Motivation: Respiration motion can induce in-accurate quantization of CEST signal, which may hindering the clinical application of CEST-MRI for nephropathy detection. Goal(s): We aimed to develop a stable renal CEST-MRI method employing self-navigated motion-correction (SNMC) without the need for breath holds. Approach: The respiratory motion signal from the center data of golden-angle radial sampling was used to split the motion states, the motion fields obtained by elastic registration are used for the final reconstruction of the CEST images. Results: Compared to the motion-average CEST images, SNMC-CEST images exhibited reduced motion artifacts, clearer margins of the kidneys, and high repeatability of CEST signal. Impact: This study may provide a stable renal CEST-MRI method for patients with nephropathy under free breathing, and improving the accuracy of injury detection.
Quan et al. (2025) studied this question.