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

TL;DR

This paper presents a self-supervised deep learning reconstruction method that achieves 20-fold acceleration in 7T fMRI imaging, significantly enhancing image quality and functional analysis accuracy compared to existing techniques.

Contribution

The study introduces a novel self-supervised physics-guided deep learning approach for ultra-high acceleration in 7T fMRI, eliminating the need for fully-sampled training data.

Findings

Achieves 20-fold acceleration with high-quality image reconstruction

Maintains functional precision comparable to lower acceleration methods

Outperforms existing reconstruction techniques in high-acceleration scenarios

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.