Deep learning-based CEST MRI classification of injured kidneys in multiple preclinical models
Chongxue Bie1,2,3, Zheng Han1,2, Peter C. M. van Zijl1,2, Nirbhay N. Yadav1,2, and Guanshu Liu1,2
1F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, 2The Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 3Department of Information Science and Technology, Northwest University, Xi'an, China
Accurate detection of kidney injury is of immense importance for the diagnosis and treatment of acute kidney injury (AKI). While CEST MRI has the potential to reveal the pathophysiological changes on a molecular level, no automatic, CEST-based classification model has been developed. We developed a deep neural network (DNN) to analyze features of the Z-spectral data and to classify injured and healthy renal tissues. The results show that the classification model was capable of reliable prediction of kidney injury among different AKI mouse models. Results correlated well with serum creatinine (SCr) measurement.
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