ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2021
20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 18 citations in OpenAlex.
- CONSISTENCY MODELS FOR FAST MRI USING REGULARIZATION BY DENOISING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining.ArXiv · 2025Article
- Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI.ArXiv · 2025Article
- SNAKE: A modular realistic fMRI data simulator from the space-time domain to k-space and back.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Time-Embedded Algorithm Unrolling for Computational MRI.Advances in neural information processing systems · 2025Article
- Real-time fMRI using multi-band echo-volumar imaging with millimeter spatial resolution and sub-second temporal resolution at 3 tesla.Frontiers in neuroscience · 2025Article
- CYCLE-CONSISTENT SELF-SUPERVISED LEARNING FOR IMPROVED HIGHLY-ACCELERATED MRI RECONSTRUCTION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Exploring methodological frontiers in laminar fMRI.Psychoradiology · 2024Review
- High-fidelity Database-free Deep Learning Reconstruction for Real-time Cine Cardiac MRI.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR.Magnetic resonance in medicine · 2023Article
- Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging.NMR in biomedicine · 2022Article
- Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement: An overview from a signal processing perspective.IEEE signal processing magazine · 2022Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.