Novel Models for High-Dimensional Imaging: High-Resolution fMRI Acceleration and Quantification
This work addresses a domain-specific problem in medical imaging for researchers and clinicians, offering incremental improvements in fMRI acquisition and reconstruction.
The paper tackles the challenge of achieving high spatial and temporal resolution in fMRI while maintaining signal-to-noise ratio, proposing novel pipelines that improve SNR and resolution without increasing scan time, with all models outperforming comparison approaches.
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.