Artificial Neural Networks approach to pharmacokinetic model selection in DCE-MRI studies

Multiparametric MRI based assessment of kidney injury in a mouse model of ischemia reperfusion injury

Kidney diseases pose a global healthcare burden, with millions requiring renal replacement therapy. Ischemia/reperfusion injury (IRI) is a common pathology of acute kidney injury, causing hypoxia and subsequent inflammation-induced kidney damage. Accurate detection of acute kidney injury due to IRI is crucial for timely intervention. We used longitudinal, multi-parametric magnetic resonance imaging (MRI) employing arterial spin labelling (ASL), diffusion weighted imaging (DWI), and dynamic contrast enhanced (DCE)-MRI to assess IRI induced changes in both the injured and healthy contralateral kidney, in a unilateral IRI mouse model (n = 9). Multi-parametric MRI demonstrated significant differences in kidney volume (p = 0.001), blood flow (p = 0.002), filtration coefficient (p = 0.038), glomerular filtration rate (p = 0.005) and apparent diffusion coefficient (p = 0.048) between the injured kidney and contralateral kidney on day 1 post-IRI surgery. Identification of the injured kidney using principal component analysis including most of the imaging parameters demonstrated an area under the curve (AUC) of 0.97. These results point to the utility of multi-parametric MRI in early detection of IRI-induced kidney damage suggesting that the combination of various MRI parameters may be suitable for monitoring the extent of injury in this model.