An Exploration of Parallel Imaging System for Very-low Field (50mT) MRI Scanner

TL;DR

This paper introduces a novel parallel imaging technique specifically designed for very-low field MRI scanners, significantly reducing scan time while maintaining image quality, and demonstrating superior performance over existing methods.

Contribution

The first application of parallel imaging in VLF MRI, including a VLF-specific algorithm, phased-array coil design, and denoising methods, to improve scan efficiency and image quality.

Findings

Achieved 72.5% SNR improvement over fully sampled images.

Demonstrated lower MSE, higher PSNR and SSIM compared to existing PI methods.

Reduced scan time to less than half of traditional methods.

Abstract

Reducing the scanning time of very-low field (VLF) magnetic resonance imaging (MRI) scanners, commonly employed for stroke diagnosis, can enhance patient comfort and operational efficiency. The conventional parallel imaging (PI) technique for high-field MRI should be tailored to apply here, considering the differences in the direction of the main magnetic field and the presence of noise. A VLF-specific PI algorithm and phased-array coil are proposed, marking the first application of PI in VLF MRI. Reconstruction quality is enhanced by denoising undersampled k-space data using a linear-prediction based Kalman filter. Subsequently, the denoised k-space data are nonlinearly mapped from the original space onto a high-dimensional feature space, utilizing a polynomial feature mapping defined nonlinear frame. Frame parameters are calculated using auto-calibration signals (ACS) from the center k-space, and missing phase-encoding lines in the original space are estimated using acquired lines in the feature space. An 8-channel phased-array coil, designed for a vertical main magnetic field, is decoupled using geometric overlap and a low input impedance (LII) preamplifier. Healthy volunteer head imaging experiments using the proposed PI technique exhibit the lowest mean-squared-error (MSE) value and the highest peak-signal-to-noise (PSNR) and structural similarity index (SSIM) values compared to two widely used PI methods. The proposed PI technique enables the VLF MRI scanner to achieve similar image quality and a 72.5% improvement in signal-to-noise ratio (SNR) compared to fully sampled images while requiring less than 50% of the scan time. We present a PI technique tailored for VLF MRI scanner for the first time, along with potential research direction to achieve greater reduction factor.