Rapid Diffusion Magnetic Resonance Imaging Using Slice-Interleaved Encoding.
In: Medical image analysis, Jg. 81 (2022-10-01), S. 102548
academicJournal
Zugriff:
In this paper, we present a robust reconstruction scheme for diffusion MRI (dMRI) data acquired using slice-interleaved diffusion encoding (SIDE). When combined with SIDE undersampling and simultaneous multi-slice (SMS) imaging, our reconstruction strategy is capable of significantly reducing the amount of data that needs to be acquired, enabling high-speed diffusion imaging for pediatric, elderly, and claustrophobic individuals. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume per diffusion wavevector, SIDE acquires in each repetition time (TR) a volume that consists of interleaved slice groups, each group corresponding to a different diffusion wavevector. This strategy allows SIDE to rapidly acquire data covering a large number of wavevectors within a short period of time. The proposed reconstruction method uses a diffusion spectrum model and multi-dimensional total variation to recover full DW images from DW volumes that are slice-undersampled due to unacquired SIDE volumes. We formulate an inverse problem that can be solved efficiently using the alternating direction method of multipliers (ADMM). Experiment results demonstrate that DW images can be reconstructed with high fidelity even when the acquisition is accelerated by 25 folds.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)
Titel: |
Rapid Diffusion Magnetic Resonance Imaging Using Slice-Interleaved Encoding.
|
---|---|
Autor/in / Beteiligte Person: | Xu, T ; Wu, Y ; Hong, Y ; Ahmad, S ; Huynh, KM ; Wang, Z ; Lin, W ; Chang, WT ; Yap, PT |
Zeitschrift: | Medical image analysis, Jg. 81 (2022-10-01), S. 102548 |
Veröffentlichung: | Amsterdam : Elsevier ; <i>Original Publication</i>: London : Oxford University Press, [1996-, 2022 |
Medientyp: | academicJournal |
ISSN: | 1361-8423 (electronic) |
DOI: | 10.1016/j.media.2022.102548 |
Schlagwort: |
|
Sonstiges: |
|