Functional MRI (fMRI) is commonly used for interpreting neural activities across the brain. Numerous accelerated fMRI techniques aim to provide improved spatiotemporal resolutions. Among these, simultaneous multi-slice (SMS) imaging has emerged as a powerful strategy, becoming a part of large-scale studies, such as the Human Connectome Project. However, when SMS imaging is combined with in-plane acceleration for higher acceleration rates, conventional SMS reconstruction methods may suffer from noise amplification and other artifacts. Recently, deep learning (DL) techniques have gained interest for improving MRI reconstruction. However, these methods are typically trained in a supervised manner that necessitates fully-sampled reference data, which is not feasible in highly-accelerated fMRI acquisitions. Self-supervised learning that does not require fully-sampled data has recently been proposed and has shown similar performance to supervised learning. However, it has only been applied for in-plane acceleration. Furthermore the effect of DL reconstruction on subsequent fMRI analysis remains unclear. In this work, we extend self-supervised DL reconstruction to SMS imaging. Our results on prospectively 10-fold accelerated 7T fMRI data show that self-supervised DL reduces reconstruction noise and suppresses residual artifacts. Subsequent fMRI analysis remains unaltered by DL processing, while the improved temporal signal-to-noise ratio produces higher coherence estimates between task runs.
|Original language||English (US)|
|Title of host publication||55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021|
|Editors||Michael B. Matthews|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|State||Published - 2021|
|Event||55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 - Virtual, Pacific Grove, United States|
Duration: Oct 31 2021 → Nov 3 2021
|Name||Conference Record - Asilomar Conference on Signals, Systems and Computers|
|Conference||55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021|
|City||Virtual, Pacific Grove|
|Period||10/31/21 → 11/3/21|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS Grant support: NIH, Grant numbers: R01HL153146, U01EB025144, P41EB027061, P30NS076408; NSF, Grant number: CAREER CCF-1651825.
© 2021 IEEE.
- accelerated imaging
- deep learning
- functional magnetic resonance imaging
- human connectome project
- retinotopic mapping
- self-supervised learning