20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction
| Autor: | Omer Burak Demirel, Burhaneddin Yaman, Logan Dowdle, Steen Moeller, Luca Vizioli, Essa Yacoub, John Strupp, Cheryl A. Olman, Kamil Ugurbil, Mehmet Akcakaya |
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| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) 0206 medical engineering Computer Science - Computer Vision and Pattern Recognition FOS: Physical sciences Brain 02 engineering and technology Electrical Engineering and Systems Science - Image and Video Processing Physics - Medical Physics Magnetic Resonance Imaging 020601 biomedical engineering Article Machine Learning (cs.LG) Deep Learning FOS: Electrical engineering electronic engineering information engineering Connectome Image Processing Computer-Assisted Humans Medical Physics (physics.med-ph) Electrical Engineering and Systems Science - Signal Processing |
| Zdroj: | Annu Int Conf IEEE Eng Med Biol Soc |
| DOI: | 10.1109/embc46164.2021.9631107 |
| Popis: | High spatial and temporal resolution across the whole brain is essential to accurately resolve neural activities in fMRI. Therefore, accelerated imaging techniques target improved coverage with high spatio-temporal resolution. Simultaneous multi-slice (SMS) imaging combined with in-plane acceleration are used in large studies that involve ultrahigh field fMRI, such as the Human Connectome Project. However, for even higher acceleration rates, these methods cannot be reliably utilized due to aliasing and noise artifacts. Deep learning (DL) reconstruction techniques have recently gained substantial interest for improving highly-accelerated MRI. Supervised learning of DL reconstructions generally requires fully-sampled training datasets, which is not available for high-resolution fMRI studies. To tackle this challenge, self-supervised learning has been proposed for training of DL reconstruction with only undersampled datasets, showing similar performance to supervised learning. In this study, we utilize a self-supervised physics-guided DL reconstruction on a 5-fold SMS and 4-fold in-plane accelerated 7T fMRI data. Our results show that our self-supervised DL reconstruction produce high-quality images at this 20-fold acceleration, substantially improving on existing methods, while showing similar functional precision and temporal effects in the subsequent analysis compared to a standard 10-fold accelerated acquisition. |
| Databáze: | OpenAIRE |
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