Deep learning based chemical shift artifact reduction in Zero Echo Time (ZTE) MRI

Motivation: Chemical Shift artifacts in non-Cartesian MRI scans can lead to blurring and other artifacts at tissue interfaces. ZTE scans are particularly sensitive to this issue. Goal(s): Addressing this artifact can enable the generation of more accurate ZTE images. Additionally, subsequent post-processing tasks like bone volume rendering, pseudo CT etc., can benefit from mitigating this artifact. Approach: We introduce a deep learning based method to address this artifact and demonstrate its performance on phantom and in-vivo cases. Results: The results demonstrate that gross chemical shift artifact can be corrected using the proposed method. Impact: ZTE suffers from poor intrinsic SNR and chemical shift related blurring. Scanning at high field helps SNR but makes blurring more serious. Our proposed method helps mitigate chemical shift artifacts and opens up new possibilities for ZTE imaging.