Acquisition Acceleration of Ultra-Low Field MRI With Parallel Imaging and Compressed Sensing in Microtesla Fields.

Abstract

In recent years, ultra-low field (ULF) magnetic resonance imaging (MRI) has gained widespread attention due to its advantages, such as low cost, light weight, and portability. However, the low signal-to-noise ratio (SNR) leads to a long scan time. Herein, we study the acceleration performance of parallel imaging (PI) and compressed sensing (CS) in different k-space sampling strategies at 0.12 mT. This study employs phantoms to assess the efficiency of acceleration methods at ULF MRI, in which signals are detected by ultra-sensitive superconducting quantum interference devices (SQUIDs). We compare the performance of fast Fourier transform (FFT), generalized auto-calibrating partially parallel acquisitions (GRAPPA), and eigenvector-based SPIRiT (ESPIRiT) in Cartesian sampling, while also evaluating non-uniform FFT (NUFFT), GRAPPA operator gridding, and ESPIRiT in non-Cartesian sampling. We design a resolution phantom to investigate the effectiveness of these methods in maintaining image resolution. In Cartesian sampling, GRAPPA and ESPIRiT jointly regularized by total variation and ℓ¹-norm (TVJℓ¹-ESPIRiT) methods reconstructed good-quality phantom images with an acceleration factor of R = 2. In contrast, TVJℓ¹-ESPIRiT exhibited improved image quality and much less signal loss even for R = 4. In radial sampling, TVJℓ¹-ESPIRiT reduced the acquisition time to 1.69 minutes at R = 4, with a respective improvement of 12.26 dB in peak SNR compared to NUFFT. The resolution phantom imaging showed that the reconstructions by PI and CS maintained the original resolution of 2 mm. This study improves the practicality of ULF MRI at microtesla fields by implementing imaging acceleration with PI and CS in different k-space sampling.