Dynamic Contrast-Enhanced MR Urography in the Evaluation of Pediatric Hydronephrosis: Part 1, Functional Assessment

Jones, Richard A.; Easley, Kirk A.; Little, Stephen B.; Scherz, Hal C.; Kirsch, Andrew J.; Grattan‐Smith, J. Damien

doi:10.2214/ajr.04.1540

Cited by 99 publications

(54 citation statements)

References 24 publications

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“…The smaller dose of contrast results in a lower signal-to-noise ratio in the resultant images, and may reduce our ability to distinguish between patients with different degrees of liver disease. However, a small dose of contrast for DCE MR imaging also has several benefits, including: 1) providing a relatively linear relationship between signal intensity and concen- tration of contrast material (13)(14)(15); and 2) allowing for subsequent diagnostic MRI with a full bolus of contrast material. Fourth, our images were acquired once every four seconds while those of Annet et al (7) were acquired approximately once per second.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the low concentration of intravenous gadolinium contrast material administered for our timing bolus examinations, a linear relationship was assumed between changes in signal intensity and changes in gadolinium concentrations. This assumption is based on previous work on measurement of gadolinium-diethylene triamine pentaacetic acid (Gd-DTPA) concentration in vivo and in vitro (13)(14)(15). Two readers without knowledge of the pathological or clinical information independently reviewed all timing bolus MR images.…”

Section: Image Analysismentioning

confidence: 99%

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Timing bolus dynamic contrast‐enhanced (DCE) MRI assessment of hepatic perfusion: Initial experience

Baxter

Wang

Joe

et al. 2009

Magnetic Resonance Imaging

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Purpose: To assess whether dynamic contrast-enhanced (DCE) MRI timing bolus data from routine clinical examinations can be postprocessed to obtain hepatic perfusion parameters for diagnosing cirrhosis. Materials and Methods:We retrospectively identified 57 patients (22 with cirrhosis and 35 without cirrhosis) who underwent abdominal MRI, which included a low-dose (2 mL gadodiamide) timing bolus using a volumetric spoiled gradient echo T1-weighted sequence through the abdomen. Using a dual-input single-compartment model, the following perfusion parameters were measured: arterial, portal, and total blood flow; arterial fraction; mean transit time; and distribution volume. Those parameters were compared between patients with and without cirrhosis using t-tests. Receiver operating characteristic (ROC) curve analysis was used to identify the perfusion parameters that can best predict the presence of cirrhosis. Results:The hepatic arterial fraction, arterial flow, and distribution volume in patients with cirrhosis (27.7 Ϯ 8.3%, 44.8 Ϯ 14.1 mL/minute/100 g, and 16.3 Ϯ 4.5%, respectively) were significantly higher than those without cirrhosis (18.7 Ϯ 4.4%, 28.5 Ϯ 11.7 mL/minute/100 g, and 14.0 Ϯ 4.2%, respectively; P Ͻ 0.05 for all). ROC analysis showed arterial fraction as the best predictor of cirrhosis, with sensitivity of 73% and specificity of 86%. Conclusion:Timing bolus DCE MR images from routine examinations can be postprocessed to yield potentially useful hepatic perfusion parameters.

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Section: Discussionmentioning

confidence: 99%

Section: Image Analysismentioning

confidence: 99%

Timing bolus dynamic contrast‐enhanced (DCE) MRI assessment of hepatic perfusion: Initial experience

Baxter

Wang

Joe

et al. 2009

Magnetic Resonance Imaging

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“…[1]. The proportionality coefficient k depends on the T 1 0 and can be determined for a given tissue as the average slope of the concentration vs. the predicted ⌬S in the relevant concentration range.…”

Section: Relative Si Conversion Methodsmentioning

confidence: 99%

“…For the relative signal method, the coefficient k in Eq. [1] was determined from the slope of the concentration vs. the predicted ⌬SI between 0 and 0.5 mM.…”

Section: Calculation Of Mrr Contrast Concentrationsmentioning

confidence: 99%

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

Bokacheva

Rusinek

Chen

et al. 2007

Magnetic Resonance in Med

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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. Phantombased 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. Quantitative determination of in vivo contrast agent concentration is required in many MR studies that involve perfusion, tracer kinetics, and tissue permeability. The main difficulty of making such estimates arises from the nonlinearity of the MR signal intensity (SI) with the concentration. Additional challenges emerge from the dependence of the concentration on precontrast relaxation times T 1 and T 2 and other difficult-to-control factors, such as flow and respiratory motion.Several methods have been developed for estimating the contrast agent concentration. Most commonly, relative SI change values ⌬S ϭ ͑S Ϫ S 0 ͒/S 0 , where S 0 and S are the pre-and postcontrast SI, are scaled by a constant k to approximate the concentration c ⌬S (1):[1]Alternatively, the acquisition may be designed so that the SI is approximately linear with gadolinium (Gd) concentration over a clinically relevant range (2,3). This method is restrictive and may result in incorrect concentration estimates, especially in regions with high contrast concentrations (e.g., the kidneys). Another approach is to use known sequence parameters and an analytical calibration relationship between SI and T 1 values, which is available for some sequences. However, the validity of some nominal parameters, such as the effective flip angle, has been shown to be problematic (4). Recently several groups have developed a method for calculating the concentration of gadopentetate dimeglumine (Gd-DTPA) from the MR SI based on phantom measurements of signal enhancement over a range of T 1 values (5,6). This method is free from assumptions of linearity between SI and concentration, and may be applied to any pulse sequence. It has the potential to be more accurate than the linear approximation or the analytical calibration methods described above.In this study we demonstrate that the phantom-based method is free from bias for Gd concentrations between 0 and 1 mM. We apply this method to the analysis of the dynamic, Gd-enhanced 3D MR renography (MRR). We compare the results with those obtained using the relative SI change conversion method. In addition, we compare G...

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

Zhang

Rusinek

Bokacheva

et al. 2009

Magnetic Resonance Imaging

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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 interand 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.

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Dynamic Contrast-Enhanced MR Urography in the Evaluation of Pediatric Hydronephrosis: Part 1, Functional Assessment

Abstract: Combining signal-intensity-versus-time-curve analysis with the other parameters that can be derived from the same MR urography data set provides a powerful tool for the diagnosis of obstruction.

Cited by 99 publications

References 24 publications

Timing bolus dynamic contrast‐enhanced (DCE) MRI assessment of hepatic perfusion: Initial experience

Timing bolus dynamic contrast‐enhanced (DCE) MRI assessment of hepatic perfusion: Initial experience

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

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

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Dynamic Contrast-Enhanced MR Urography in the Evaluation of Pediatric Hydronephrosis: Part 1, Functional Assessment

Abstract: Combining signal-intensity-versus-time-curve analysis with the other parameters that can be derived from the same MR urography data set provides a powerful tool for the diagnosis of obstruction.

Cited by 99 publications

References 24 publications

Timing bolus dynamic contrast‐enhanced (DCE) MRI assessment of hepatic perfusion: Initial experience

Timing bolus dynamic contrast‐enhanced (DCE) MRI assessment of hepatic perfusion: Initial experience

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

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

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