Eddy-Current Suppression with Self-Compensation Technique in Ultra-Low-Field MRI

The ultralow field magnetic resonance imaging (ULF MRI) system operates in a static magnetic field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{B}_0&lt;250 \mu\boldsymbol{\mathrm{T}}$</tex>. As superconducting magnets are no longer required, ULF MRI offers advantages such as improved portability, significantly lower cost, and insensitivity to metallic objects. However, the ULF MRI system faces the challenge of poor signal-to-noise ratio (SNR). The highly sensitive superconducting quantum interference devices (SQUIDs) can detect the weak ULF MRI signal to enhance the SNR. A pre-polarization field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\boldsymbol{B}_{\boldsymbol{p}})$</tex> in the order of tens of mT is used to pre-polarize the sample at the beginning of MRI sequences. However, the rapid switch-off of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{B}_{\boldsymbol{p}}$</tex> current induces eddy currents in the conductively shielded room (CSR) and the surrounding conductors, increasing the MRI system's dead time before signal acquisition. In this case, the gain of the SQUID readout electronics must be decreased by reducing its feedback resistance, which will worsen the detector sensitivity and lower the SNR. Herein, we describe a self-compensation technique through a programmable hardware system with a digital-to-analog converter. The compensation parameters are set based on the complex transient residual field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{B}_{\mathbf{t}\mathbf{r}}$</tex> caused by eddy currents. With this technique, the transient voltage outputs Vtr of the SQUID electronics caused by the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{B}_{\mathbf{t}\mathbf{r}}$</tex> are well suppressed so that larger feedback resistance becomes feasible, and the SNR of the ULF-MRI image is improved. The experimental results reveal that this technique would reduce the maximal output range of eddy current by approximately 79.2% and improve the SNR of ULF MRI images by a factor of 2. We employed a convolutional neural network U-net to reduce noise and improve SNR further. With its potential to significantly enhance SNR in ULF MRI, this compensation technique will be of great importance in human imaging, marking a significant advancement in the field.