamic Contrast-Enhanced MRI following Neural Stem Cell-Mediated Therapy of Recurrent High-Grade Glioma Correlate with Overall Survival.

Background The aim of this study was to correlate T1-weighted dynamic contrast-enhanced MRI- (DCE-MRI-) derived perfusion parameters with overall survival of recurrent high-grade glioma patients who received neural stem cell- (NSC-) mediated enzyme/prodrug gene therapy. Methods A total of 12 patients were included in this retrospective study. All patients were enrolled in a first-in-human study (NCT01172964) of NSC-mediated therapy for recurrent high-grade glioma. DCE-MRI data from all patients were collected and analyzed at three time points: MRI#1—day 1 postsurgery/treatment, MRI#2— day 7 ± 3 posttreatment, and MRI#3—one-month follow-up. Plasma volume (V p), permeability (K tr), and leakage (λ tr) perfusion parameters were calculated by fitting a pharmacokinetic model to the DCE-MRI data. The contrast-enhancing (CE) volume was measured from the last dynamic phase acquired in the DCE sequence. Perfusion parameters and CE at each MRI time point were recorded along with their relative change between MRI#2 and MRI#3 (Δ32). Cox regression was used to analyze patient survival. Results At MRI#1 and at MRI#3, none of the parameters showed a significant correlation with overall survival (OS). However, at MRI#2, CE and λ tr were significantly associated with OS (p < 0.05). The relative λ tr and V p from timepoint 2 to timepoint 3 (Δ32 λ tr and Δ32 V p) were each associated with a higher hazard ratio (p < 0.05). All parameters were highly correlated, resulting in a multivariate model for OS including only CE at MRI#2 and Δ32 V p, with an R 2 of 0.89. Conclusion The change in perfusion parameter values from 1 week to 1 month following NSC-mediated therapy combined with contrast-enhancing volume may be a useful biomarker to predict overall survival in patients with recurrent high-grade glioma.

Diagnostic accuracy of automatic normalization of CBV in glioma grading using T1- weighted DCE-MRI

PurposeAim of this retrospective study was to compare diagnostic accuracy of proposed automatic normalization method to quantify the relative cerebral blood volume (rCBV) with existing contra-lateral region of interest (ROI) based CBV normalization method for glioma grading using T1-weighted dynamic contrast enhanced MRI (DCE-MRI). Material and methodsSixty patients with histologically confirmed gliomas were included in this study retrospectively. CBV maps were generated using T1-weighted DCE-MRI and are normalized by contralateral ROI based method (rCBV_contra), unaffected white matter (rCBV_WM) and unaffected gray matter (rCBV_GM), the latter two of these were generated automatically. An expert radiologist with >10years of experience in DCE-MRI and a non-expert with one year experience were used independently to measure rCBVs. Cutoff values for glioma grading were decided from ROC analysis. Agreement of histology with rCBV_WM, rCBV_GM and rCBV_contra respectively was studied using Kappa statistics and intra-class correlation coefficient (ICC). ResultThe diagnostic accuracy of glioma grading using the measured rCBV_contra by expert radiologist was found to be high (sensitivity=1.00, specificity=0.96, p<0.001) compared to the non-expert user (sensitivity=0.65, specificity=0.78, p<0.001). On the other hand, both the expert and non-expert user showed similar diagnostic accuracy for automatic rCBV_WM (sensitivity=0.89, specificity=0.87, p=0.001) and rCBV_GM (sensitivity=0.81, specificity=0.78, p=0.001) measures. Further, it was also observed that, contralateral based method by expert user showed highest agreement with histological grading of tumor (kappa=0.96, agreement 98.33%, p<0.001), however; automatic normalization method showed same percentage of agreement for both expert and non-expert user. rCBV_WM showed an agreement of 88.33% (kappa=0.76,p<0.001) with histopathological grading. ConclusionIt was inferred from this study that, in the absence of expert user, automated normalization of CBV using the proposed method could provide better diagnostic accuracy compared to the manual contralateral based approach.

Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma.

Glioblastoma and primary CNS lymphoma dictate different neurosurgical strategies; it is critical to distinguish them preoperatively. However, current imaging modalities do not effectively differentiate them. We aimed to examine the use of DWI and T1-weighted dynamic contrast-enhanced-MR imaging as potential discriminative tools. We retrospectively reviewed 18 patients with primary CNS lymphoma and 36 matched patients with glioblastoma with pretreatment DWI and dynamic contrast-enhanced-MR imaging. VOIs were drawn around the tumor on contrast-enhanced T1WI and FLAIR images; these images were transferred onto coregistered ADC maps to obtain the ADC and onto dynamic contrast-enhanced perfusion maps to obtain the plasma volume and permeability transfer constant. Histogram analysis was performed to determine the mean and relative ADCmean and relative 90th percentile values for plasma volume and the permeability transfer constant. Nonparametric tests were used to assess differences, and receiver operating characteristic analysis was performed for optimal threshold calculations. The enhancing component of primary CNS lymphoma was found to have significantly lower ADCmean (1.1 × 10-3 versus 1.4 × 10-3; P < .001) and relative ADCmean (1.5 versus 1.9; P < .001) and relative 90th percentile values for plasma volume (3.7 versus 5.0; P < .05) than the enhancing component of glioblastoma, but not significantly different relative 90th percentile values for the permeability transfer constant (5.4 versus 4.4; P = .83). The nonenhancing portions of glioblastoma and primary CNS lymphoma did not differ in these parameters. On the basis of receiver operating characteristic analysis, mean ADC provided the best threshold (area under the curve = 0.83) to distinguish primary CNS lymphoma from glioblastoma, which was not improved with normalized ADC or the addition of perfusion parameters. ADC was superior to dynamic contrast-enhanced-MR imaging perfusion, alone or in combination, in differentiating primary CNS lymphoma from glioblastoma.

Brain capillary transit time heterogeneity in healthy volunteers measured by dynamic contrast-enhanced T1 -weighted perfusion MRI.

PurposeCapillary transit time heterogeneity, measured as CTH, may set the upper limit for extraction of substances in brain tissue, e.g., oxygen. The purpose of this study was to investigate the feasibility of dynamic contrast‐enhanced T1 weighted MRI (DCE‐MRI) at 3 Tesla (T), in estimating CTH based on a gamma‐variate model of the capillary transit time distribution. In addition, we wanted to investigate if a subtle increase of the blood–brain barrier permeability can be incorporated into the model, still allowing estimation of CTH.Materials and MethodsTwenty‐three healthy subjects were scanned at 3.0T MRI system applying DCE‐MRI and using a gamma‐variate model to estimate CTH as well as cerebral blood flow (CBF), cerebral blood volume (CBV), and permeability of the blood–brain barrier, measured as the influx constant Ki. For proof of principle we also investigated three patients with recent thromboembolic events and a patient with a high grade brain tumor.ResultsIn the healthy subjects, we found a narrow symmetric delta‐like capillary transit time distribution in basal ganglia gray matter with median CTH of 0.93 s and interquartile range of 1.33 s. The corresponding residue impulse response function was compatible with the adiabatic tissue homogeneity model. In two patients with complete occlusion of the internal carotid artery and in the patient with a brain tumor CTH was increased with values up to 6 s in the affected brain tissue, with an exponential like residue impulse response function.ConclusionOur results open the possibility of characterizing brain perfusion by the capillary transit time distribution using DCE‐MRI, theoretically a determinant of efficient blood to brain transport of important substances. Level of Evidence: 2J. MAGN. RESON. IMAGING 2017;45:1809–1820

In Vivo Correlation of Glucose Metabolism, Cell Density and Microcirculatory Parameters in Patients with Head and Neck Cancer: Initial Results Using Simultaneous PET/MRI

ObjectiveTo demonstrate the feasibility of simultaneous acquisition of 18F-FDG-PET, diffusion-weighted imaging (DWI) and T1-weighted dynamic contrast-enhanced MRI (T1w-DCE) in an integrated simultaneous PET/MRI in patients with head and neck squamous cell cancer (HNSCC) and to investigate possible correlations between these parameters.Methods17 patients that had given informed consent (15 male, 2 female) with biopsy-proven HNSCC underwent simultaneous 18F-FDG-PET/MRI including DWI and T1w-DCE. SUVmax, SUVmean, ADCmean, ADCmin and K trans, k ep and v e were measured for each tumour and correlated using Spearman’s ρ.ResultsSignificant correlations were observed between SUVmean and K trans (ρ = 0.43; p ≤ 0.05); SUVmean and k ep (ρ = 0.44; p ≤ 0.05); K trans and k ep (ρ = 0.53; p ≤ 0.05); and between k ep and v e (ρ = -0.74; p ≤ 0.01). There was a trend towards statistical significance when correlating SUVmax and ADCmin (ρ = -0.35; p = 0.08); SUVmax and K trans (ρ = 0.37; p = 0.07); SUVmax and k ep (ρ = 0.39; p = 0.06); and ADCmean and v e (ρ = 0.4; p = 0.06).ConclusionSimultaneous 18F-FDG-PET/MRI including DWI and T1w-DCE in patients with HNSCC is feasible and allows depiction of complex interactions between glucose metabolism, microcirculatory parameters and cellular density.

How far is arterial spin labeling MRI from a clinical reality? Insights from arterial spin labeling comparative studies in Alzheimer's disease and other neurological disorders.

How far is arterial spin labeling MRI from a clinical reality? Insights from arterial spin labeling comparative studies in Alzheimer's disease and other neurological disorders.

Assessment of irradiated brain metastases using dynamic contrast-enhanced magnetic resonance imaging

The purpose of this study was to evaluate the effect of stereotactic radiosurgery (SRS) on cerebral metastases using the transfer constant (K trans) assessed by dynamic contrast-enhanced (DCE) MRI. Furthermore, we aimed to evaluate the ability of K trans measurements to predict midterm tumor outcomes after SRS. The study received institutional review board approval, and informed consent was obtained from all subjects. Twenty-six adult patients with a total of 34 cerebral metastases underwent T1-weighted DCE MRI in a 1.5-T magnet at baseline (prior to SRS) and 4-8 weeks after treatment. Quantitative analysis of DCE MRI was performed by generating K trans parametric maps, and region-of-interest-based measurements were acquired for each metastasis. Conventional MRI was performed at least 16 weeks after SRS to assess midterm tumor outcome using volume variation. The mean (±SD) K trans value was 0.13 ± 0.11 min(-1) at baseline and 0.08 ± 0.07 min(-1) after 4-8 weeks post-treatment (p < 0.001). The mean (±SD) total follow-up time was 7.9 ± 4.7 months. Seventeen patients (22 lesions) underwent midterm MRI. Of those, nine (41 %) lesions had progressed at the midterm follow-up. An increase in K trans after SRS was predictive of tumor progression (hazard ratio = 1.50; 95 % CI = 1.16-1.70, p < 0.001). An increase of 15 % in K trans showed a sensitivity of 78 % and a specificity of 85 % for the prediction of progression at midterm follow-up. SRS was associated with a reduction of K trans values of the cerebral metastases in the early post-treatment period. Furthermore, K trans variation as assessed using DCE MRI may be helpful to predict midterm outcomes after SRS.

Assessment of blood-brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review.

There is increasing recognition of the importance of blood–brain barrier (BBB) disruption in aging, dementia, stroke and multiple sclerosis in addition to more commonly-studied pathologies such as tumors. Dynamic contrast-enhanced MRI (DCE-MRI) is a method for studying BBB disruption in vivo. We review pathologies studied, scanning protocols and data analysis procedures to determine the range of available methods and their suitability to different pathologies. We systematically review the existing literature up to February 2014, seeking studies that assessed BBB integrity using T1-weighted DCE-MRI techniques in animals and humans in normal or abnormal brain tissues. The literature search provided 70 studies that were eligible for inclusion, involving 417 animals and 1564 human subjects in total. The pathologies most studied are intracranial neoplasms and acute ischemic strokes. There are large variations in the type of DCE-MRI sequence, the imaging protocols and the contrast agents used. Moreover, studies use a variety of different methods for data analysis, mainly based on model-free measurements and on the Patlak and Tofts models. Consequently, estimated KTrans values varied widely. In conclusion, DCE-MRI is shown to provide valuable information in a large variety of applications, ranging from common applications, such as grading of primary brain tumors, to more recent applications, such as assessment of subtle BBB dysfunction in Alzheimer's disease. Further research is required in order to establish consensus-based recommendations for data acquisition and analysis and, hence, improve inter-study comparability and promote wider use of DCE-MRI.

Using intraoperative dynamic contrast-enhanced T1-weighted MRI to identify residual tumor in glioblastoma surgery

The goal of surgery in high-grade gliomas is to maximize the resection of contrast-enhancing tumor without causing additional neurological deficits. Intraoperative MRI improves surgical results. However, when using contrast material intraoperatively, it may be difficult to differentiate between surgically induced enhancement and residual tumor. The purpose of this study was to assess the usefulness of intraoperative dynamic contrast-enhanced T1-weighted MRI to guide this differential diagnosis and test it against tissue histopathology. Preoperative and intraoperative dynamic contrast-enhanced MRI was performed in 21 patients with histopathologically confirmed WHO Grade IV gliomas using intraoperative 3-T MRI. Standardized regions of interest (ROIs) were placed manually at 2 separate contrast-enhancing areas at the resection border for each patient. Time-intensity curves (TICs) were generated for each ROI. All ROIs were biopsied and the TIC types were compared with histopathological results. Pharmacokinetic modeling was performed in the last 10 patients to confirm nonparametric TIC analysis findings. Of the 42 manually selected ROIs in 21 patients, 25 (59.5%) contained solid tumor tissue and 17 (40.5%) retained the brain parenchymal architecture but contained infiltrating tumor cells. Time-intensity curves generated from residual contrast-enhancing tumor and their preoperative counterparts were comparable and showed a quick and persistently increasing slope ("climbing type"). All 17 TICs obtained from regions that did not contain solid tumor tissue were undulating and low in amplitude, compared with those obtained from residual tumors ("low-amplitude type"). Pharmacokinetic findings using the transfer constant, extravascular extracellular volume fraction, rate constant, and initial area under the curve parameters were significantly different for the tumor mass, nontumoral regions, and surgically induced contrast-enhancing areas. Intraoperative dynamic contrast-enhanced MRI provides quick, reproducible, high-quality, and simply interpreted dynamic MR images in the intraoperative setting and can aid in differentiating surgically induced enhancement from residual tumor.

MRI‐based prediction of pulsed high‐intensity focused ultrasound effect on tissue transport in rabbit muscle

To design an algorithm for optimizing pulsed high intensity focused ultrasound (p-HIFU) treatment parameters to maximize tissue transport while minimizing thermal necrosis based on MR image guidance. P-HIFU power, duty cycle, and treatment duration were varied to generate different levels of thermal and mechanical deposition in rabbit muscle. Changes in T2-weighted and T1 contrast-enhanced (CE) signal were assessed immediately following treatment and at 24 h. Transport parameters were extracted by means of T1-weighted dynamic contrast-enhanced MRI (DCE-MRI) technique at 0 and 24-h time points. Successful p-HIFU treatment was indicated by focal hyperintensity on the T2-weighted image immediately post-treatment, suggesting increased fluid (edema), with little intensity change in CE image. After 24 h, the affected region expanded along the muscle fiber accompanied by clear hyperintensity in CE image (contrast uptake). Quantitative DCE-MRI analysis revealed statistically significant increases in both leakage rate and extracellular space, accompanied by a decrease in clearance rate. Successful p-HIFU treatment was mainly correlated to tissue heating. Tissue transport properties following treatment success would result in improved contact between drug and targets in both time and space. MRI is the key to controlling treatment by means of thermometry and also monitoring efficacy by means of T2-weighted imaging.

Quantification of Perfusion and Permeability in Multiple Sclerosis

The quantification of cerebral blood flow (CBF), cerebral blood volume (CBV), and blood-brain barrier permeability in scattered lesions in the brain is a methodological challenge. We aimed to investigate the feasibility of a 3D T1-weighted dynamic contrast-enhanced (DCE) MRI acquisition in combination with a 2-compartment modeling approach for the quantification of CBF, CBV and permeability surface area product (PS) in lesions, and normal-appearing white matter (NAWM) in patients with multiple sclerosis (MS). In all, 19 MS patients (mean age 35 years, 12 female) underwent DCE-MRI with a 3D T1-weighted spoiled gradient-echo sequence on a 3T MRI scanner. A total of 44 slices (thickness 3 mm) with an in-plane resolution of 1.7 × 1.7 mm(2) (matrix size 128 × 104), providing coverage of the whole brain, were acquired every 2.1 seconds over a total measurement time of 420 s. Data postprocessing was performed using a set of 2-compartment models with automated model selection; CBF, CBV, and PS as a measure of blood-brain barrier leakage were determined in contrast-enhancing (CE) and nonenhancing lesions as well as in NAWM. Perfusion quantification produced reasonable values in lesions as well as in NAWM. In CE lesions, CBF (22.9 (22.7) vs. 15.8 (6.7) mL/100 mL/min), CBV (1.18 (0.48) vs. 0.76 (0.19) mL/100 mL), and PS (0.98 (0.46) vs. 0.04 (0.03) mL/100 mL/min) were significantly (P < 0.001) higher than in NAWM. In nonenhancing lesions, a weakly (P < 0.05) significantly increased CBV of 1.00 (0.35) mL/100 mL, compared with NAWM, was observed. Our study demonstrates the feasibility of 3D T1-weighted DCE-MRI for the quantitative assessment of CBF, CBV, and PS in NAWM as well as in multiple MS lesions scattered throughout the brain, even without previous knowledge of their location. Quantification on the region level produced reasonable values both in lesions and in NAWM, but parameter maps would benefit from an increase in contrast-to-noise ratio. The increased values of CBF, CBV, and PS in CE lesions may reflect inflammatory activity, the heterogeneity of parameter estimates suggests a potential for lesion characterization. NAWM appears hypoperfused, this is in accordance with previous studies, but requires validation with a control group.

Quantification of cerebral hemodynamics in patients with multiple sclerosis: dynamic contrast-enhanced MRI in 3D at 3T

The quantification of cerebral hemodynamics, comprised of cerebral blood flow (CBF), cerebral blood volume (CBV) and permeability-surface area product (PS) as a measure of blood-brain barrier permeability in MS lesions in the brain is challenging. With conventional dynamic susceptibility weighted MRI, several issues with quantification arise, in particular in areas with blood-brain barrier disruption. In this study, we investigate the feasibility of a rapid 3D T1-weighted dynamic contrast-enhanced (DCE) MRI measurement, providing coverage of the entire neurocranium, in combination with a two-compartment modelling approach for the quantification of CBF, CBV and PS as a measure of blood-brain barrier leakage in contrast-enhancing (CE) lesions and in normal-appearing white matter (NAWM) in patients with multiple sclerosis (MS).