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

Abstract

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. Key words: computed tomography; glomerular filtration rate; impulse retention function; magnetic resonance renography; renal plasma flow MR renography (MRR) and computed tomography renography (CTR) are increasingly used for noninvasive measurement of single-kidney function (1-7). These dynamic imaging techniques record the transit of a tracer, such as Gd-DTPA or iodinated contrast agents, from the aorta through the renal system. Tracer activity versus time curves can then be derived for intrarenal regions such as renal cortex, medulla, and collecting system. Design of an appropriate physiologic model is an essential part of accurate quantification of renal function (1,2).Several models have been proposed to estimate glomerular filtration rate (GFR) from MRR (3-6) and CTR (7). Baumann and Rudin (3) computed the GFR from the medullary uptake of the tracer using the cortical concentration as the input function. Another method (4) used a PatlakRutland plot to estimate GFR from the clearance of the tracer from the vascular compartment. This approach used whole-kidney concentration, obviating the need for regional segmentation of the kidneys. Both of these methods ignored the outflow of the tracer, and the results can be biased by improper selection of the "upslope" interval. Annet et al. (5) extended these techniques to account for tracer leaving the nephron space, thus enabling fitting of the model to measured data over a longer time period. All of these models assume instantaneous mixing of tracer within every compartment.More recently, models have been proposed with the aim of extending physiologic measures beyond GFR. Krier et al. (7) represented the cortex and medulla curves as extended gamma-variate functions with parameters shown to yield renal plasma flow (RPF) and tubular transit times in addition to GFR. GFR and RPF measures were validated against the reference values in pig model using CT renography. Hermoye et al. (8) determined RPF and GFR in rabbits from the cortical impulse response function by numerical deconvolution of renal cortical enhancement curves. The impulse response function exhibited three sequential peaks presumed to reflect the contrast in glomeruli, pro...

Quantification of renal perfusion: Comparison of arterial spin labeling and dynamic contrast‐enhanced MRI

“…Likewise, previous applications of the separable compartment model did not focus on the medulla but fitted instead the mean whole kidney, mean cortical, and pixel-wise cortical DCE signal changes (5). More complex compartmental models that account for dynamic contrast changes are likely required to provide estimates of global medulla perfusion (9).…”

“…In principle, perfusion can be obtained in addition to the GFR (3)(4)(5), but pharmacokinetic models of higher complexity are required in the kidney to distinguish perfusion from filtration-influenced contrast distribution. Various models have been proposed: deconvolution (6,7), 2-compartment separable compartment model (3)(4)(5), 3-compartment models (8,9), and a 7-compartment model (10). The potential to reap greater specificity from using higher-order models depends on the available signal-to-noise (SNR) and the ability to obtain a high temporal resolution arterial input function (AIF).…”

A renal vascular compartment segmentation method based on dynamic contrast-enhanced images

“…The reference GFR was obtained from 99mTc-DTPA renal scintigraphy [10,19,20]. All subjects underwent 99mTc-DTPA scintigraphy first and gadopentetate dimeglumine (Gd-DTPA) dynamic contrastenhanced MR examinations afterwards.…”

“…Lee et al [9] proposed a multi-compartmental model which described three compartments including vascular compartment, proximal tubules and loops of Henle. Based on Lee's model, Zhang et al [10] assumed that due to a distribution of pathways, the contrast took a minimum transit time to traverse through each compartment before emerging at the outlet. Hence, an impulse retention function (IRF) was applied to this model.…”

Renovascular imaging in the NSF Era

“…7) (62,(64)(65)(66)(67)(68)(69)(70)(71)(72). To data there are only a few clinical studies measuring renal perfusion.…”