Automated High-Order Shimming for Neuroimaging Studies

Abstract

B0 inhomogeneity presents a significant challenge in MRI and MR spectroscopy, particularly at high-field strengths, leading to image distortion, signal loss, and spectral broadening. Existing high-order shimming methods can alleviate these issues but often require time-consuming and subjective manual selection of regions of interest (ROIs). To address this, we proposed an automated high-order shimming (autoHOS) method, incorporating deep-learning-based brain extraction and image-based high-order shimming. This approach performs automated real-time brain extraction to define the ROI of the field map to be used in the shimming algorithm. The shimming performance of autoHOS was assessed through in vivo echo-planar imaging (EPI) and spectroscopic studies at both 3T and 7T field strengths. AutoHOS outperforms linear shimming and manual high-order shimming, enhancing both the image and spectral quality by reducing the EPI image distortion and narrowing the MRS spectral lineshapes. Therefore, autoHOS demonstrated a significant improvement in correcting B0 inhomogeneity while eliminating the need for additional user interaction.