Improved reliability of perfusion estimation in dynamic susceptibility contrast MRI by using the arterial input function from dynamic contrast enhanced MRI

The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast-agent concentrations is challenging, due to uncertainty in relation to the measuredR 2 ∗ -weighted signal, signal depletion at high concentration, and partial-volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE-driven AIF with perfusion coefficients determined using a DSC-based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi-automatic AIF-selection algorithm was applied to the DSC data. The amplitude and full width at half-maximum of the AIFs were compared statistically using the Wilcoxon rank-sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi-automatic DSC-AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC-AIF. Additionally, the DCE-based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE-AIFs that were compatible with previously reported values. In conclusion, DCE-based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE-AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.

Insulin Resistance Is Associated With Reduced Capillary Permeability of Thigh Muscles in Patients With Type 2 Diabetes

CONTEXT

Insulin-mediated microvascular permeability and blood flow of skeletal muscle appears to be altered in the condition of insulin resistance. Previous studies on this effect used invasive procedures in humans or animals.

OBJECTIVE

The aim of this study was to demonstrate the feasibility of a noninvasive assessment of human muscle microcirculation via dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of skeletal muscle in patients with type 2 diabetes (T2D).

METHODS

A total of 56 participants (46 with T2D, 10 healthy controls [HC]) underwent DCE-MRI of the right thigh at 3 Tesla. The constant of the musculature's microvascular permeability (Ktrans), extravascular extracellular volume fraction (ve), and plasma volume fraction (vp) were calculated.

RESULTS

In T2D patients, skeletal muscle Ktrans was lower (HC 0.0677 ± 0.002 min-1, T2D 0.0664 ± 0.002 min-1; P = 0.042) while the homeostasis model assessment (HOMA) index was higher in patients with T2D compared to HC (HC 2.72 ± 2.2, T2D 6.11 ± 6.2; P = .011). In T2D, Ktrans correlated negatively with insulin (r = -0.39, P = .018) and HOMA index (r = -0.38, P = .020).

CONCLUSION

The results signify that skeletal muscle DCE-MRI can be employed as a noninvasive technique for the assessment of muscle microcirculation in T2D. Our findings suggest that microvascular permeability of skeletal muscle is lowered in patients with T2D and that a decrease in microvascular permeability is associated with insulin resistance. These results are of interest with regard to the impact of muscle perfusion on diabetic complications such as diabetic sarcopenia.

Prediction of Lung Shunt Fraction for Yttrium-90 Treatment of Hepatic Tumors Using Dynamic Contrast Enhanced MRI with Quantitative Perfusion Processing

There is no noninvasive method to estimate lung shunting fraction (LSF) in patients with liver tumors undergoing Yttrium-90 (Y90) therapy. We propose to predict LSF from noninvasive dynamic contrast enhanced (DCE) MRI using perfusion quantification. Two perfusion quantification methods were used to process DCE MRI in 25 liver tumor patients: Kety's tracer kinetic modeling with a delay-fitted global arterial input function (AIF) and quantitative transport mapping (QTM) based on the inversion of transport equation using spatial deconvolution without AIF. LSF was measured on SPECT following Tc-99m macroaggregated albumin (MAA) administration via hepatic arterial catheter. The patient cohort was partitioned into a low-risk group (LSF ≤ 10%) and a high-risk group (LSF > 10%). Results: In this patient cohort, LSF was positively correlated with QTM velocity |u| (r = 0.61, F = 14.0363, p = 0.0021), and no significant correlation was observed with Kety's parameters, tumor volume, patient age and gender. Between the low LSF and high LSF groups, there was a significant difference for QTM |u| (0.0760 ± 0.0440 vs. 0.1822 ± 0.1225 mm/s, p = 0.0011), and Kety's Ktrans (0.0401 ± 0.0360 vs 0.1198 ± 0.3048, p = 0.0471) and Ve (0.0900 ± 0.0307 vs. 0.1495 ± 0.0485, p = 0.0114). The area under the curve (AUC) for distinguishing between low LSF and high LSF was 0.87 for |u|, 0.80 for Ve and 0.74 for Ktrans. Noninvasive prediction of LSF is feasible from DCE MRI with QTM velocity postprocessing.

Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach

PURPOSE

To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI.

METHODS

A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy.

RESULT

The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81.

CONCLUSION

This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.

Blood-Brain Barrier Leakage Is Increased in Parkinson's Disease

Background

Blood–brain barrier (BBB) disruption has been noted in animal models of Parkinson’s disease (PD) and forms the basis of the vascular hypothesis of neurodegeneration, yet clinical studies are lacking.

Objective

To determine alterations in BBB integrity in PD, with comparison to cerebrovascular disease.

Methods

Dynamic contrast enhanced magnetic resonance images were collected from 49 PD patients, 15 control subjects with cerebrovascular disease [control positive (CP)] and 31 healthy control subjects [control negative (CN)], with all groups matched for age. Quantitative maps of the contrast agent transfer coefficient across the BBB (K^trans) and plasma volume (v_p) were produced using Patlak analysis. Differences in K^trans and v_p were assessed with voxel-based analysis as well as in regions associated with PD pathophysiology. In addition, the volume of white matter lesions (WMLs) was obtained from T₂-weighted fluid attenuation inversion recovery (FLAIR) images.

Results

Higher K^trans, reflecting higher BBB leakage, was found in the PD group than in the CN group using voxel-based analysis; differences were most prominent in the posterior white matter regions. Region of interest analysis confirmed K^trans to be significantly higher in PD than in CN, predominantly driven by differences in the substantia nigra, normal-appearing white matter, WML and the posterior cortex. WML volume was significantly higher in PD compared to CN. K^trans values and WML volume were similar in PD and CP, suggesting a similar burden of cerebrovascular disease despite lower cardiovascular risk factors.

Conclusion

These results show BBB disruption in PD.

Fully automatic quantification of fibroglandular tissue and background parenchymal enhancement with accurate implementation for axial and sagittal breast MRI protocols

PURPOSE

To propose and evaluate a fully automated technique for quantification of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE) in breast MRI.

METHODS

We propose a fully automated method, where after preprocessing, FGT is segmented in T1-weighted, nonfat-saturated MRI. Incorporating an anatomy-driven prior probability for FGT and robust texture descriptors against intensity variations, our method effectively addresses major image processing challenges, including wide variations in breast anatomy and FGT appearance among individuals. Our framework then propagates this segmentation to dynamic contrast-enhanced (DCE)-MRI to quantify BPE within the segmented FGT regions. Axial and sagittal image data from 40 cancer-unaffected women were used to evaluate our proposed method vs a manually annotated reference standard.

RESULTS

High spatial correspondence was observed between the automatic and manual FGT segmentation (mean Dice similarity coefficient 81.14%). The FGT and BPE quantifications (denoted FGT% and BPE%) indicated high correlation (Pearson's r = 0.99 for both) between automatic and manual segmentations. Furthermore, the differences between the FGT% and BPE% quantified using automatic and manual segmentations were low (mean differences: -0.66 ± 2.91% for FGT% and -0.17 ± 1.03% for BPE%). When correlated with qualitative clinical BI-RADS ratings, the correlation coefficient for FGT% was still high (Spearman's ρ = 0.92), whereas that for BPE was lower (ρ = 0.65). Our proposed approach also performed significantly better than a previously validated method for sagittal breast MRI.

CONCLUSIONS

Our method demonstrated accurate fully automated quantification of FGT and BPE in both sagittal and axial breast MRI. Our results also suggested the complexity of BPE assessment, demonstrating relatively low correlation between segmentation and clinical rating.

Real-Time Quantitative Assessment of Accuracy and Precision of Blood Volume Derived from DCE-MRI in Individual Patients During a Clinical Trial

Accuracy and precision of quantitative imaging (QI) metrics should be assessed in real time in each patient during a clinical trial to support QI-based decision-making. We developed a framework for real-time quantitative assessment of QI metrics and evaluated accuracy and precision of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI)–derived blood volume (BV) in a clinical trial for head and neck cancers. Patients underwent DCE-MRI before and after 2 weeks of radiation therapy (2wkRT). A mean as a reference value and a repeatability coefficient (RC) of BV values established from n patients in cerebellum volumes of interest (VOIs), which were normal and affected little by therapy, served as accuracy and precision measurements. The BV maps of a new patient were called accurate and precise if the values in cerebellum VOIs and the difference between the 2 scans agreed with the respective mean and RC with 95% confidence. The new data could be used to update reference values. Otherwise, the data were flagged for further evaluation before use in the trial. BV maps from 62 patients enrolled on the trial were evaluated. Mean BV values were 2.21 (±0.14) mL/100 g pre-RT and 2.22 (±0.17) mL/100 g at 2wkRT; relative RC was 15.9%. The BV maps from 3 patients were identified to be inaccurate and imprecise before use in the clinical trial. Our framework of real-time quantitative assessment of QI metrics during a clinical trial can be translated to different QI metrics and organ-sites for supporting QI-based decision-making that warrants success of a clinical trial.

Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects

Purpose

To evaluate the impact of (k,t) data sampling on the variance of tracer‐kinetic parameter (TK) estimation in high‐resolution whole‐brain dynamic contrast enhanced magnetic resonance imaging (DCE‐MRI) using digital reference objects. We study this in the context of TK model constraints, and in the absence of other constraints.

Methods

Three anatomically and physiologically realistic brain‐tumor digital reference objects were generated. Data sampling strategies included uniform and variable density; zone‐based, lattice, pseudo‐random, and pseudo‐radial; with 50‐time frames and 4‐fold to 25‐fold undersampling. In all cases, we assume a fully sampled first time frame, and prior knowledge of the arterial input function. TK parameters were estimated by indirect estimation (i.e., image‐time‐series reconstruction followed by model fitting), and direct estimation from the under‐sampled data. We evaluated methods based on the Cramér‐Rao bound and Monte‐Carlo simulations, over the range of signal‐to‐noise ratio (SNR) seen in clinical brain DCE‐MRI.

Results

Lattice‐based sampling provided the lowest SDs, followed by pseudo‐random, pseudo‐radial, and zone‐based. This ranking was consistent for the Patlak and extended Tofts model. Pseudo‐random sampling resulted in 19% higher averaged SD compared to lattice‐based sampling. Zone‐based sampling resulted in substantially higher SD at undersampling factors above 10. CRB analysis showed only a small difference between uniform and variable density for both lattice‐based and pseudo‐random sampling up to undersampling factors of 25.

Conclusion

Lattice sampling provided the lowest SDs, although the differences between sampling schemes were not substantial at low undersampling factors. The differences between lattice‐based and pseudo‐random sampling strategies with both uniform and variable density were within the range of error induced by other sources, at up to 25‐fold undersampling.

Convolutional Neural Networks for Direct Inference of Pharmacokinetic Parameters: Application to Stroke Dynamic Contrast-Enhanced MRI

Background and Purpose: The T1-weighted dynamic contrast enhanced (DCE)-MRI is an imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters characterizing microvasculature of tissues. For the present study, we propose a new machine learning (ML) based approach to directly estimate the PK parameters from the acquired DCE-MRI image-time series that is both more robust and faster than conventional model fitting.

Materials and Methods: We specifically utilize deep convolutional neural networks (CNNs) to learn the mapping between the image-time series and corresponding PK parameters. DCE-MRI datasets acquired from 15 patients with clinically evident mild ischaemic stroke were used in the experiments. Training and testing were carried out based on leave-one-patient-out cross- validation. The parameter estimates obtained by the proposed CNN model were compared against the two tracer kinetic models: (1) Patlak model, (2) Extended Tofts model, where the estimation of model parameters is done via voxelwise linear and nonlinear least squares fitting respectively.

Results: The trained CNN model is able to yield PK parameters which can better discriminate different brain tissues, including stroke regions. The results also demonstrate that the model generalizes well to new cases even if a subject specific arterial input function (AIF) is not available for the new data.

Conclusion: A ML-based model can be used for direct inference of the PK parameters from DCE image series. This method may allow fast and robust parameter inference in population DCE studies. Parameter inference on a 3D volume-time series takes only a few seconds on a GPU machine, which is significantly faster compared to conventional non-linear least squares fitting.

Low-dose T1W DCE-MRI for early time points perfusion measurement in patients with intracranial tumors: A pilot study applying the microsphere model to measure absolute cerebral blood flow

BACKGROUND

Previous studies have measured cerebral blood flow (CBF) with DSC-MRI using an "early time points" (ET) method based on microsphere theory.

PURPOSE

To develop and assess a new ET method for absolute CBF estimation using low-dose high-temporal (LDHT) T1W-DCE-MRI.

STUDY TYPE

Retrospective cohort study.

SUBJECTS

Seven patients with sporadic vestibular schwannoma (VS) who underwent test-retest imaging; one patient with glioblastoma multiforme (GBM) imaged pretreatment; and 12 neurofibromatosis type 2 (NF2) patients undergoing bevacizumab treatment, imaged pre- and 90 days posttreatment.

FIELD STRENGTH/SEQUENCE

LDHT-DCE-MRI was performed at 1.5 and 3.0T, using 3D spoiled gradient echo with phase cycling. DSC-MRI performed in one patient, using 3D echo-shifted multi-shot echo-planar imaging (PRESTO) at 3T.

ASSESSMENT

Through Monte Carlo simulations, CBF estimation using three newly developed average contrast agent concentration (AC) -based methods (ACrPK, ACrMG, ACcomb), was compared against conventional maximum gradient (MG) approaches, at varying Rician noise levels. Reproducibility and applicability of the ACcomb method was assessed in our sporadic-VS/GBM/NF2 patient cohort, respectively.

STATISTICAL TESTS

Reproducibility was measured using test-retest coefficient of variation (CoV). Pre- and posttreatment CBF values were compared using paired t-test with Bonferroni correction.

RESULTS

Monte Carlo stimulations demonstrated that AC-based methods, particularly ACcomb, offered superior accuracy to conventional MG approaches. Overall test-retest CoV using the ACcomb method was 5.76 in normal-appearing white matter (NAWM). The new ACcomb method produced gray matter/white matter CBF estimates in the NF2 patient cohort of 55.9 ± 13.9/25.8 ± 3.5 on day 0; compared with 155.6 ± 17.2/128.4 ± 29.1 for the classical MG method. There was a moderate (10% using ACcomb and ACrPK) increase in CBF of NAWM 90 days post therapy (P = 0.03 and 0.005).

DATA CONCLUSION

Our new AC-based method of CBF estimation offers excellent reproducibility, and displays more accuracy in both Monte Carlo analysis and clinical data application, than conventional MG-based approaches.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 4 J. MAGN. RESON. IMAGING 2018;48:543-557.

Differential study of DCE-MRI parameters in spinal metastatic tumors, brucellar spondylitis and spinal tuberculosis

Objective

In the present study, spinal metastatic tumors, brucellar spondylitis and spinal tuberculosis were quantitatively analyzed using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to assess the value of DCE-MRI in the differential diagnosis of these diseases.

Methods

Patients with brucellar spondylitis, spinal tuberculosis or a spinal metastatic tumor (30 cases of each) received conventional MRI and DCE-MRI examination. The volume transfer constant (K^trans), rate constant (K_ep), extravascular extracellular volume fraction (V_e) and plasma volume fraction (V_p) of the diseased vertebral bodies were measured on the perfusion parameter map, and the differences in these parameters between the patients were compared.

Results

For pathological vertebrae in cases of spinal metastatic tumor, brucellar spondylitis and spinal tuberculosis, respectively, the K^trans values (median ± quartile pitch) were 0.989±0.014, 0.720±0.011 and 0.317±0.005 min^–1; the K_ep values were 2.898±0.055, 1.327±0.017 and 0.748±0.006 min^–1; the V_e values were 0.339±0.008, 0.542±0.013 and 0.428±0.018; the V_p values were 0.048±0.008, 0.035±0.004 and 0.028±0.009; the corresponding H values were 50.25 (for K^trans), 52.47 (for K_ep), 48.33 (for V_e) and 46.56 (for V_p), and all differences were statistically significant (two-sided P<0.05).

Conclusions

The quantitative analysis of DCE-MRI has a certain value in the differential diagnosis of spinal metastatic tumor, brucellar spondylitis and spinal tuberculosis.

Estimating the arterial input function from dynamic contrast-enhanced MRI data with compensation for flow enhancement (II): Applications in spine diagnostics and assessment of crohn's disease

BACKGROUND

Pharmacokinetic (PK) models can describe microvascular density and integrity. An essential component of PK models is the arterial input function (AIF) representing the time-dependent concentration of contrast agent (CA) in the blood plasma supplied to a tissue.

PURPOSE/HYPOTHESIS

To evaluate a novel method for subject-specific AIF estimation that takes inflow effects into account.

STUDY TYPE

Retrospective study.

SUBJECTS

Thirteen clinical patients referred for spine-related complaints; 21 patients from a study into luminal Crohn's disease with known Crohn's Disease Endoscopic Index of Severity (CDEIS).

FIELD STRENGTH/SEQUENCE

Dynamic fast spoiled gradient echo (FSPGR) at 3T.

ASSESSMENT

A population-averaged AIF, AIFs derived from distally placed regions of interest (ROIs), and the new AIF method were applied. Tofts' PK model parameters (including v_p and K^trans ) obtained with the three AIFs were compared. In the Crohn's patients K^trans was correlated to CDEIS.

STATISTICAL TESTS

The median values of the PK model parameters from the three methods were compared using a Mann-Whitney U-test. The associated variances were statistically assessed by the Brown-Forsythe test. Spearman's rank correlation coefficient was computed to test the correlation of K^trans to CDEIS.

RESULTS

The median v_p was significantly larger when using the distal ROI approach, compared to the two other methods (P < 0.05 for both comparisons, in both applications). Also, the variances in v_p were significantly larger with the ROI approach (P < 0.05 for all comparisons). In the Crohn's disease study, the estimated K^trans parameter correlated better with the CDEIS (r = 0.733, P < 0.001) when the proposed AIF was used, compared to AIFs from the distal ROI method (r = 0.429, P = 0.067) or the population-averaged AIF (r = 0.567, P = 0.011).

DATA CONCLUSION

The proposed method yielded realistic PK model parameters and improved the correlation of the K^trans parameter with CDEIS, compared to existing approaches.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2018;47:1197-1204.

Estimating the arterial input function from dynamic contrast-enhanced MRI data with compensation for flow enhancement (I): Theory, method, and phantom experiments

BACKGROUND

The arterial input function (AIF) represents the time-dependent arterial contrast agent (CA) concentration that is used in pharmacokinetic modeling.

PURPOSE

To develop a novel method for estimating the AIF from dynamic contrast-enhanced (DCE-) MRI data, while compensating for flow enhancement.

STUDY TYPE

Signal simulation and phantom measurements.

PHANTOM MODEL

Time-intensity curves (TICs) were simulated for different numbers of excitation pulses modeling flow effects. A phantom experiment was performed in which a solution (without CA) was passed through a straight tube, at constant flow velocity.

FIELD STRENGTH/SEQUENCE

Dynamic fast spoiled gradient echo (FSPGRs) at 3T MRI, both in the simulations and in the phantom experiment. TICs were generated for a duration of 373 seconds and sampled at intervals of 1.247 seconds (300 timepoints).

ASSESSMENT

The proposed method first estimates the number of pulses that spins have received, and then uses this knowledge to accurately estimate the CA concentration.

STATISTICAL TESTS

The difference between the median of the estimated number of pulses and the true value was determined, as well as the interquartile range (IQR) of the estimations. The estimated CA concentrations were evaluated in the same way. The estimated number of pulses was also used to calculate flow velocity.

RESULTS

The difference between the median estimated and reference number of pulses varied from -0.005 to -1.371 (corresponding IQRs: 0.853 and 48.377) at true values of 10 and 180 pulses, respectively. The difference between the median estimated CA concentration and the reference value varied from -0.00015 to 0.00306 mmol/L (corresponding IQRs: 0.01989 and 1.51013 mmol/L) at true values of 0.5 and 8.0 mmol/l, respectively, at an intermediate value of 100 pulses. The estimated flow velocities in the phantom were within 10% of the reference value.

DATA CONCLUSION

The proposed method accurately corrects the MRI signal affected by the inflow effect.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1190-1196.

Simultaneous measurement of T1 /B1 and pharmacokinetic model parameters using active contrast encoding (ACE)-MRI

The aim of this study was to assess the feasibility of combining dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) with the measurement of the radiofrequency (RF) transmit field B₁ and pre-contrast longitudinal relaxation time T₁₀ . A novel approach has been proposed to simultaneously estimate B₁ and T₁₀ from a modified DCE-MRI scan that actively encodes the washout phase of the curve with different amounts of T₁ and B₁ weighting using multiple flip angles and repetition times, hence referred to as active contrast encoding (ACE)-MRI. ACE-MRI aims to simultaneously measure B₁ and T₁₀ , together with contrast kinetic parameters, such as the transfer constant K^trans , interstitial space volume fraction v_e and vascular space volume fraction v_p . The proposed method was tested using numerical simulations and in vivo studies with mouse models of breast cancer implanted in the flank and mammary fat pad, and glioma in the brain. In the numerical simulation study with a signal-to-noise ratio of 10, both B₁ and T₁₀ were estimated accurately with errors of 5.1 ± 3.5% and 12.3 ± 8.8% and coefficients of variation (CV) of 14.9 ± 8.6% and 15.0 ± 5.0%, respectively. Using the same ACE-MRI data, the kinetic parameters K^trans , v_e and v_p were also estimated with errors of 14.2 ± 8.3% (CV = 13.5 ± 4.6%), 14.7 ± 9.9% (CV = 13.3 ± 4.5%) and 14.0 ± 9.3% (CV = 14.0 ± 4.5%), respectively. For the in vivo tumor data from 11 mice, voxel-wise comparisons between ACE-MRI and DCE-MRI methods showed that the mean differences for the five parameters were as follows: ΔK^trans  = 0.006 (/min), Δv_e  = 0.016, Δv_p  = 0.000, ΔB₁  = -0.014 and ΔT₁  = -0.085 (s), which suggests a good agreement between the two methods. When compared with separately measured B₁ and T₁₀ , and DCE-MRI estimated kinetic parameters as a reference, the mean relative errors of ACE-MRI estimation were B₁  = -0.3%, T₁₀  = -8.5%, K^trans  = 11.4%, v_e  = 14.5% and v_p  = 4.5%. This proof-of-concept study demonstrates that the proposed ACE-MRI method can be used to estimate B₁ and T₁₀ , together with contrast kinetic model parameters.

Automatic renal segmentation for MR urography using 3D-GrabCut and random forests

PURPOSE

To introduce and evaluate a fully automated renal segmentation technique for glomerular filtration rate (GFR) assessment in children.

METHODS

An image segmentation method based on iterative graph cuts (GrabCut) was modified to work on time-resolved 3D dynamic contrast-enhanced MRI data sets. A random forest classifier was trained to further segment the renal tissue into cortex, medulla, and the collecting system. The algorithm was tested on 26 subjects and the segmentation results were compared to the manually drawn segmentation maps using the F1-score metric. A two-compartment model was used to estimate the GFR of each subject using both automatically and manually generated segmentation maps.

RESULTS

Segmentation maps generated automatically showed high similarity to the manually drawn maps for the whole-kidney (F1 = 0.93) and renal cortex (F1 = 0.86). GFR estimations using whole-kidney segmentation maps from the automatic method were highly correlated (Spearman's ρ = 0.99) to the GFR values obtained from manual maps. The mean GFR estimation error of the automatic method was 2.98 ± 0.66% with an average segmentation time of 45 s per patient.

CONCLUSION

The automatic segmentation method performs as well as the manual segmentation for GFR estimation and reduces the segmentation time from several hours to 45 s. Magn Reson Med 79:1696-1707, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Apparent diffusion coefficient in estrogen receptor-positive and lymph node-negative invasive breast cancers at 3.0T DW-MRI: A potential predictor for an oncotype Dx test recurrence score

PURPOSE

To measure the apparent diffusion coefficient (ADC) values in estrogen receptor-positive (ER+) and axillary lymph node-negative (LN-) invasive breast cancer and investigate the correlation of ADC with Oncotype Dx test recurrence scores (ODxRS).

MATERIALS AND METHODS

This was a Health Insurance Portability and Accountability Act (HIPAA)-compliant single-site retrospective study. Patients underwent preoperative 3.0T MRI scans with additional diffusion-weighted imaging sequential scans (b = 0, 600 and b = 0, 1000 s/mm² ) from January 2011 to 2013. The study population included 31 ER+/LN- invasive breast cancers, which underwent ODxRS genomic testing. ADC₆₀₀ and ADC₁₀₀₀ parametric maps were generated, and ADC values were calculated from a user-drawn region of interest. ODxRS predicts 10-year recurrence risk in individual patients: low (RS <18), intermediate (RS: 18-30), or high (RS >30). All breast lesions, including subgroups of invasive ductal carcinoma (IDC) lesions and mass-only lesions were dichotomized by RS scores, low-risk versus intermediate/high-risk, and statistical analysis was performed using Mann-Whitney's test (statistical significance at P < 0.05) and receiver operating characteristic (ROC) curves. Multivariate analysis was also performed.

RESULTS

Invasive breast cancers, when scored as low-risk by ODxRS, had significantly higher ADC values compared with intermediate/high-risk lesions for both ADC₆₀₀ (P = 0.007) and ADC₁₀₀₀ (P = 0.008) mean values. This was true both when analyzing only mass-lesions (P = 0.03 and 0.01) or only IDCs (P = 0.001 and 0.009).

CONCLUSION

Preliminary findings suggest that lesion ADC values correlate with recurrence risk likelihood stratified using ODxRS. Hence, ADC is a potential surrogate biomarker for tumor aggressiveness.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;47:401-409.

Multiresolution imaging using golden angle stack-of-stars and compressed sensing for dynamic MR urography

PURPOSE

To develop a novel multiresolution MRI methodology for accurate estimation of glomerular filtration rate (GFR) in vivo.

MATERIALS AND METHODS

A three-dimensional golden-angle radial stack-of-stars (SoS) trajectory was used for data acquisition on a 3 Tesla MRI scanner. Multiresolution reconstruction and analysis was performed using arterial input function reconstructed at 1-s. temporal resolution and renal dynamic data reconstructed using compressed sensing (CS) with 4-s temporal resolution. The method was first validated using simulations and the clinical utility of the technique was evaluated by comparing the GFR estimates from the proposed method to the estimated GFR (eGFR) obtained from serum creatinine for 10 subjects.

RESULTS

The 4-s temporal resolution CS images minimized streaking artifacts and noise while the 1-s temporal resolution AIF minimized errors in GFR estimates. A paired t-test showed that there was no statistically significant difference between MRI based total GFR values and serum creatinine based eGFR estimates (P = 0.92).

CONCLUSION

We have demonstrated the feasibility of multiresolution MRI using a golden angle radial stack-of-stars scheme to accurately estimate GFR as well as produce diagnostic quality dynamic images in vivo.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;46:303-311.

Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation

PURPOSE

To determine whether dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) characteristics of the breast tumor and background parenchyma can distinguish molecular subtypes (ie, luminal A/B or basal) of breast cancer.

MATERIALS AND METHODS

In all, 84 patients from one institution and 126 patients from The Cancer Genome Atlas (TCGA) were used for discovery and external validation, respectively. Thirty-five quantitative image features were extracted from DCE-MRI (1.5 or 3T) including morphology, texture, and volumetric features, which capture both tumor and background parenchymal enhancement (BPE) characteristics. Multiple testing was corrected using the Benjamini-Hochberg method to control the false-discovery rate (FDR). Sparse logistic regression models were built using the discovery cohort to distinguish each of the three studied molecular subtypes versus the rest, and the models were evaluated in the validation cohort.

RESULTS

On univariate analysis in discovery and validation cohorts, two features characterizing tumor and two characterizing BPE were statistically significant in separating luminal A versus nonluminal A cancers; two features characterizing tumor were statistically significant for separating luminal B; one feature characterizing tumor and one characterizing BPE reached statistical significance for distinguishing basal (Wilcoxon P < 0.05, FDR < 0.25). In discovery and validation cohorts, multivariate logistic regression models achieved an area under the receiver operator characteristic curve (AUC) of 0.71 and 0.73 for luminal A cancer, 0.67 and 0.69 for luminal B cancer, and 0.66 and 0.79 for basal cancer, respectively.

CONCLUSION

DCE-MRI characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1017-1027.

Separation of benign and malignant breast lesions using dynamic contrast enhanced MRI in a biopsy cohort

PURPOSE

To assess the diagnostic utility of contrast kinetic analysis for breast lesions and background parenchyma of women undergoing MRI-guided biopsies, for whom standard clinical analysis had failed to separate benign and malignant lesions.

MATERIALS AND METHODS

This study included 115 women who had indeterminate lesions based on routine diagnostic breast MRI exams and underwent an MRI (3 Tesla) -guided biopsy of one or more lesions suspicious for breast cancer. Breast dynamic contrast-enhanced (DCE)-MRI was performed using a radial stack-of-stars three-dimensional spoiled gradient echo pulse sequence and modified k-space weighted image contrast image reconstruction. Contrast kinetic model analysis was conducted to characterize the contrast enhancement patterns measured in lesions and background parenchyma (BP). The transfer rate (K^trans ), interstitial volume fraction (v_e ), and vascular volume fraction (v_p ) estimated from the lesion and BP were used to separate malignant from benign lesions.

RESULTS

The patients with malignant lesions had significantly (P < 0.05) higher median lesion-K^trans (0.081 min^-1 ), higher median BP-K^trans (0.032 min^-1 ), and BP-v_p (0.020) than those without malignant lesions (0.056 min^-1 , 0.017 min^-1 and 0.012, respectively). The area under the receiver operating characteristic curve (AUC) of the BP-K^trans (0.687) was highest among the single parameters and higher than that of the lesion-K^trans (0.664). The combined logistic regression model of lesion-K^trans , lesion-v_e , BP-K^trans , BP-v_e , and BP-v_p had the highest AUC of 0.730.

CONCLUSION

Our results suggest that the contrast kinetic analysis of DCE-MRI data can be used to differentiate the malignant lesions from the benign and high-risk lesions among the indeterminate breast lesions recommended for MRI-guided biopsy exams.

LEVEL OF EVIDENCE

3 J. MAGN. RESON. IMAGING 2017;45:1385-1393.

Diagnostic accuracy of intracellular uptake rates calculated using dynamic Gd-EOB-DTPA-enhanced MRI for hepatic fibrosis stage

PURPOSE

To assess the diagnostic accuracy of intracellular uptake rates (K_i ), and other quantitative pharmacokinetic (PK) parameters, for hepatic fibrosis stage; to compare this accuracy with a previously published semiquantitative metric, contrast enhancement index (CEI); and to assess variability of these parameters between liver regions.

MATERIALS AND METHODS

This was a case-control study design. Dynamic Gd-EOB-DTPA-enhanced 1.5T magnetic resonance imaging (MRI) was performed prospectively in 22 subjects with varying known stages of hepatic fibrosis. PK parameters and CEI were derived from the whole livers and from three fixed regions of interest (ROIs) in all subjects. Spearman rank correlation coefficients were computed to assess the relationship between fibrosis stages and each parameter. Receiver operating characteristic (ROC) curves were constructed to discriminate severe fibrosis (stages 3-4) from nonsevere fibrosis (stages 0-2). The coefficient of variation (CV) was calculated to assess variability in parameters between ROIs.

RESULTS

K_i and fibrosis stage were significantly correlated (R = -0.55, 95% confidence interval [CI] [-0.79, -0.14], P = 0.01). Area under ROC curve (AUC) in distinguishing severe from nonsevere fibrosis for K_i was 0.84 (95% CI [0.65,1.00]), and for CEI was 0.64 (95% CI [0.39, 0.89]) (P = 0.0248). CV for K_i and CEI were 33.4 and 5.8, respectively. The only other parameter in the PK model having significant correlation with fibrosis stage was absolute arterial blood flow (F_a ) (R = -0.48, 95% CI [-0.75,-0.05], P = 0.03).

CONCLUSION

Hepatocyte intracellular uptake rate, K_i , derived from dynamic contrast-enhanced MRI, correlates with fibrosis stage and may contribute to a noninvasive biomarker of hepatic fibrosis.

LEVEL OF EVIDENCE

2 J. Magn. Reson. Imaging 2017;45:1177-1185.

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.

Renal DCE-MRI Model Selection Using Bayesian Probability Theory

The goal of this work was to demonstrate the utility of Bayesian probability theory-based model selection for choosing the optimal mathematical model from among 4 competing models of renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. DCE-MRI data were collected on 21 mice with high (n = 7), low (n = 7), or normal (n = 7) renal blood flow (RBF). Model parameters and posterior probabilities of 4 renal DCE-MRI models were estimated using Bayesian-based methods. Models investigated included (1) an empirical model that contained a monoexponential decay (washout) term and a constant offset, (2) an empirical model with a biexponential decay term (empirical/biexponential model), (3) the Patlak–Rutland model, and (4) the 2-compartment kidney model. Joint Bayesian model selection/parameter estimation demonstrated that the empirical/biexponential model was strongly favored for all 3 cohorts, the modeled DCE signals that characterized each of the 3 cohorts were distinctly different, and individual empirical/biexponential model parameter values clearly distinguished cohorts of low and high RBF from one another. The Bayesian methods can be readily extended to a variety of model analyses, making it a versatile and valuable tool for model selection and parameter estimation.

Diagnostic accuracy of diffusion-weighted MRI for differentiation of cervical cancer and benign cervical lesions at 3.0T: Comparison with routine MRI and dynamic contrast-enhanced MRI

PURPOSE

To compare the diagnostic accuracy of routine magnetic resonance imaging (MRI) (T1 WI and T2 WI), diffusion-weighted MRI (DWI), and DCE-MRI (dynamic contrast-enhanced MRI) at 3.0T for differentiation of cervical cancer and benign cervical lesions.

MATERIALS AND METHODS

A cohort of 75 cervical cancer patients, 26 cervical leiomyoma patients, 22 patients with cervical polyps consecutively underwent pelvic MRI scanning on a 3T MR unit. Two radiologists independently evaluated images at three imaging settings; routine MRI alone, DWI combined with routine MRI (DWI+routine MRI), and DCE-MRI. The apparent diffusion coefficients (ADCs) were calculated from b 0, 600 s/mm(2) and b 0, 1000 s/mm(2).

RESULTS

DWI+routine MRI was significantly better than routine MRI and obtained high accuracy (0.95); the diagnostic performance was not significantly different between DWI+routine MRI and DCE-MRI. Reader agreement was excellent for both DWI+routine MRI (κ, 0.90) and DCE-MRI (κ, 0.92). The ADCs of cervical cancer were significantly lower than those of benign cervical lesions at both ADC maps (P = 0.0001). The diagnostic accuracy was not different at both ADC maps (P = 0.375).

CONCLUSION

For differentiation of cervical cancer and benign cervical lesions, unenhanced MRI with combined diffusion-weighted and routine MRI (DWI+routine MRI) at 3T can provide accurate information and may be preferable to DCE.

Evaluation of gynecologic cancer with MR imaging, 18F-FDG PET/CT, and PET/MR imaging

MR imaging and (18)F-FDG PET/CT play central and complementary roles in the care of patients with gynecologic cancer. Because treatment often requires combinations of surgery, radiotherapy, and chemotherapy, imaging is central to triage and to determining prognosis. This article reviews the use of the 2 imaging modalities in the initial evaluation of 3 common cancers: uterine cervical, uterine endometrial, and epithelial ovarian. Imaging features that affect management are highlighted, as well as the relative strengths and weaknesses of the 2 modalities. Use of imaging after initial therapy to assess for recurrence and to plan salvage therapy is described. Newer functional and molecular techniques in MR imaging and PET are evaluated. Finally, we describe our initial experience with PET/MR imaging, an emerging technology that may prove to be a mainstay in personalized gynecologic cancer care.

Binswanger's disease: toward a diagnosis agreement and therapeutic approach

Binswanger's disease (BD) is a progressive form of cerebral small vessel disease affecting the white matter and other subcortical structures. Clinical and imaging characteristics, neuropsychological profile and cerebrospinal fluid analysis aid in making the diagnosis. BD shares features of other small vessel diseases and degenerative neurological conditions, which makes diagnosis difficult. However, with recent developments in MRI methods and serum/cerebrospinal fluid biomarkers, we have gained a greater understanding of the complex pathophysiology of the disease that will guide us to a more certain diagnosis. There is growing evidence that the white matter injury in BD is related to endothelial dysfunction with a secondary inflammatory response leading to breakdown of the neurovascular unit. This review summarizes current and future research directions, including pathophysiological mechanisms and potential therapeutic approaches.

Association of overall survival in patients with newly diagnosed glioblastoma with contrast-enhanced perfusion MRI: Comparison of intraindividually matched T1 - and T2 (*) -based bolus techniques

BACKGROUND

To compare intraindividual dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE) MR perfusion parameters and determine the association of DCE parameters with overall survival (OS) with the established predictive DSC parameter cerebral blood volume (CBV) in patients with newly diagnosed glioblastoma.

METHODS

Perfusion data were analyzed retrospectively, and included scans performed preoperatively at 3.0 Tesla in 37 patients (25 males, 12 females, 39-83 years, median 65) later diagnosed with glioblastoma. All patients received standard treatment consisting of surgery and radiochemotherapy. Images were spatially coregistered and maximum region of interest-based DCE and DSC parameter measurements compared and thresholds identified using multivariate linear regression, Pearson's correlation coefficients and using receiver operating characteristic analysis. Survival analysis was performed using Kaplan-Meier curves.

RESULTS

While both, elevated volume transfer constant (K(trans) ) (>0.29 min(-1) ; P = 0.041) and CBV (>23.7 mL/100 mL; P < 0.001) were significantly associated with OS, elevated CBV was associated with worse OS compared with elevated K(trans) . K(trans) was significantly correlated with the leakage correction factor K2 but not with CBV.

CONCLUSION

The combined use of DSC and DCE MR perfusion may provide additional information of prognostic value for glioblastoma patient survival prediction. As K(trans) was not tightly coupled to CBV, both parameters may reflect different stages in the pathogenetic sequence of glioblastoma growth.

Performance of perfusion-weighted Fourier decomposition MRI for detection of chronic pulmonary emboli

PURPOSE

To evaluate the test performance of perfusion-weighted Fourier-decomposition (pw-FD) magnetic resonance imaging (MRI) in comparison to dynamic contrast-enhanced (DCE)-MRI as a reference standard in patients with known or suspected chronic pulmonary embolism (PE).

MATERIALS AND METHODS

In 64 patients, chronic PE was ruled out or confirmed by DCE-MRI using a time-resolved angiography with stochastic trajectories (TWIST) sequence in one breath-hold. Pw-FD-MRI was performed using a 2D fast low-angle shot (FLASH) sequence in free-breathing. After a nonrigid image registration, FD was applied to generate pw-images. Lungs were scored by two radiologists (2 and 12 years of lung MRI experience) visually for each lobe and segment for hypoperfused areas. For intra- and interobserver variability, the MR images were analyzed 2 months after the first analysis, blinded to the results of the first reader.

RESULTS

PE was diagnosed by DCE-MRI in 39 patients. For the pw-FD MRI sensitivity, specificity, accuracy, and positive and negative predictive value for diagnosis of PE were 100%, 95%, 98%, 98%, and 100% on a per-patient basis, 94%, 94%, 94%, 95%, 94% on a per-lobe basis, and 82%, 92%, 88%, 88%, 88% on a segmental basis, respectively. Detection of subsegmental and segmental hypoperfusion using pw-FD MRI showed a moderate agreement with DCE-MRI (kappa of 0.68; 95% confidence interval: 0.64; 0.72).

CONCLUSION

Pw-FD of the lung is a feasible test to diagnose chronic PE on a per-patient level during free-breathing without the use of ionizing radiation or contrast agents.

Dynamic contrast enhanced MR imaging for rectal cancer response assessment after neo-adjuvant chemoradiation

BACKGROUND

Patient selection for organ sparing treatment after good response to neo-adjuvant chemoradiation (CRT) for locally advanced rectal cancer is challenging as no optimal restaging modality is available after CRT. In this study, we assessed the value of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for rectal cancer pathological response prediction.

METHODS

In 51 patients with locally advanced rectal cancer, the tumor volume and volume transfer constant (Ktrans) were obtained at 3 Tesla before CRT and surgery. The predictive potential for pathological complete response (pCR) and good response (GR) was assessed. GR was defined as pCR and near-pCR based on the tumor regression grade.

RESULTS

The GR group consisted of 10 patients (19.6%) with six pCR (11.8%). Both the post-CRT tumor volume and post-CRT Ktrans values and the relative change in volume (ΔVolume) and Ktrans (ΔKtrans) were predictive for pathological response. ΔKtrans showed the best predictive potential with a positive predictive value (PPV) of 100% for GR using a cutoff value of 32% reduction in Ktrans. For pCR the best PPV was 80% with a multiparameter model containing ΔVolume and ΔKtrans.

CONCLUSION

DCE-MRI has predictive potential for pathological response after CRT in rectal cancer with the relative ΔKtrans being the most predictive parameter.

Regional analysis of femoral head perfusion following displaced fractures of the femoral neck

PURPOSE

To assess regional variations in the arterial and venous blood supply to the femoral head following displaced fracture of the femoral neck using dynamic contrast enhanced (DCE)-MRI quadrant analysis.

MATERIALS AND METHODS

A total of 27 subjects with displaced femoral neck fractures were enrolled in the study. Quadrant specific DCE-MRI perfusion analysis was performed on a 1.5 Tesla MRI scanner. Simultaneous imaging of control and displaced fractured hips was done for comparison.

RESULTS

Quadrant specific decreases were found in the arterial (A (0.52 versus 0.27; P = 5.7E-13), Akep (1.0/min(-1) versus 0.41/min(-1) ; P = 1.3E-9) and venous (kel (0.05/min(-1) versus -0.02/min(-1) ; P = 5.1E-5) supply to the femoral head between control and injured sides using a two-factor analysis of variance test. The fractional perfusion (initial area under the curve) in the supero/inferolateral quadrants was 49% min/54% min, in the supero/inferomedial quadrants was 43% min/46% min and for the total femoral head was 39% min on the fracture versus control sides.

CONCLUSION

Quadrant specific decreases in arterial and venous perfusion on the fracture side were observed when compared with control.

Value of whole body MRI and dynamic contrast enhanced MRI in the diagnosis, follow-up and evaluation of disease activity and extent in multiple myeloma

PURPOSE

To evaluate the significance of dynamic contrast enhanced MRI (DCE-MRI) and whole body MRI (WB-MRI) in the diagnosis, prognosis and assessment of therapy for patients with monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma (MM).

MATERIALS AND METHODS

The retrospective study includes 219 patients providing 463 WB-MRI and DCE-MRI investigations for the subgroups MGUS (n=70), MM active disease (n=126; this includes 70 patients with new diagnosis of MM, according to the International Staging System (ISS): 41.4% ISS stage I, 20.0% ISS stage II, 7.1% ISS stage III, 31.4% insufficient for staging; and 56 patients with '(re-)active disease': 16.07% relapse, 32.14% progressive disease and 51.79% stable disease) and MM remission (n=23; 60.87% complete remission, 17.39% very good partial remission and 21.74% partial remission). Investigations of patients with hereditary multiple exostoses (n=5), neurofibromatosis (n=7) and healthy persons (n=9) were added as control subjects (n=21). WB-MRI evaluation was done by evaluating thirteen skeletal regions, providing a 'skeletal score'. DCE-MRI images of the spine, were analyzed with regions-of-interest and time-intensity-curves (TIC).

RESULTS

All TIC parameters can significantly differentiate between the predefined subgroups (p<0.001). One hundred days after autologous stem cell transplantation a 75% decrease of the slope wash-in value (p<0.001) can be seen. A cubic regression trend between 'skeletal score' and slope wash-in (adj.R(2)=0.412) could demonstrate a significant increase bone marrow perfusion if MM affects more than 10 skeletal regions (p<0.001), associated with a poorer prognosis (p<0.001).

CONCLUSION

DCE-MRI evaluation of the spine is useful for diagnosis of MM, follow-up after stem cell transplantation and evaluation of disease activity. A combined evaluation with WB-MRI and DCE-MRI provides additional micro-vascular information on the morphologic lesions and could help categorize patients with MM in two different groups to offer useful therapeutic and prognostic advise.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Simultaneous magnetic resonance angiography and perfusion (MRAP) measurement: initial application in lower extremity skeletal muscle

PURPOSE

To obtain a simultaneous 3D magnetic resonance angiography and perfusion (MRAP) using a single acquisition and to demonstrate MRAP in the lower extremities. A time-resolved contrast-enhanced exam was used in MRAP to simultaneously acquire a contrast-enhanced MR angiography (MRA) and dynamic contrast-enhanced (DCE) perfusion, which currently requires separate acquisitions and thus two contrast doses. MRAP can be used to assess large and small vessels in vascular pathologies such as peripheral arterial disease.

MATERIALS AND METHODS

MRAP was performed on 10 volunteers following unilateral plantar flexion exercise (one leg exercised and one rested) on two separate days. Data were acquired after administration of a single dose of contrast agent using an optimized sampling strategy, parallel imaging, and partial-Fourier acquisition to obtain a high spatial resolution, 3D-MRAP frame every 4 seconds. Two radiologists assessed MRAs for image quality, a signal-to-noise ratio (SNR) analysis was performed, and pharmacokinetic modeling yielded perfusion (K(trans) ).

RESULTS

MRA images had high SNR and radiologist-assessed diagnostic quality. Mean K(trans) ± standard error were 0.136 ± 0.009, 0.146 ± 0.012, and 0.191 ± 0.012 min(-1) in the resting tibialis anterior, gastrocnemius, and soleus, respectively, which significantly increased with exercise to 0.291 ± 0.018, 0.270 ± 0.019, and 0.338 ± 0.022 min(-1) . Bland-Altman analysis showed good repeatability.

CONCLUSION

MRAP provides simultaneous high-resolution MRA and quantitative DCE exams to assess large and small vessels with a single contrast dose. Application in skeletal muscle shows quantitative, repeatable perfusion measurements, and the ability to measure physiological differences.

Embolization therapy for benign prostatic hyperplasia: influence of embolization particle size on gland perfusion

PURPOSE

To assess the influence of embolic size on the therapy response of prostatic arterial embolization (PAE) based on perfusional changes seen on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Twelve beagles underwent PAE, four dogs with each particle size: A: 100-300 μm; B: 300-500 μm; and C: 500-700 μm. Prior to and 1 month after the embolization all dogs underwent prostate DCE MRI.

RESULTS

After embolization, time to maximal perfusion intensity for prostate parenchyma increased in B (188 vs. 135 sec, P = 0.023) and C (200 vs. 120 sec, P = 0.001), while it did not change for A (139 vs. 124 sec, P = 0.39). The maximal relative intensity increased after embolization in C (3.84 vs. 2.38, P < 0.001), while it did not change for A (2.50 vs. 2.44, P = 0.36) and B (3.23 vs. 2.9, P = 0.21). The extent of visualized intraprostatic urethral wall increased after embolization in B compared with A and C, 239.5 ± 138.1% vs. 56.1 ± 34.3, P = 0.04. Enhancement changes correlated with prostate volume changes: prostate volumes in A decreased less as compared with B and C (77 ± 34% vs. 56 ± 14%), P = 0.02.

CONCLUSION

The enhancement and morphological data are useful to monitor response to therapy after embolization. Embolization with 300-500 and 500-700 μm particle may provide better results than with 100-300 μm particles in a canine model.

Imaging of subacute blood-brain barrier disruption after methadone overdose

BACKGROUND

Methadone intoxication can cause respiratory depression, leading to hypoxia with subsequent coma and death. Delayed postanoxic leukoencephalopathy (DAL) has been reported with intoxication by carbon monoxide, narcotics, and other toxins.

OBJECTIVE

To investigate the metabolic derangement of the white matter (WM) and blood-brain barrier (BBB) after DAL caused by methadone overdose.

DESIGN, SETTING, AND PATIENTS

Case report of 2 patients with DAL after a single dose of "diverted" methadone used for pain control.

RESULTS

In both cases brain magnetic resonance imaging (MRI) revealed initial extensive bilateral restricted diffusion lesions within the WM. Follow-up MRI using proton magnetic resonance spectroscopic imaging ((1) H-MRSI) showed markedly lower N-acetylaspartate and higher choline within the WM. BBB permeability, calculated by Patlak graphical analysis of MRI T1 data obtained after contrast agent injection, showed disruption of the BBB within the WM lesions, which persisted longer than a year in 1 patient. Neuropsychological evaluation showed executive dysfunction in both patients. After 1 year, one patient recovered whereas the second remained impaired.

CONCLUSIONS

Methadone overdose can cause DAL with profound disturbances of neural metabolism and the BBB. The time course of these disturbances can be monitored with MR methods.