Deep learning improved autofocus for motion artifact reduction and its application in quantitative susceptibility mapping

TL;DR

This paper introduces a deep learning-enhanced autofocus pipeline that significantly reduces motion artifacts in MRI imaging, improving image quality in both simulated and real patient data, especially for Parkinson's disease applications.

Contribution

The study presents a novel deep learning strategy integrated with autofocus to effectively remove residual motion artifacts in quantitative susceptibility mapping.

Findings

Significant improvement in image quality metrics (SSIM, PSNR, RMSE) in simulated data.

Enhanced image quality scores in real patient QSM images with motion artifacts.

Deep learning-based correction outperforms traditional autofocus methods.

Abstract

Purpose: To develop a pipeline for motion artifact correction in mGRE and quantitative susceptibility mapping (QSM). Methods: Deep learning is integrated with autofocus to improve motion artifact suppression, which is applied QSM of patients with Parkinson's disease (PD). The estimation of affine motion parameters in the autofocus method depends on signal-to-noise ratio and lacks accuracy when data sampling occurs outside the k-space center. A deep learning strategy is employed to remove the residual motion artifacts in autofocus. Results: Results obtained in simulated brain data (n =15) with reference truth show that the proposed autofocus deep learning method significantly improves the image quality of mGRE and QSM (p = 0.001 for SSIM, p < 0.0001 for PSNR and RMSE). Results from 10 PD patients with real motion artifacts in QSM have also been corrected using the proposed method and sent to an experienced radiologist for image quality evaluation, and the average image quality score has increased (p=0.0039). Conclusions: The proposed method enables substantial suppression of motion artifacts in mGRE and QSM.