Abstract
Submillimeter fMRI plays a vital role in studying the brain function at the mesoscale level, allowing investigation of functional activity in small cortical structures. However, such resolutions require extreme trade-offs between SNR, spatio-temporal resolution and coverage leading to numerous challenges. Therefore, interpretable locally low-rank denoising methods based on random matrix theory have been proposed and built into fMRI pipelines, but they require well-characterized noise distributions on reconstructed images, which hinders the use of emerging physics-driven deep learning reconstructions. In this work, we re-envision the conventional fMRI computational imaging pipeline to an alternative where denoising is performed prior to reconstruction. This allows for a synergistic combination of random matrix theory based thermal noise suppression and physics-driven deep learning re-construction, enabling high-quality 0.5mm isotropic functional MRI. Our results show that the proposed strategy improves on denoising or physics-driven deep learning reconstruction alone, with better delineation of brain structures, higher tSNR particularly in mid-brain areas and the largest expected extent of activation in GLM-derived t-maps.
Original language | English (US) |
---|---|
Title of host publication | 11th International IEEE/EMBS Conference on Neural Engineering, NER 2023 - Proceedings |
Publisher | IEEE Computer Society |
ISBN (Electronic) | 9781665462921 |
DOIs | |
State | Published - 2023 |
Event | 11th International IEEE/EMBS Conference on Neural Engineering, NER 2023 - Baltimore, United States Duration: Apr 25 2023 → Apr 27 2023 |
Publication series
Name | International IEEE/EMBS Conference on Neural Engineering, NER |
---|---|
Volume | 2023-April |
ISSN (Print) | 1948-3546 |
ISSN (Electronic) | 1948-3554 |
Conference
Conference | 11th International IEEE/EMBS Conference on Neural Engineering, NER 2023 |
---|---|
Country/Territory | United States |
City | Baltimore |
Period | 4/25/23 → 4/27/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.