Motivation: Adoption of QSM in Multiple Sclerosis(MS) is constrained by the trade-off between acquisition time and image quality of clinically-applicable protocols. Goal(s): Evaluating the potential of a deep learning-based reconstruction(DLR),denoising and super-resolution pipeline to enhance the clinical utility of 3DEPI-based QSM in MS. Approach: χ-maps derived from DLR- and conventionally-reconstructed(CR)-GRE,1 and 2-averages-3DEPI from a healthy individual were quantitatively compared. DLR- and CR-3DEPI-based χ-maps from 7 MS-patients were rated for MS-specific biomarkers identification and quality. Results: DLR-3DEPI-based QSM was comparable to state-of-the-art GRE-based QSM,with 4-fold reduction in acquisition time. Ratings showed significant improvement in MS-specific biomarkers identification and image quality for DLR-3DEPI-based vs CR-3DEPI-based QSM. Impact: Deep learning-based reconstruction, denoising and super-resolution pipeline substantially enhances the quality of QSM maps obtained from fast 3DEPI. This holds promise for advancing the broader implementation of QSM in the clinical management of Multiple Sclerosis.
Gkotsoulias et al. (Tue,) studied this question.