Precise measurement of renal filtration and vascular parameters using a two-compartment model for dynamic contrast-enhanced MRI of the kidney gives realistic normal values

Brighton and Sussex Medical School, Falmer, Sussex BN1 9PX, UK.
European Radiology (Impact Factor: 4.01). 03/2012; 22(6):1320-30. DOI: 10.1007/s00330-012-2382-9
Source: PubMed


To model the uptake phase of T(1)-weighted DCE-MRI data in normal kidneys and to demonstrate that the fitted physiological parameters correlate with published normal values.
The model incorporates delay and broadening of the arterial vascular peak as it appears in the capillary bed, two distinct compartments for renal intravascular and extravascular Gd tracer, and uses a small-vessel haematocrit value of 24%. Four physiological parameters can be estimated: regional filtration K ( trans ) (ml min(-1) [ml tissue](-1)), perfusion F (ml min(-1) [100 ml tissue](-1)), blood volume v ( b ) (%) and mean residence time MRT (s). From these are found the filtration fraction (FF; %) and total GFR (ml min(-1)). Fifteen healthy volunteers were imaged twice using oblique coronal slices every 2.5 s to determine the reproducibility.
Using parenchymal ROIs, group mean values for renal biomarkers all agreed with published values: K ( trans ): 0.25; F: 219; v ( b ): 34; MRT: 5.5; FF: 15; GFR: 115. Nominally cortical ROIs consistently underestimated total filtration (by ~50%). Reproducibility was 7-18%. Sensitivity analysis showed that these fitted parameters are most vulnerable to errors in the fixed parameters kidney T(1), flip angle, haematocrit and relaxivity.
These renal biomarkers can potentially measure renal physiology in diagnosis and treatment.
• Dynamic contrast-enhanced magnetic resonance imaging can measure renal function. • Filtration and perfusion values in healthy volunteers agree with published normal values. • Precision measured in healthy volunteers is between 7 and 15%.

Download full-text


Available from: Marica Cutajar
  • Source
    • "Briefly, the differential equations for a multiple region system (i.e., the TOI and RR) are given as Eqs. (2) and (3) [21] [22]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This study compared three methods for analyzing DCE-MRI data with a reference region (RR) model: a linear least-square fitting with numerical analysis (LLSQ-N), a nonlinear least-square fitting with numerical analysis (NLSQ-N), and an analytical analysis (NLSQ-A). The accuracy and precision of estimating the pharmacokinetic parameter ratios KR and VR, where KR is defined as a ratio between the two volume transfer constants, K(trans,TOI) and K(trans,RR), and VR is the ratio between the two extracellular extravascular volumes, ve,TOI and ve,RR, were assessed using simulations under various signal-to-noise ratios (SNRs) and temporal resolutions (4, 6, 30, and 60s). When no noise was added, the simulations showed that the mean percent error (MPE) for the estimated KR and VR using the LLSQ-N and NLSQ-N methods ranged from 1.2% to 31.6% with various temporal resolutions while the NLSQ-A method maintained a very high accuracy (<1.0×10(-4) %) regardless of the temporal resolution. The simulation also indicated that the LLSQ-N and NLSQ-N methods appear to underestimate the parameter ratios more than the NLSQ-A method. In addition, seven in vivo DCE-MRI datasets from spontaneously occurring canine brain tumors were analyzed with each method. Results for the in vivo study showed that KR (ranging from 0.63 to 3.11) and VR (ranging from 2.82 to 19.16) for the NLSQ-A method were both higher than results for the other two methods (KR ranging from 0.01 to 1.29 and VR ranging from 1.48 to 19.59). A temporal downsampling experiment showed that the averaged percent error for the NLSQ-A method (8.45%) was lower than the other two methods (22.97% for LLSQ-N and 65.02% for NLSQ-N) for KR, and the averaged percent error for the NLSQ-A method (6.33%) was lower than the other two methods (6.57% for LLSQ-N and 13.66% for NLSQ-N) for VR. Using simulations, we showed that the NLSQ-A method can estimate the ratios of pharmacokinetic parameters more accurately and precisely than the NLSQ-N and LLSQ-N methods over various SNRs and temporal resolutions. All simulations were validated with in vivo DCE MRI data.
    Full-text · Article · Apr 2014 · Magnetic Resonance Imaging
  • Source
    • "Compartmental modeling is typically used to extract renal physiological parameters like GFR from enhancement curves. The details of the models and their comparison can be found in literature 2, 21–25. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Established as a method to study anatomic changes, such as renal tumors or atherosclerotic vascular disease, magnetic resonance imaging (MRI) to interrogate renal function has only recently begun to come of age. In this review, we briefly introduce some of the most important MRI techniques for renal functional imaging, and then review current findings on their use for diagnosis and monitoring of major kidney diseases. Specific applications include renovascular disease, diabetic nephropathy, renal transplants, renal masses, acute kidney injury, and pediatric anomalies. With this review, we hope to encourage more collaboration between nephrologists and radiologists to accelerate the development and application of modern MRI tools in nephrology clinics.Kidney International advance online publication, 25 September 2013; doi:10.1038/ki.2013.361.
    Full-text · Article · Sep 2013 · Kidney International
  • [Show abstract] [Hide abstract]
    ABSTRACT: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) renography, in common with other medical imaging techniques, is influenced by respiratory motion. As a result, data quantification may be inaccurate. This work presents a novel on-line approach for motion correction by implementing a spatio-temporal independent component analysis method (STICA). This methodology firstly results in removal of motion artefacts and secondly provides independent components that have physiological characteristics. The STICA was applied to 10 healthy volunteers' renal DCE-MRI data. The results were evaluated using independent component curve gradients (lCGs) from different regions of interest and by comparing them with the Rutland-Patlak (RP) analysis. The r2 values for the lCGs were significantly higher compared to the RP curves. The standard deviations of the IC curve gradients also showed less dispersion with comparison to the RP curve gradients across all the ten volunteers' renal data.
    No preview · Conference Paper · Jan 2012
Show more

We use cookies to give you the best possible experience on ResearchGate. Read our cookies policy to learn more.