Abstract #4417

Deep learning-based CEST MRI classification of injured kidneys in multiple preclinical models

Chongxue Bie^1,2,3, Zheng Han^1,2, Peter C. M. van Zijl^1,2, Nirbhay N. Yadav^1,2, and Guanshu Liu^1,2

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

Synopsis

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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