SQUID-sensor-based ultra-low-field MRI calibration with phantom images: towards quantitative imaging

Area of Science:

• Medical Imaging
• Biophysics
• Sensor Technology

Background:

• Ultra-low-field magnetic resonance imaging (ULF MRI) operates at significantly lower Larmor frequencies (~1 kHz) than high-field MRI (>100 MHz).
• Traditional induction coils are unsuitable for ULF MRI's weak signals; superconducting quantum interference device (SQUID) sensors are required for femtotesla-level detection.
• Signal-to-noise ratio in ULF MRI is improved through prepolarization in a much stronger magnetic field.

Purpose of the Study:

• To present a novel procedure for calibrating SQUID-sensor-based ULF MRI systems using MR images.
• To demonstrate the feasibility of this calibration method through both experimental verification and simulations.
• To explore the potential application of this calibration in integrated magnetoencephalography (MEG)-MRI systems.

Main Methods:

• Development and validation of a calibration procedure for SQUID sensors in ULF MRI using an imaging phantom.
• Integration of theoretical frameworks with experimental measurements for system calibration.
• Comparison of the proposed ULF MRI-based calibration with conventional electromagnet probe methods used in MEG.

Main Results:

• Successful calibration of a SQUID-sensor-based ULF MRI system was achieved using MR imaging data.
• The presented method provides experimental verification of the theoretical calibration framework.
• The ULF MRI calibration procedure shows promise for replacing conventional MEG calibration techniques.

Conclusions:

• A robust calibration procedure for SQUID-based ULF MRI systems has been established.
• This method facilitates quantitative ULF MRI without the need for sample-specific reference scans.
• The developed calibration technique is applicable to both standalone ULF MRI and hybrid MEG-MRI systems.