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. One technique to determine renal function consists of the intravenous injection of radioactive tracer followed by assessment of its plasma clearance 2-4 hr later. This technique is time-consuming, requires multiple blood samples, and measures only the global glomerular filtration rate (GFR)-a disadvantage when asymmetric or unilateral renal disease is present. The gold standard technique for assessing single-kidney GFR is inulin clearance, but this method is too invasive and complex for routine clinical application. As an alternative, dynamic gamma camera imaging with 99m Tc-DTPA has been shown to provide single-kidney GFR by analysis of the renal radioactivity. By combining measures of renal physiology with depiction of anatomical detail, dynamic contrast-enhanced (DCE) 3D MR renography (MRR) has the potential to improve upon nuclear medicine techniques and also provide useful functional information to supplement anatomic renal MRI examinations (1). Good spatial, temporal, and contrast resolution is achievable with current contrast-enhanced dynamic protocols, whereby serial 3D MR images of the kidneys are generated following an injection of contrast material. Gadolinium (Gd) chelates, such as gadopentetate dimeglumine (Gd-DTPA), are suitable MR contrast agents because they are freely filtered at the glomerulus without tubular secretion or resorption.Several approaches have been proposed to analyze renography data, including the upslope method (2), deconvolution (3,4), the Rutland-Patlak method (5,6), and renal kinetic modeling (7-10). The key prerequisite is the ability to segment dynamic MR images into functional regions (i.e., the cortical and medullary compartments). Fitting concentration-time activity (CTA) curves to kinetic models yields perfusion and filtration rates per unit volume of tissue. These kinetic rates multiplied by the cortical and medullary volumes (determined from segmented images) give the renal plasma flow (RPF) and GFR for the entire kidney.Accurate segmentation of contrast-enhanced MRR data remains a difficult task. Dynamic 3D MR images of the abdomen suffer from partial-volume and respiratory-motion artifacts and have a relatively low signal-to-noise ratio (SNR). Other sources of error include signal nonuniformity and wraparound artifacts. The presence of cysts and renal masses, and reduced...