The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge.

PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Reduced white matter venous density on MRI is associated with neurodegeneration and cognitive impairment in the elderly.

High-resolution susceptibility weighted imaging (SWI) provides unique contrast to small venous vasculature. The conspicuity of these mesoscopic veins, such as deep medullary veins in white matter, is subject to change from SWI venography when venous oxygenation in these veins is altered due to oxygenated blood susceptibility changes. The changes of visualization in small veins shows potential to depict regional changes of oxygen utilization and/or vascular density changes in the aging brain. The goal of this study was to use WM venous density to quantify small vein visibility in WM and investigate its relationship with neurodegenerative features, white matter hyperintensities (WMHs), and cognitive/functional status in elderly subjects (N = 137). WM venous density was significantly associated with neurodegeneration characterized by brain atrophy (ß = 0.046± 0.01, p < 0.001), but no significant association was found between WM venous density and WMHs lesion load (p = 0.3963). Further analysis of clinical features revealed a negative trend of WM venous density with the sum-of-boxes of Clinical Dementia Rating and a significant association with category fluency (1-min animal naming). These results suggest that WM venous density on SWI can be used as a sensitive marker to characterize cerebral oxygen metabolism and different stages of cognitive and functional status in neurodegenerative diseases.

Kidney tumor diffusion-weighted magnetic resonance imaging derived ADC histogram parameters combined with patient characteristics and tumor volume to discriminate oncocytoma from renal cell carcinoma.

PURPOSE: To assess the ability to discriminate oncocytoma from RCC based on a model using whole tumor ADC histogram parameters with additional use of tumor volume and patient characteristics. METHOD: In this prospective study, 39 patients (mean age 65 years, range 28-79; 9/39 (23%) female) with 39 renal tumors (32/39 (82%) RCC and 7/39 (18%) oncocytoma) underwent multiparametric MRI between November 2014 and June 2018. Two regions of interest (ROIs) were drawn to cover both the entire tumor volume and a part of healthy renal cortex. ROI ADC maps were calculated using a mono-exponential model and ADC histogram distribution parameters were calculated. A logistic regression model was created using ADC histogram parameters, radiographic and patient characteristics that were significantly different between oncocytoma and RCC. A ROC curve of the model was constructed and the AUC, sensitivity and specificity were calculated. Furthermore, differences in intra-patient ADC histogram parameters between renal tumor and healthy cortex were calculated. A separate ROC curve was constructed to differentiate oncocytoma from RCC using statistically significant intra-patient parameter differences. RESULTS: ADC standard deviation (p = 0.008), entropy (p = 0.010), tumor volume (p = 0.012), and patient sex (p = 0.018) were significantly different between RCC and oncocytoma. The regression model of these parameters combined had an ROC-AUC of 0.91 with a sensitivity of 86% and specificity of 84%. Intra-patient difference in ADC 25th percentile (p < 0.01) and entropy (p = 0.030) combined had a ROC-AUC of 0.86 with a sensitivity and specificity of 86%, and 81%, respectively. CONCLUSION: A model combining ADC standard deviation and entropy with tumor volume and patient sex has the highest diagnostic value for discrimination of oncocytoma. Although less accurate, intra-patient difference in ADC 25th percentile and entropy between renal tumor and healthy cortex can also be used. Although the results of this preliminary study do not yet justify clinical use of the model, it does stimulate further research using whole tumor ADC histogram parameters.

Effect of acute hyperglycemia on moderately hypothermic GL261 mouse glioma monitored by T1-weighted DCE MRI.

OBJECTIVE: We sought to evaluate the effects of acute hyperglycemia induced by intraperitoneal injection of glucose (2.7 g/kg) on vascular delivery to GL261 mouse gliomas kept at moderate hypothermia (~30 °C). MATERIALS AND METHODS: Seven GL261 glioma-bearing mice were studied by T1-weighted DCE MRI before and after an injection of glucose (n = 4) or saline (n = 3). Maximum relative contrast enhancement (RCE) and initial area under the enhancement curve (IAUC) were determined in each pixel. RESULTS: The mean tumor parameter values showed no significant changes after injecting either saline (RCE -5.9 ± 5.0 %; IAUC -3.7 ± 3.6 %) or glucose (RCE -1.6 ± 9.0 %; IAUC +0.6 ± 6.4 %). Pixel-by-pixel analysis revealed small post-injection changes in RCE and IAUC between the glucose and saline groups, all within 13 % range of their baseline values. CONCLUSION: Perturbing the metabolism of GL261 tumors kept at moderate hypothermia with hyperglycemia did not induce significant changes in the permeability/perfusion of these tumors. This is relevant for future studies with this model since regional differences in glucose accumulation could thus reflect basal heterogeneities in vasculature and/or metabolism of GL261 tumors.

Intravoxel incoherent motion diffusion-weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma.

PURPOSE: To study the differentiation of malignant breast lesions from benign lesions and fibroglandular tissue (FGT) using apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters. MATERIALS AND METHODS: This retrospective study included 26 malignant and 14 benign breast lesions in 35 patients who underwent diffusion-weighted MRI at 3.0T and nine b-values (0-1000 s/mm(2) ). ADC and IVIM parameters (perfusion fraction fp , pseudodiffusion coefficient Dp , and true diffusion coefficient Dd ) were determined in lesions and FGT. For comparison, IVIM was also measured in 16 high-risk normal patients. A predictive model was constructed using linear discriminant analysis. Lesion discrimination based on ADC and IVIM parameters was assessed using receiver operating characteristic (ROC) and area under the ROC curve (AUC). RESULTS: In FGT of normal subjects, fp was 1.1 ± 1.1%. In malignant lesions, fp (6.4 ± 3.1%) was significantly higher than in benign lesions (3.1 ± 3.3%, P = 0.0025) or FGT (1.5 ± 1.2%, P < 0.001), and Dd ((1.29 ± 0.28) × 10(-3) mm(2) /s) was lower than in benign lesions ((1.56 ± 0.28) × 10(-3) mm(2) /s, P = 0.011) or FGT ((1.86 ± 0.34) × 10(-3) mm(2) /s, P < 0.001). A combination of Dd and fp provided higher AUC for discrimination between malignant and benign lesions (0.84) or FGT (0.97) than ADC (0.72 and 0.86, respectively). CONCLUSION: The IVIM parameters provide accurate identification of malignant lesions.

Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imaging.

PURPOSE: To assess whether an artificial neural network (ANN) model is a useful tool for automatic detection of cancerous voxels in the prostate from (1)H-MRSI datasets and whether the addition of information about anatomical segmentation improves the detection of cancer. MATERIALS AND METHODS: The Institutional Review Board approved this HIPAA-compliant study and waived informed consent. Eighteen men with prostate cancer (median age, 55 years; range, 36-71 years) who underwent endorectal MRI/MRSI before radical prostatectomy were included in this study. These patients had at least one cancer area on whole-mount histopathological map and at least one matching MRSI voxel suspicious for cancer detected. Two ANN models for automatic classification of MRSI voxels in the prostate were implemented and compared: model 1, which used only spectra as input, and model 2, which used the spectra plus information from anatomical segmentation. The models were trained, tested and validated using spectra from voxels that the spectroscopist had designated as cancer and that were verified on histopathological maps. RESULTS: At ROC analysis, model 2 (AUC = 0.968) provided significantly better (P = 0.03) classification of cancerous voxels than did model 1 (AUC = 0.949). CONCLUSION: Automatic analysis of prostate MRSI to detect cancer using ANN model is feasible. Application of anatomical segmentation from MRI as an additional input to ANN improves the accuracy of detecting cancerous voxels from MRSI.

High-field small animal magnetic resonance oncology studies.

This review focuses on the applications of high magnetic field magnetic resonance imaging (MRI) and spectroscopy (MRS) to cancer studies in small animals. High-field MRI can provide information about tumor physiology, the microenvironment, metabolism, vascularity and cellularity. Such studies are invaluable for understanding tumor growth and proliferation, response to treatment and drug development. The MR techniques reviewed here include (1)H, (31)P, chemical exchange saturation transfer imaging and hyperpolarized (13)C MRS as well as diffusion-weighted, blood oxygen level dependent contrast imaging and dynamic contrast-enhanced MRI. These methods have been proven effective in animal studies and are highly relevant to human clinical studies.

Response of HT29 colorectal xenograft model to cediranib assessed with 18 F-fluoromisonidazole positron emission tomography, dynamic contrast-enhanced and diffusion-weighted MRI.

Cediranib is a small-molecule pan-vascular endothelial growth factor receptor inhibitor. The tumor response to short-term cediranib treatment was studied using dynamic contrast-enhanced and diffusion-weighted MRI at 7 T, as well as (18) F-fluoromisonidazole positron emission tomography and histological markers. Rats bearing subcutaneous HT29 human colorectal tumors were imaged at baseline; they then received three doses of cediranib (3 mg/kg per dose daily) or vehicle (dosed daily), with follow-up imaging performed 2 h after the final cediranib or vehicle dose. Tumors were excised and evaluated for the perfusion marker Hoechst 33342, the endothelial cell marker CD31, smooth muscle actin, intercapillary distance and tumor necrosis. Dynamic contrast-enhanced MRI-derived parameters decreased significantly in cediranib-treated tumors relative to pretreatment values [the muscle-normalized initial area under the gadolinium concentration curve decreased by 48% (p=0.002), the enhancing fraction by 43% (p=0.003) and K(trans) by 57% (p=0.003)], but remained unchanged in controls. No change between the pre- and post-treatment tumor apparent diffusion coefficients in either the cediranib- or vehicle-treated group was observed over the course of this study. The (18) F-fluoromisonidazole mean standardized uptake value decreased by 33% (p=0.008) in the cediranib group, but showed no significant change in the control group. Histological analysis showed that the number of CD31-positive vessels (59 per mm(2) ), the fraction of smooth muscle actin-positive vessels (80-87%) and the intercapillary distance (0.17 mm) were similar in cediranib- and vehicle-treated groups. The fraction of perfused blood vessels in cediranib-treated tumors (81 ± 7%) was lower than that in vehicle controls (91 ± 3%, p=0.02). The necrotic fraction was slightly higher in cediranib-treated rats (34 ± 12%) than in controls (26 ± 10%, p=0.23). These findings suggest that short-term treatment with cediranib causes a decrease in tumor perfusion/permeability across the tumor cross-section, but changes in vascular morphology, vessel density or tumor cellularity are not manifested at this early time point.

Semi-automatic deformable registration of prostate MR images to pathological slices.

PURPOSE: To present a semi-automatic deformable registration algorithm for co-registering T2-weighted (T2w) images of the prostate with whole-mount pathological sections of prostatectomy specimens. MATERIALS AND METHODS: Twenty-four patients underwent 1.5 Tesla (T) endorectal MR imaging before radical prostatectomy with whole-mount step-section pathologic analysis of surgical specimens. For each patient, the T2w imaging containing the largest area of tumor was manually matched with the corresponding pathologic slice. The prostate was co-registered using a free-form deformation (FFD) algorithm based on B-splines. Registration quality was assessed through differences between prostate diameters measured in right-left (RL) and anteroposterior (AP) directions on T2w images and pathologic slices and calculation of the Dice similarity coefficient, D, for the whole prostate (WP), the peripheral zone (PZ) and the transition zone (TZ). RESULTS: The mean differences in diameters measured on pathology and MR imaging in the RL direction and the AP direction were 0.49 cm and -0.63 cm, respectively, before registration and 0.10 cm and -0.11 cm, respectively, after registration. The mean D values for the WP, PZ and TZ, were 0.76, 0.65, and 0.77, respectively, before registration and increased to 0.91, 0.76, and 0.85, respectively, after registration. The improvements in D were significant for all three tissues (P < 0.001 for all). CONCLUSION: The proposed semi-automatic method enabled successful co-registration of anatomical prostate MR images to pathologic slices.

Use of cardiac output to improve measurement of input function in quantitative dynamic contrast-enhanced MRI.

PURPOSE: To validate a new method for converting MR arterial signal intensity versus time curves to arterial input functions (AIFs). MATERIALS AND METHODS: The method constrains AIF with patient's cardiac output (Q). Monte Carlo simulations of MR renography and tumor perfusion protocols were carried out for comparison with two alternative methods: direct measurement and population-averaged input function. MR renography was performed to assess the method's inter- and intraday reproducibility for renal parameters. RESULTS: In simulations of tumor perfusion, the precision of the parameters (K(trans) and v(e)) computed using the proposed method was improved by at least a factor of three compared to direct measurement. Similar improvements were obtained in simulations of MR renography. Volunteer study for testing interday reproducibility confirmed the improvement of precision in renal parameters when using the proposed method compared to conventional methods. In another patient study (two injections within one session), the proposed method significantly increased the correlation coefficient (R) between GFR of the two exams (0.92 vs. 0.83) compared to direct measurement. CONCLUSION: A new method significantly improves the precision of dynamic contrast-enhanced (DCE) parameters. The method may be especially useful for analyzing repeated DCE examinations, such as monitoring tumor therapy or angiotensin converting enzyme-inhibitor renography.

Optimal k-space sampling for dynamic contrast-enhanced MRI with an application to MR renography.

For time-resolved acquisitions with k-space undersampling, a simulation method was developed for selecting imaging parameters based on minimization of errors in signal intensity versus time and physiologic parameters derived from tracer kinetic analysis. Optimization was performed for time-resolved angiography with stochastic trajectories (TWIST) algorithm applied to contrast-enhanced MR renography. A realistic 4D phantom comprised of aorta and two kidneys, one healthy and one diseased, was created with ideal tissue time-enhancement pattern generated using a three-compartment model with fixed parameters, including glomerular filtration rate (GFR) and renal plasma flow (RPF). TWIST acquisitions with different combinations of sampled central and peripheral k-space portions were applied to this phantom. Acquisition performance was assessed by the difference between simulated signal intensity (SI) and calculated GFR and RPF and their ideal values. Sampling of the 20% of the center and 1/5 of the periphery of k-space in phase-encoding plane and data-sharing of the remaining 4/5 minimized the errors in SI (<5%), RPF, and GFR (both <10% for both healthy and diseased kidneys). High-quality dynamic human images were acquired with optimal TWIST parameters and 2.4 sec temporal resolution. The proposed method can be generalized to other dynamic contrast-enhanced MRI applications, e.g., MR angiography or cancer imaging.

Angiotensin-converting enzyme inhibitor-enhanced MR renography: repeated measures of GFR and RPF in hypertensive patients.

This study aims to assess the feasibility of a protocol to diagnose renovascular disease using dual MR renography acquisitions: before and after administration of angiotensin-converting enzyme inhibitor (ACEi). Results of our simulation study aimed at testing the reproducibility of glomerular filtration rate (GFR) and renal plasma flow demonstrate that for a fixed overall dose of 12 ml gadolinium-based contrast material (500 mmol/l), the second dose should be approximately twice as large as the first dose. A three-compartment model for analyzing the second-injection data was shown to appropriately handle the tracer residue from the first injection. The optimized protocol was applied to 18 hypertensive patients without renovascular disease, showing minimal systematic difference in GFR measurements before and after ACEi of 0.8 +/- 4.4 ml/min or 2.7 +/- 14.9%. For 10 kidneys with significant renal artery stenosis, GFR decreased significantly after ACEi (P < 0.001, T value = 3.79), and the difference in GFR measurements before and after ACEi averaged 8.3 +/- 6.9 ml/min or 26.2 +/- 43.9%. Dual-injection MRI with optimized dose distribution appears promising for ACEi renography by offering measures of GFR changes with clinically acceptable precision and accuracy.

Estimates of glomerular filtration rate from MR renography and tracer kinetic models.

PURPOSE: To compare six methods for calculating the single-kidney glomerular filtration rate (GFR) from T(1)-weighted magnetic resonance (MR) renography (MRR) against reference radionuclide measurements. MATERIALS AND METHODS: In 10 patients, GFR was determined using six published methods: the Baumann-Rudin model (BR), the Patlak-Rutland method (PR), the two-compartment model without bolus dispersion (2C) and with dispersion (2CD), the three-compartment model (3CD), and the distributed parameter model (3C-IRF). Reference single-kidney GFRs were measured by radionuclide renography. The coefficient of variation of GFR (CV) was determined for each method by Monte Carlo analyses for one healthy and one dysfunctional kidney at a noise level (sigma(n)) of 2%, 5%, and 10%. RESULTS: GFR estimates in patients varied from 6% overestimation (BR) to 50% underestimation (PR and 2CD applied to cortical data). Correlations with reference GFRs ranged from R = 0.74 (2CD, cortical data) to R = 0.85 (BR). In simulations, the lowest CV was produced by 3C-IRF in healthy kidney (1.7sigma(n)) and by PR in diseased kidney ((2.2-2.4)sigma(n)). In both kidneys the highest CV was obtained with 2CD ((5.9-8.2)sigma(n)) and with 3CD in diseased kidney (8.9sigma(n) at sigma(n) = 10%). CONCLUSION: GFR estimates depend on the renal model and type of data used. Two- and three-compartment models produce comparable GFR correlations.

Assessment of renal function with dynamic contrast-enhanced MR imaging.

MR imaging is a promising noninvasive modality that can provide a comprehensive picture of renal anatomy and function in a single examination. The advantages of MR imaging are its high contrast and temporal resolution and lack of exposure to ionizing radiation. In the past few years, considerable progress has been made in development of methods of renal functional MR imaging and their applications in various diseases. This article reviews the key factors for acquisition and analysis of dynamic contrast-enhanced renal MR imaging (MR renography) and the most significant developments in this field over the past few years.

Functional assessment of the kidney from magnetic resonance and computed tomography renography: impulse retention approach to a multicompartment model.

A three-compartment model is proposed for analyzing magnetic resonance renography (MRR) and computed tomography renography (CTR) data to derive clinically useful parameters such as glomerular filtration rate (GFR) and renal plasma flow (RPF). The model fits the convolution of the measured input and the predefined impulse retention functions to the measured tissue curves. A MRR study of 10 patients showed that relative root mean square errors by the model were significantly lower than errors for a previously reported three-compartmental model (11.6% +/- 4.9 vs 15.5% +/- 4.1; P < 0.001). GFR estimates correlated well with reference values by (99m)Tc-DTPA scintigraphy (correlation coefficient r = 0.82), and for RPF, r = 0.80. Parameter-sensitivity analysis and Monte Carlo simulation indicated that model parameters could be reliably identified. When the model was applied to CTR in five pigs, expected increases in RPF and GFR due to acetylcholine were detected with greater consistency than with the previous model. These results support the reliability and validity of the new model in computing GFR, RPF, and renal mean transit times from MR and CT data.

Quantitative determination of Gd-DTPA concentration in T1-weighted MR renography studies.

A method for calculating contrast agent concentration from MR signal intensity (SI) was developed and validated for T(1)-weighted MR renography (MRR) studies. This method is based on reference measurements of SI and relaxation time T(1) in a Gd-DTPA-doped water phantom. The same form of SI vs. T(1) dependence was observed in human tissues. Contrast concentrations calculated by the proposed method showed no bias between 0 and 1 mM, and agreed better with the reference values derived from direct T(1) measurements than the concentrations calculated using the relative signal method. Phantom-based conversion was used to determine the contrast concentrations in kidney tissues of nine patients who underwent dynamic Gd-DTPA-enhanced 3D MRR at 1.5T and (99m)Tc-DTPA radionuclide renography (RR). The concentrations of both contrast agents were found to be close in magnitude and showed similar uptake and washout behavior. As shown by Monte Carlo simulations, errors in concentration due to SI noise were below 10% for SNR = 20, while a 10% error in precontrast T(1) values resulted in a 12-17% error for concentrations between 0.1 and 1 mM. The proposed method is expected to be particularly useful for assessing regions with highly concentrated contrast.

Performance of an automated segmentation algorithm for 3D MR renography.

The accuracy and precision of an automated graph-cuts (GC) segmentation technique for dynamic contrast-enhanced (DCE) 3D MR renography (MRR) was analyzed using 18 simulated and 22 clinical datasets. For clinical data, the error was 7.2 +/- 6.1 cm(3) for the cortex and 6.5 +/- 4.6 cm(3) for the medulla. The precision of segmentation was 7.1 +/- 4.2 cm(3) for the cortex and 7.2 +/- 2.4 cm(3) for the medulla. Compartmental modeling of kidney function in 22 kidneys yielded a renal plasma flow (RPF) error of 7.5% +/- 4.5% and single-kidney GFR error of 13.5% +/- 8.8%. The precision was 9.7% +/- 6.4% for RPF and 14.8% +/- 11.9% for GFR. It took 21 min to segment one kidney using GC, compared to 2.5 hr for manual segmentation. The accuracy and precision in RPF and GFR appear acceptable for clinical use. With expedited image processing, DCE 3D MRR has the potential to expand our knowledge of renal function in individual kidneys and to help diagnose renal insufficiency in a safe and noninvasive manner.

What causes diminished corticomedullary differentiation in renal insufficiency?

PURPOSE: To investigate whether the loss of corticomedullary differentiation (CMD) on T1-weighted MR images due to renal insufficiency can be attributed to changes in T1 values of the cortex, medulla, or both. MATERIALS AND METHODS: Study subjects included 10 patients (serum creatinine range 0.6-3.0 mg/dL) referred for suspected renovascular disease who underwent 99mTc-diethylene triamine pentaacetic acid (DTPA) renography to determine single kidney glomerular filtration rate (SKGFR) and same-day MRI, which included T1 measurements and unenhanced T1-weighted gradient echo imaging. Corticomedullary differentiation on T1-weighted images was assessed qualitatively and quantitatively. RESULTS: SKGFR values ranged from 3.5 to 89.4 mL/minute based on radionuclide studies. T1 relaxation times of the medulla exceeded those of renal cortex by 147.9+/-176.0 msec (mean+/-standard deviation [SD]). Regression analysis showed a negative correlation between cortex T1 and SKGFR (r=-0.5; P=0.03), whereas there was no significant correlation between medullary T1 and SKGFR. The difference between medullary and cortical T1s correlated significantly with SKGFR (r=0.58; P<0.01). In all five kidneys with a corticomedullary contrast-to-noise ratio (CNR)<5.0 on T1-weighted images, SKGFR was less than 20 mL/minute. CONCLUSION: In our subject population, loss of CMD with decreasing SKGFR can be attributed primarily to an increased T1 relaxation time of the cortex. Medullary T1 values vary but do not appear to correlate with degree of renal insufficiency.

Renal function measurements from MR renography and a simplified multicompartmental model.

The purpose of this study was to determine the accuracy and sources of error in estimating single-kidney glomerular filtration rate (GFR) derived from low-dose gadolinium-enhanced T1-weighted MR renography. To analyze imaging data, MR signal intensity curves were converted to concentration vs. time curves, and a three-compartment, six-parameter model of the vascular-nephron system was used to analyze measured aortic, cortical, and medullary enhancement curves. Reliability of the parameter estimates was evaluated by sensitivity analysis and by Monte Carlo analyses of model solutions to which random noise had been added. The dominant sensitivity of the medullary enhancement curve to GFR 1-4 min after tracer injection was supported by a low coefficient of variation in model-fit GFR values (4%) when measured data were subjected to 5% noise. These analyses also showed the minimal effects of bolus dispersion in the aorta on parameter reliability. Single-kidney GFR from MR renography analyzed by the three-compartment model (4.0-71.4 ml/min) agreed well with reference measurements from (99m)Tc-DTPA clearance and scintigraphy (r = 0.84, P < 0.001). Bland-Altman analysis showed an average difference of 11.9 ml/min (95% confidence interval = 5.8-17.9 ml/min) between model and reference values. We conclude that a nephron-based multicompartmental model can be used to derive clinically useful estimates of single-kidney GFR from low-dose MR renography.