Validation of deep-learning accelerated quantitative susceptibility imaging for application in deep brain nuclei

Motivation: Quantitative susceptibility imaging (QSM) has demonstrated its potential in clinical applications. In patients with Parkinson’s disease, stroke, etc., a shorter acquisition time is desired. Goal(s): Here we aim to validate the accuracy of a deep learning (DL) based method for accelerating QSM in human volunteers. Approach: We enrolled 59 participants from communities and acquired both routine QSM and DL-QSM images. We measured iron deposition in deep brain nucleus and studied the influence of different acceleration factors (3,4, and 5). Results: Results showed that susceptibility values from DL-QSM are highly consistent with routine parallel imaging accelerated images, and they also correlated well with age. Impact: As we validated the reliability and accuracy of deep-learning accelerated quantitative susceptibility imaging, future clinical studies can use this method on patients who cannot tolerate long scan time.