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

Zhang, Jeff L.; Rusinek, Henry; Bokacheva, Louisa; Chen, Qun; Storey, Pippa; Lee, Vivian S.

doi:10.1002/jmri.21891

Cited by 35 publications

(35 citation statements)

References 51 publications

(73 reference statements)

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“…Gadolinium concentration was estimated using the ratio of the magnetization equations for the pre-and postcontrast images. 28 The mean vascular fraction was then multiplied by the total cortical volume to yield an estimate of AVC. AVC was examined as both a hemodynamic variable and a structure variable, because it could be a marker of either vascular tone or microvascular rarefaction, and distinguishing between the two was not possible from the imaging data.…”

Section: Image Acquisition and Analysismentioning

confidence: 99%

Mediation Analysis of Aortic Stiffness and Renal Microvascular Function

Woodard

Sigurðsson

Gotal

et al. 2015

Journal of the American Society of Nephrology

101

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Aortic stiffening, assessed by carotid-femoral pulse wave velocity, is associated with CKD. Transmission of excessive flow pulsatility into the low-impedance renal microvasculature may mediate this association. However, direct analyses of macrovascular-microvascular relations in the kidney are limited. Using arterial tonometry, iohexol clearance, and magnetic resonance imaging, we related arterial stiffness, GFR, urinary albumin excretion, and potential mediators, including renal artery pulsatility index, renal vascular resistance, and arterial volume in the cortex, in 367 older adults (ages 72-92 years) participating in the Age, Gene/ Environment Susceptibility-Reykjavik Study. In a model adjusted for age, sex, heart rate, and body size, aortic stiffness was related to GFR (Slope of regression B=22.2860.85 ml/min per SD, P=0.008) but not urine albumin (P=0.09). After accounting for pulsatility index, the relation between aortic stiffness and GFR was no longer significant (P=0.10). Mediation analysis showed that 34% of the relation between aortic stiffness and GFR was mediated by pulsatility index (95% confidence interval of indirect effect, 21.35 to 20.29). An additional 20% or 36% of the relation was mediated by lower arterial volume in the cortex or higher renal vascular resistance, respectively, when offered as mediators downstream from higher pulsatility index (95% confidence interval of indirect effect including arterial volume in the cortex, 22.22 to 20.40; 95% confidence interval of indirect effect including renal vascular resistance, 22.51 to 20.76). These analyses provide the first evidence that aortic stiffness may contribute to lower GFR by transferring excessive flow pulsatility into the susceptible renal microvasculature, leading to dynamic constriction or vessel loss.

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Section: Image Acquisition and Analysismentioning

confidence: 99%

Mediation Analysis of Aortic Stiffness and Renal Microvascular Function

Woodard

Sigurðsson

Gotal

et al. 2015

Journal of the American Society of Nephrology

101

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“…The size of this component is approximately 0.02 min ) ( 3-5,32 ). These differences in absolute K trans may be owing to differences in imaging protocol and problems with absolute quantifi cation (12)(13)(14)(15).…”

Section: Roi Analysis Parametersmentioning

confidence: 99%

“…However, quantifi cation of DCE MR imaging data is diffi cult because of the complex relation between the signal intensity and the concentration of the contrast agent in tissue ( 12 ). In particular, the determination of the arterial input function (AIF) required for tracer kinetic modeling can be unreliable (13)(14)(15).…”

mentioning

confidence: 99%

Dynamic Contrast-enhanced CT for Prostate Cancer: Relationship between Image Noise, Voxel Size, and Repeatability

Korporaal

Berg²,

Jeukens³

et al. 2010

Radiology

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Purpose:To evaluate the relationship between image noise, voxel size, and voxel-wise repeatability of a dynamic contrast agent-enhanced (DCE) computed tomographic (CT) examination for prostate cancer. Materials and Methods:This prospective study was approved by the local research ethics committee, and all patients gave written informed consent. Twenty-nine patients (mean age, 69.1 years; range, 56-80 years) with biopsy-proved prostate cancer underwent two DCE CT examinations within 1 week prior to radiation therapy. Parameter maps of transfer constant ( K trans ), the fraction of blood plasma (v p ) , the fraction of extravascular extracellular space (v e ) , and the fl ux rate constant between the extravascular extracellular space and plasma ( k ep ) were calculated at 15 different image resolutions, with kernel sizes ranging from 0.002 to 2.57 cm 3 . Statistical analysis to quantify the voxel-wise repeatability was performed by using a Bland-Altman analysis on all tracer kinetic model parameter maps of each patient. From this analysis, the within-voxel standard deviation (wSD) was calculated as a function of spatial resolution. Results:A kernel size in the range of 0. Conclusion:There is a high voxel-wise repeatability of the DCE CT imaging technique for prostate cancer for kernel sizes as small as 0.1 cm 3 . With the relationship between kernel size, image noise and voxel-wise repeatability, it becomes possible to estimate for alternative DCE CT protocols (eg, those with a reduced radiation dose) at what kernel size a suffi cient repeatability can be obtained.q RSNA, 2010

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“…Accurate measurement of AIF is critical for DCE MRI . Our preliminary study indicated that arterial blood experiences a wide range of tracer concentration, and so, for the SR sequence, TD needs to be properly selected to estimate all possible concentration values accurately.…”

Section: Methodsmentioning

confidence: 98%

Optimization of saturation-recovery dynamic contrast-enhanced MRI acquisition protocol: monte carlo simulation approach demonstrated with gadolinium MR renography

et al. 2016

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Dynamic contrast-enhanced (DCE) MRI is widely used for the measurement of tissue perfusion and to assess organ function. MR renography, which is acquired using a DCE sequence, can measure renal perfusion, filtration and concentrating ability. Optimization of the DCE acquisition protocol is important for the minimization of the error propagation from the acquired signals to the estimated parameters, thus improving the precision of the parameters. Critical to the optimization of contrast-enhanced T1-weighted protocols is the balance of the T1-shortening effect across the range of gadolinium (Gd) contrast concentration in the tissue of interest. In this study, we demonstrate a Monte Carlo simulation approach for the optimization of DCE MRI, in which a saturation-recovery T1-weighted gradient echo sequence is simulated and the impact of injected dose (D) and time delay (TD, for saturation recovery) is tested. The results show that high D and/or high TD cause saturation of the peak arterial signals and lead to an overestimation of renal plasma flow (RPF) and glomerular filtration rate (GFR). However, the use of low TD (e.g. 100 ms) and low D leads to similar errors in RPF and GFR, because of the Rician bias in the pre-contrast arterial signals. Our patient study including 22 human subjects compared TD values of 100 and 300 ms after the injection of 4 mL of Gd contrast for MR renography. At TD = 100 ms, we computed an RPF value of 157.2 ± 51.7 mL/min and a GFR of 33.3 ± 11.6 mL/min. These results were all significantly higher than the parameter estimates at TD = 300 ms: RPF = 143.4 ± 48.8 mL/min (p = 0.0006) and GFR = 30.2 ± 11.5 mL/min (p = 0.0015). In conclusion, appropriate optimization of the DCE MRI protocol using simulation can effectively improve the precision and, potentially, the accuracy of the measured parameters.

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

Cited by 35 publications

References 51 publications

Mediation Analysis of Aortic Stiffness and Renal Microvascular Function

Mediation Analysis of Aortic Stiffness and Renal Microvascular Function

Dynamic Contrast-enhanced CT for Prostate Cancer: Relationship between Image Noise, Voxel Size, and Repeatability

Optimization of saturation-recovery dynamic contrast-enhanced MRI acquisition protocol: monte carlo simulation approach demonstrated with gadolinium MR renography

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