# 20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction

arXiv: https://arxiv.org/abs/2105.05827 · HTML: https://mlsanity.com/paper/2105.05827/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 4 of 4)

- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017)
- [A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1704.02422/) (2017)
- [Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/1904.01112/) (2019)

## Built on by (most cited first, 9 of 9)

- [Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning](https://mlsanity.com/paper/2110.00075/) (2021)
- [Robust Simultaneous Multislice MRI Reconstruction Using Slice-Wise Learned Generative Diffusion Priors](https://mlsanity.com/paper/2407.21600/) (2024)
- [Fast MRI for All: Bridging Access Gaps by Training without Raw Data](https://mlsanity.com/paper/2411.13022/) (2024)
- [Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining](https://mlsanity.com/paper/2509.09880/) (2025)
- [Time-Embedded Algorithm Unrolling for Computational MRI](https://mlsanity.com/paper/2510.16321/) (2025)
- [Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI](https://mlsanity.com/paper/2505.00643/) (2025)
- [Deep Dive into MRI: Exploring Deep Learning Applications in 0.55T and 7T MRI](https://mlsanity.com/paper/2407.01318/) (2024)
- [Training-Free Adversarial Robustness in Computational MRI](https://mlsanity.com/paper/2501.01908/) (2025)
- [Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study](https://mlsanity.com/paper/2506.03183/) (2025)

## Abstract (arXiv)

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.