Identifiability of spatiotemporal tissue perfusion models.

Objective.Standard models for perfusion quantification in DCE-MRI produce a bias by treating voxels as isolated systems. Spatiotemporal models can remove this bias, but it is unknown whether they are fundamentally identifiable. The aim of this study is to investigate this question in silico using one-dimensional toy systems with a one-compartment blood flow model and a two-compartment perfusion model.Approach.For each of the two models, identifiability is explored theoretically and in-silico for three systems. Concentrations over space and time are simulated by forward propagation. Different levels of noise and temporal undersampling are added to investigate sensitivity to measurement error. Model parameters are fitted using a standard gradient descent algorithm, applied iteratively with a stepwise increasing time window. Model fitting is repeated with different initial values to probe uniqueness of the solution. Reconstruction accuracy is quantified for each parameter by comparison to the ground truth.Main results.Theoretical analysis shows that flows and volume fractions are only identifiable up to a constant, and that this degeneracy can be removed by proper choice of parameters. Simulations show that in all cases, the tissue concentrations can be reconstructed accurately. The one-compartment model shows accurate reconstruction of blood velocities and arterial input functions, independent of the initial values and robust to measurement error. The two-compartmental perfusion model was not fully identifiable, showing good reconstruction of arterial velocities and input functions, but multiple valid solutions for the perfusion parameters and venous velocities, and a strong sensitivity to measurement error in these parameters.Significance.These results support the use of one-compartment spatiotemporal flow models, but two-compartment perfusion models were not sufficiently identifiable. Future studies should investigate whether this degeneracy is resolved in more realistic 2D and 3D systems, by adding physically justified constraints, or by optimizing experimental parameters such as injection duration or temporal resolution.

Current status in spatiotemporal analysis of contrast-based perfusion MRI.

In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions-of-interest as isolated systems supplied by a single global source. This simplification not only leads to long-recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between-voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state-of-the-art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.

Physiological confounders of renal blood flow measurement.

OBJECTIVES: Renal blood flow (RBF) is controlled by a number of physiological factors that can contribute to the variability of its measurement. The purpose of this review is to assess the changes in RBF in response to a wide range of physiological confounders and derive practical recommendations on patient preparation and interpretation of RBF measurements with MRI. METHODS: A comprehensive search was conducted to include articles reporting on physiological variations of renal perfusion, blood and/or plasma flow in healthy humans. RESULTS: A total of 24 potential confounders were identified from the literature search and categorized into non-modifiable and modifiable factors. The non-modifiable factors include variables related to the demographics of a population (e.g. age, sex, and race) which cannot be manipulated but should be considered when interpreting RBF values between subjects. The modifiable factors include different activities (e.g. food/fluid intake, exercise training and medication use) that can be standardized in the study design. For each of the modifiable factors, evidence-based recommendations are provided to control for them in an RBF-measurement. CONCLUSION: Future studies aiming to measure RBF are encouraged to follow a rigorous study design, that takes into account these recommendations for controlling the factors that can influence RBF results.

Bias, Repeatability and Reproducibility of Liver T1 Mapping With Variable Flip Angles.

BACKGROUND: Three-dimensional variable flip angle (VFA) methods are commonly used for T1 mapping of the liver, but there is no data on the accuracy, repeatability, and reproducibility of this technique in this organ in a multivendor setting. PURPOSE: To measure bias, repeatability, and reproducibility of VFA T1 mapping in the liver. STUDY TYPE: Prospective observational. POPULATION: Eight healthy volunteers, four women, with no known liver disease. FIELD STRENGTH/SEQUENCE: 1.5-T and 3.0-T; three-dimensional steady-state spoiled gradient echo with VFAs; Look-Locker. ASSESSMENT: Traveling volunteers were scanned twice each (30 minutes to 3 months apart) on six MRI scanners from three vendors (GE Healthcare, Philips Medical Systems, and Siemens Healthineers) at two field strengths. The maximum period between the first and last scans among all volunteers was 9 months. Volunteers were instructed to abstain from alcohol intake for at least 72 hours prior to each scan and avoid high cholesterol foods on the day of the scan. STATISTICAL TESTS: Repeated measures ANOVA, Student t-test, Levene's test of variances, and 95% significance level. The percent error relative to literature liver T1 in healthy volunteers was used to assess bias. The relative error (RE) due to intrascanner and interscanner variation in T1 measurements was used to assess repeatability and reproducibility. RESULTS: The 95% confidence interval (CI) on the mean bias and mean repeatability RE of VFA T1 in the healthy liver was 34 ± 6% and 10 ± 3%, respectively. The 95% CI on the mean reproducibility RE at 1.5 T and 3.0 T was 29 ± 7% and 25 ± 4%, respectively. DATA CONCLUSION: Bias, repeatability, and reproducibility of VFA T1 mapping in the liver in a multivendor setting are similar to those reported for breast, prostate, and brain. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

Bias and Precision in Magnetic Resonance Imaging-Based Estimates of Renal Blood Flow: Assessment by Triangulation.

BACKGROUND: Renal blood flow (RBF) can be measured with dynamic contrast enhanced-MRI (DCE-MRI) and arterial spin labeling (ASL). Unfortunately, individual estimates from both methods vary and reference-standard methods are not available. A potential solution is to include a third, arbitrating MRI method in the comparison. PURPOSE: To compare RBF estimates between ASL, DCE, and phase contrast (PC)-MRI. STUDY TYPE: Prospective. POPULATION: Twenty-five patients with type-2 diabetes (36% female) and five healthy volunteers (HV, 80% female). FIELD STRENGTH/SEQUENCES: A 3 T; gradient-echo 2D-DCE, pseudo-continuous ASL (pCASL) and cine 2D-PC. ASSESSMENT: ASL, DCE, and PC were acquired once in all patients. ASL and PC were acquired four times in each HV. RBF was estimated and split-RBF was derived as (right kidney RBF)/total RBF. Repeatability error (RE) was calculated for each HV, RE = 1.96 × SD, where SD is the standard deviation of repeat scans. STATISTICAL TESTS: Paired t-tests and one-way analysis of variance (ANOVA) were used for statistical analysis. The 95% confidence interval (CI) for difference between ASL/PC and DCE/PC was assessed using two-sample F-test for variances. Statistical significance level was P < 0.05. Influential outliers were assessed with Cook's distance (Di > 1) and results with outliers removed were presented. RESULTS: In patients, the mean RBF (mL/min/1.73m2 ) was 618 ± 62 (PC), 526 ± 91 (ASL), and 569 ± 110 (DCE). Differences between measurements were not significant (P = 0.28). Intrasubject agreement was poor for RBF with limits-of-agreement (mL/min/1.73m2 ) [-687, 772] DCE-ASL, [-482, 580] PC-DCE, and [-277, 460] PC-ASL. The difference PC-ASL was significantly smaller than PC-DCE, but this was driven by a single-DCE outlier (P = 0.31, after removing outlier). The difference in split-RBF was comparatively small. In HVs, mean RE (±95% CI; mL/min/1.73 m2 ) was significantly smaller for PC (79 ± 41) than for ASL (241 ± 85). CONCLUSIONS: ASL, DCE, and PC RBF show poor agreement in individual subjects but agree well on average. Triangulation with PC suggests that the accuracy of ASL and DCE is comparable. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Motion correction of free-breathing magnetic resonance renography using model-driven registration.

INTRODUCTION: Model-driven registration (MDR) is a general approach to remove patient motion in quantitative imaging. In this study, we investigate whether MDR can effectively correct the motion in free-breathing MR renography (MRR). MATERIALS AND METHODS: MDR was generalised to linear tracer-kinetic models and implemented using 2D or 3D free-form deformations (FFD) with multi-resolution and gradient descent optimization. MDR was evaluated using a kidney-mimicking digital reference object (DRO) and free-breathing patient data acquired at high temporal resolution in multi-slice 2D (5 patients) and 3D acquisitions (8 patients). Registration accuracy was assessed using comparison to ground truth DRO, calculating the Hausdorff distance (HD) between ground truth masks with segmentations and visual evaluation of dynamic images, signal-time courses and parametric maps (all data). RESULTS: DRO data showed that the bias and precision of parameter maps after MDR are indistinguishable from motion-free data. MDR led to reduction in HD (HDunregistered = 9.98 ± 9.76, HDregistered = 1.63 ± 0.49). Visual inspection showed that MDR effectively removed motion effects in the dynamic data, leading to a clear improvement in anatomical delineation on parametric maps and a reduction in motion-induced oscillations on signal-time courses. DISCUSSION: MDR provides effective motion correction of MRR in synthetic and patient data. Future work is needed to compare the performance against other more established methods.

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 (Ki ), 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: Ki 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 Ki 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 Ki 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 (Fa ) (R = -0.48, 95% CI [-0.75,-0.05], P = 0.03). CONCLUSION: Hepatocyte intracellular uptake rate, Ki , 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.

Assessment of aortic stiffness by cardiovascular magnetic resonance following the treatment of severe aortic stenosis by TAVI and surgical AVR.

BACKGROUND: Aortic stiffness is increasingly used as an independent predictor of adverse cardiovascular outcomes. We sought to compare the impact of transcatheter aortic valve implantation (TAVI) and surgical aortic valve replacement (SAVR) upon aortic vascular function using cardiovascular magnetic resonance (CMR) measurements of aortic distensibility and pulse wave velocity (PWV). METHODS AND RESULTS: A 1.5 T CMR scan was performed pre-operatively and at 6 m post-intervention in 72 patients (32 TAVI, 40 SAVR; age 76 ± 8 years) with high-risk symptomatic severe aortic stenosis. Distensibility of the ascending and descending thoracic aorta and aortic pulse wave velocity were determined at both time points. TAVI and SAVR patients were comparable for gender, blood pressure and left ventricular ejection fraction. The TAVI group were older (81 ± 6.3 vs. 72.8 ± 7.0 years, p < 0.05) with a higher EuroSCORE II (5.7 ± 5.6 vs. 1.5 ± 1.0 %, p < 0.05). At 6 m, SAVR was associated with a significant decrease in distensibility of the ascending aorta (1.95 ± 1.15 vs. 1.57 ± 0.68 × 10(-3)mmHg(-1), p = 0.044) and of the descending thoracic aorta (3.05 ± 1.12 vs. 2.66 ± 1.00 × 10(-3)mmHg(-1), p = 0.018), with a significant increase in PWV (6.38 ± 4.47 vs. 11.01 ± 5.75 ms(-1), p = 0.001). Following TAVI, there was no change in distensibility of the ascending aorta (1.96 ± 1.51 vs. 1.72 ± 0.78 × 10(-3)mmHg(-1), p = 0.380), descending thoracic aorta (2.69 ± 1.79 vs. 2.21 ± 0.79 × 10(-3)mmHg(-1), p = 0.181) nor in PWV (8.69 ± 6.76 vs. 10.23 ± 7.88 ms(-1), p = 0.301) at 6 m. CONCLUSIONS: Treatment of symptomatic severe aortic stenosis by SAVR but not TAVI was associated with an increase in aortic stiffness at 6 months. Future work should focus on the prognostic implication of these findings to determine whether improved patient selection and outcomes can be achieved.

An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited.

BACKGROUND: Perfusion imaging has become an important image based tool to derive the physiological information in various applications, like tumor diagnostics and therapy, stroke, (cardio-) vascular diseases, or functional assessment of organs. However, even after 20 years of intense research in this field, perfusion imaging still remains a research tool without a broad clinical usage. One problem is the lack of standardization in technical aspects which have to be considered for successful quantitative evaluation; the second problem is a lack of tools that allow a direct integration into the diagnostic workflow in radiology. RESULTS: Five compartment models, namely, a one compartment model (1CP), a two compartment exchange (2CXM), a two compartment uptake model (2CUM), a two compartment filtration model (2FM) and eventually the extended Toft's model (ETM) were implemented as plugin for the DICOM workstation OsiriX. Moreover, the plugin has a clean graphical user interface and provides means for quality management during the perfusion data analysis. Based on reference test data, the implementation was validated against a reference implementation. No differences were found in the calculated parameters. CONCLUSION: We developed open source software to analyse DCE-MRI perfusion data. The software is designed as plugin for the DICOM Workstation OsiriX. It features a clean GUI and provides a simple workflow for data analysis while it could also be seen as a toolbox providing an implementation of several recent compartment models to be applied in research tasks. Integration into the infrastructure of a radiology department is given via OsiriX. Results can be saved automatically and reports generated automatically during data analysis ensure certain quality control.

Fitting the two-compartment model in DCE-MRI by linear inversion.

PURPOSE: Model fitting of dynamic contrast-enhanced-magnetic resonance imaging-MRI data with nonlinear least squares (NLLS) methods is slow and may be biased by the choice of initial values. The aim of this study was to develop and evaluate a linear least squares (LLS) method to fit the two-compartment exchange and -filtration models. METHODS: A second-order linear differential equation for the measured concentrations was derived where model parameters act as coefficients. Simulations of normal and pathological data were performed to determine calculation time, accuracy and precision under different noise levels and temporal resolutions. Performance of the LLS was evaluated by comparison against the NLLS. RESULTS: The LLS method is about 200 times faster, which reduces the calculation times for a 256 × 256 MR slice from 9 min to 3 s. For ideal data with low noise and high temporal resolution the LLS and NLLS were equally accurate and precise. The LLS was more accurate and precise than the NLLS at low temporal resolution, but less accurate at high noise levels. CONCLUSION: The data show that the LLS leads to a significant reduction in calculation times, and more reliable results at low noise levels. At higher noise levels the LLS becomes exceedingly inaccurate compared to the NLLS, but this may be improved using a suitable weighting strategy. Magn Reson Med 76:998-1006, 2016. © 2015 Wiley Periodicals, Inc.

Comparison of the Diagnostic Performance of Four Quantitative Myocardial Perfusion Estimation Methods Used in Cardiac MR Imaging: CE-MARC Substudy.

PURPOSE: To compare the diagnostic performance of four tracer kinetic analysis methods to quantify myocardial perfusion from magnetic resonance (MR) imaging cardiac perfusion data sets in terms of their ability to lead to the diagnosis of myocardial ischemia. MATERIALS AND METHODS: The study was approved by the regional ethics committee, and all patients gave written consent. A representative sample of 50 patients with suspected ischemic heart disease was retrospectively selected from the Clinical Evaluation of Magnetic Resonance Imaging in Coronary Heart Disease trial data set. Quantitative myocardial blood flow (MBF) was estimated from rest and adenosine stress MR imaging perfusion data sets by using four established methods. A matching diagnosis of both an inducible defect as assessed with single photon emission computed tomography and a luminal stenosis of 70% or more as assessed with quantitative x-ray angiography was used as the reference standard for the presence of myocardial ischemia. Diagnostic performance was evaluated with receiver operating characteristic (ROC) curve analysis for each method, with stress MBF and myocardial perfusion reserve (MPR) serving as continuous measures. RESULTS: Area under the ROC curve with stress MBF and MPR as the outcome measures, respectively, was 0.86 and 0.92 for the Fermi model, 0.85 and 0.87 for the uptake model, 0.85 and 0.80 for the one-compartment model, and 0.87 and 0.87 for model-independent deconvolution. There was no significant difference between any of the models or between MBF and MPR, except that the Fermi model outperformed the one-compartment model if MPR was used as the outcome measure (P = .02). CONCLUSION: Diagnostic performance of quantitative myocardial perfusion estimates is not affected by the tracer kinetic analysis method used.

Gadofosveset-based biomarker of tissue albumin concentration: Technical validation in vitro and feasibility in vivo.

PURPOSE: There is currently no adequate method of mapping physiologic and pathophysiologic tissue albumin concentrations in human subjects. The objective of this study was to devise and evaluate a biomarker of regional albumin concentration using gadofosveset-enhanced MRI. THEORY AND METHODS: A binding and relaxation model was devised and evaluated in vitro in solutions of albumin at 3.0 Tesla (T) and 4.7T. The method was evaluated in the heart in seven volunteers at 3.0T. RESULTS: MRI-derived estimates of albumin concentration were in good agreement with true values over the range 0.1-1.0 mM (Pearson correlation coefficients of 0.85 and 0.88 for 3.0T and 4.7T, respectively). The mean calculated albumin concentration in the myocardium for the volunteers was 0.02 mM (range, 0.01-0.03 mM). CONCLUSION: Accurate estimates of albumin concentration in vitro suggest this may be a viable noninvasive alternative to existing techniques. In the myocardium the MRI-derived estimates of albumin concentration indicate the practical feasibility of the technique but were below expected values. Gadofosveset-enhanced MR relaxometry has potential in providing biomarkers of regional albumin concentration; further evaluation is required before it can be used reliably in vivo.

Modeling DCE-MRI at low temporal resolution: a case study on rheumatoid arthritis.

PURPOSE: To identify the optimal tracer-kinetic modeling strategy for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data acquired at low temporal resolution. MATERIALS AND METHODS: DCE-MRI was performed on 13 patients with rheumatoid arthritis of the hand before and after anti-tumor necrosis factor alpha (TNFα) therapy, using a 3D sequence with a temporal resolution of 13 seconds, imaging for 4 minutes postcontrast injection. Concentration-time curves were extracted from regions of interest (ROIs) in enhancing synovium and fitted to the 3-parameter modified Tofts model (MT) and the 4-parameter two-compartment exchange model (2CXM). To assist the interpretation of the data, the same analysis was applied to simulated data with similar characteristics. RESULTS: Both models fitted the data closely, and showed similar therapy effects. The MT plasma volume was significantly lower than with 2CXM, but the differences in permeability and interstitial volume were not significant. 2CXM was less precise than MT, with larger standard deviations relative to the mean in most parameters. The additional perfusion parameter determined with 2CXM did not provide a statistically significant trend due to low precision. CONCLUSION: The standard MT model is the optimal modeling strategy at low temporal resolution. Advanced models improve the accuracy and generate an additional parameter, but these benefits are offset by low precision.

Prediction and assessment of responses to renal artery revascularization with dynamic contrast-enhanced magnetic resonance imaging: a pilot study.

The aim of this study was to assess the potential of dynamic contrast-enhanced (DCE) MRI to predict and evaluate functional outcomes after renal artery revascularization for renal artery stenosis (RAS). The single-kidney glomerular filtration rate (SK-GFR) was measured in 15 patients with atherosclerotic RAS with DCE-MRI and radioisotopes at baseline and 4 mo after revascularization. DCE-MRI also produced measurements of blood flow, blood volume, extraction fraction, tubular transit time, and functional volume. Stented kidneys (n = 22) were divided into three response groups on the basis of the changes in radioisotope SK-GFR: improved (n = 5), stable (n = 13), and deteriorated (n = 4). A good agreement was found between SK-GFR values from DCE-MRI and radioisotopes (correlation coefficient: 0.91). Before intervention, kidneys that improved had lower extraction fraction, higher blood volume, longer tubular transit time, and lower SK-GFR. After intervention, improved kidneys had increased functional volume, and deteriorated kidneys had reduced functional volume and extraction fraction. Revascularization improved blood flow and blood volume in all groups. This pilot study led to the hypothesis that well-vascularized kidneys with reduced extraction fractions are most likely to benefit from revascularization. More generally, DCE-MRI has the potential to replace radioisotope measurement of SK-GFR and may improve patient management by providing additional information on tissue perfusion.

Classic models for dynamic contrast-enhanced MRI.

Dynamic contrast-enhanced MRI (DCE-MRI) is a functional MRI method where T1 -weighted MR images are acquired dynamically after bolus injection of a contrast agent. The data can be interpreted in terms of physiological tissue characteristics by applying the principles of tracer-kinetic modelling. In the brain, DCE-MRI enables measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), blood-brain barrier (BBB) permeability-surface area product (PS) and the volume of the interstitium (ve ). These parameters can be combined to form others such as the volume-transfer constant K(trans) , the extraction fraction E and the contrast-agent mean transit times through the intra- and extravascular spaces. A first generation of tracer-kinetic models for DCE-MRI was developed in the early 1990s and has become a standard in many applications. Subsequent improvements in DCE-MRI data quality have driven the development of a second generation of more complex models. They are increasingly used, but it is not always clear how they relate to the models of the first generation or to the model-free deconvolution methods for tissues with intact BBB. This lack of understanding is leading to increasing confusion on when to use which model and how to interpret the parameters. The purpose of this review is to clarify the relation between models of the first and second generations and between model-based and model-free methods. All quantities are defined using a generic terminology to ensure the widest possible scope and to reveal the link between applications in the brain and in other organs.

Combined diffusion-weighted, blood oxygen level-dependent, and dynamic contrast-enhanced MRI for characterization and differentiation of renal cell carcinoma.

PURPOSE: To investigate a multiparametric magnetic resonance imaging (MRI) approach comprising diffusion-weighted imaging (DWI), blood oxygen-dependent (BOLD), and dynamic contrast-enhanced (DCE) MRI for characterization and differentiation of primary renal cell carcinoma (RCC). MATERIAL AND METHODS: Fourteen patients with clear-cell carcinoma and four patients with papillary RCC were examined with DWI, BOLD MRI, and DCE MRI at 1.5T. The apparent diffusion coefficient (ADC) was calculated with a monoexponential decay. The spin-dephasing rate R2* was derived from parametric R2* maps. DCE-MRI was analyzed using a two-compartment exchange model allowing separation of perfusion (plasma flow [FP] and plasma volume [VP]), permeability (permeability surface area product [PS]), and extravascular extracellular volume (VE). Statistical analysis was performed with Wilcoxon signed-rank test, Pearson's correlation coefficient, and receiver operating characteristic curve analysis. RESULTS: Clear-cell RCC showed higher ADC and lower R2* compared to papillary subtypes, but differences were not significant. FP of clear-cell subtypes was significantly higher than in papillary RCC. Perfusion parameters showed moderate but significant inverse correlation with R2*. VE showed moderate inverse correlation with ADC. Fp and Vp showed best sensitivity for histological differentiation. CONCLUSION: Multiparametric MRI comprising DWI, BOLD, and DCE MRI is feasible for assessment of primary RCC. BOLD moderately correlates to DCE MRI-derived perfusion. ADC shows moderate correlation to the extracellular volume, but does not correlate to tumor oxygenation or perfusion. In this preliminary study DCE-MRI appeared superior to BOLD and DWI for histological differentiation.

Myocardial blood flow at rest and stress measured with dynamic contrast-enhanced MRI: comparison of a distributed parameter model with a Fermi function model.

PURPOSE: To assess the feasibility of simultaneously measuring blood flow (Fb ), Gd-DTPA extraction fraction (E), and distribution volume (vd ) in healthy myocardium at rest and under adenosine stress using dynamic contrast-enhanced MRI. METHODS: Sixteen volunteers were examined at 1.5 T and 11 returned for a repeat study. The data were analyzed using a distributed parameter (DP) 2-region model to arrive at estimates of Fb , E, blood volume, and interstitial volume. For comparison, estimates of Fb were also obtained using a Fermi function model. RESULTS: DP model fits were successful in 49 of the 54 data sets. Estimates obtained using DP and Fermi models did not differ for either rest Fb or myocardial perfusion reserve though DP estimates of stress Fb were lower than Fermi estimates. The repeatability of the DP parameters Fb , E, and vd was better than or equal to the repeatability of Fermi-Fb . E at rest and under stress was estimated to be 66% and 57%, respectively. CONCLUSION: The results suggest that characteristics of the microvasculature of healthy myocardium can be reliably determined using dynamic contrast-enhanced MRI at rest and under stress and that delivery of Gd-DTPA to the myocardium is not flow-limited.

UMMPerfusion: an open source software tool towards quantitative MRI perfusion analysis in clinical routine.

To develop a generic Open Source MRI perfusion analysis tool for quantitative parameter mapping to be used in a clinical workflow and methods for quality management of perfusion data. We implemented a classic, pixel-by-pixel deconvolution approach to quantify T1-weighted contrast-enhanced dynamic MR imaging (DCE-MRI) perfusion data as an OsiriX plug-in. It features parallel computing capabilities and an automated reporting scheme for quality management. Furthermore, by our implementation design, it could be easily extendable to other perfusion algorithms. Obtained results are saved as DICOM objects and directly added to the patient study. The plug-in was evaluated on ten MR perfusion data sets of the prostate and a calibration data set by comparing obtained parametric maps (plasma flow, volume of distribution, and mean transit time) to a widely used reference implementation in IDL. For all data, parametric maps could be calculated and the plug-in worked correctly and stable. On average, a deviation of 0.032 ± 0.02 ml/100 ml/min for the plasma flow, 0.004 ± 0.0007 ml/100 ml for the volume of distribution, and 0.037 ± 0.03 s for the mean transit time between our implementation and a reference implementation was observed. By using computer hardware with eight CPU cores, calculation time could be reduced by a factor of 2.5. We developed successfully an Open Source OsiriX plug-in for T1-DCE-MRI perfusion analysis in a routine quality managed clinical environment. Using model-free deconvolution, it allows for perfusion analysis in various clinical applications. By our plug-in, information about measured physiological processes can be obtained and transferred into clinical practice.

Quantification of cerebral tumour blood flow and permeability with T1-weighted dynamic contrast enhanced MRI: a feasibility study.

OBJECTIVES: Recently, T1-weighted DCE-MRI was proposed as an alternative to T2*-weighted DSC-MRI for the quantification of perfusion and permeability in brain tumors. The aim of the present feasibility study was to explore the clinical potential of the technique in different tumor types using a case-based review of initial results. PATIENTS AND METHODS: The method for data analysis was adapted from cerebral perfusion CT and applied in this study to a small group of patients with grade IV glioma and other brain tumors. The possible use of the proposed methodology was also explored for characterizing, following-up and planning the therapy of brain tumors. RESULTS: Parametric maps clearly differentiated tumor from the surrounding brain tissue, and also distinguished areas within the tumor presenting with different characteristics, thereby allowing identification of significant target areas for biopsy and/or treatment. Differences in cerebral blood flow (CBF) and lower extraction fractions (E) were observed in various tumors. Progression from a grade II to grade IV glioma over the course of a year was characterized by an increase in CBF and a decrease in E. CONCLUSION: DCE-MRI-based quantitative perfusion and permeability may be helpful for tumor-grade characterization, biopsy guidance, radiotherapy planning, radiotherapy monitoring and clinical follow-up, thereby improving the non-invasive characterization of brain tumors.