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Background  Obstructed nephropathy is a common complication of several disease processes. Accurate evaluation of the functional status of the obstructed kidney is important to achieve a good outcome. The purpose of this study was to investigate renal cortical and medullary perfusion changes associated with unilateral ureteral obstruction (UUO) using whole-organ perfusion imaging with 320-detector row computed tomography (CT).    Methodology/Principle Findings  Sixty-four patients with UUO underwent whole-organ CT perfusion imaging. Patients were divided into 3 groups, mild, moderate, and severe, based on hydronephrosis severity. Twenty sex- and age-matched patients without renal disease, who referred to abdominal CT, were chosen as control subjects. Mean cortical and medullary perfusion parameters of obstructed and contralateral kidneys were compared, and mean perfusion ratios between obstructed and contralateral kidneys were calculated and compared. Mean cortical or medullary blood flow (BF) and blood volume (BV) of the obstructed kidneys in the moderate UUO and BF, BV, and clearance (CL) in the severe UUO were significantly lower than those of the contralateral kidneys (p < 0.05). The mean cortical or medullary BF of the obstructed kidney in the moderate UUO, and BF, BV, and CL in the severe UUO were significantly lower than those of the kidneys in control subjects (p < 0.05). Mean cortical or medullary BF of the non-obstructed kidneys in the severe UUO were statistically greater than that of normal kidneys in control subjects (p < 0.05). An inverse correlation was observed between cortical and medullary perfusion ratios and grades of hydronephosis (p < 0.01).    Conclusions/Significance  Perfusion measurements of the whole kidney can be obtained with 320-detector row CT, and estimated perfusion ratios have potential for quantitatively evaluating UUO renal injury grades.

plos one

Assessment of Renal Function in Patients with Unilateral Ureteral Obstruction Using Whole-Organ Perfusion Imaging with 320-Detector Row Computed Tomography