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|
|State||Published - 2023|
|Event||11th International IEEE/EMBS Conference on Neural Engineering, NER 2023 - Baltimore, United States|
Duration: Apr 25 2023 → Apr 27 2023
|Name||International IEEE/EMBS Conference on Neural Engineering, NER|
|Conference||11th International IEEE/EMBS Conference on Neural Engineering, NER 2023|
|Period||4/25/23 → 4/27/23|
Bibliographical noteFunding Information:
This work was partially supported by NIH: P41 EB027061, R01 HL153146, U01 EB025144, P30 NS076408, RF1 MH116978; NSF: CAREER CCF-1651825.
© 2023 IEEE.
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