Keywords: Other AI/ML, AI/ML Software, Arterial Spin Labeling, Chronic Kidney Disease, Deep Learning, Image Synthesis, Perfusion Assessment, Pix2pix Algorithm
Motivation: Arterial spin labeling (ASL), an MRI technique reflecting perfusion without contrast agents, can diagnose chronic kidney disease (CKD) but increases scan imaging cost and scan time.
Goal(s): To explore the feasibility of generating virtual ASL from conventional MRI sequences.
Approach: A model was developed using the pix2pix algorithm to generate virtual renal ASL sequences from T1 and T2-weighted imaging and diffusion-weighted imaging, and to measure renal blood flow values. Intraclass correlation coefficients, paired t tests, and receiver operating characteristic curves were used for data analysis.
Results: Virtual ASL sequences could be generated through deep learning using conventional MRI sequences and could diagnose CKD.
Impact: This virtual ASL technique enables non-invasive renal perfusion assessment based on conventional MRI sequences, potentially expanding access to perfusion imaging in CKD diagnosis and monitoring. Future studies can explore its application in treatment response prediction.
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