Novel Models for High-Dimensional Imaging: High-Resolution fMRI Acceleration and Quantification

TL;DR

This paper introduces innovative models and pipelines for high-resolution fMRI that enhance spatial and temporal resolution while maintaining high SNR, outperforming existing methods in quality and information content.

Contribution

The paper presents new acquisition and reconstruction models, including a patch-tensor low-rank model, a physics-based manifold model, and a voxel-wise attention network, for improved high-resolution fMRI.

Findings

Enhanced SNR and resolution without increasing scan time

Proposed models outperform existing approaches in resolution and functional information

Demonstrated superior reconstruction quality in high-resolution fMRI

Abstract

The goals of functional Magnetic Resonance Imaging (fMRI) include high spatial and temporal resolutions with a high signal-to-noise ratio (SNR). To simultaneously improve spatial and temporal resolutions and maintain the high SNR advantage of OSSI, we present novel pipelines for fast acquisition and high-resolution fMRI reconstruction and physics parameter quantification. We propose a patch-tensor low-rank model, a physics-based manifold model, and a voxel-wise attention network. With novel models for acquisition and reconstruction, we demonstrate that we can improve SNR and resolution simultaneously without compromising scan time. All the proposed models outperform other comparison approaches with higher resolution and more functional information.