Learning-based motion artifact removal networks for quantitative R2*mapping

Purpose To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and B0-inhomogeneity-corrected R2* maps from motion-corrupted multi-Gradient-Recalled Echo (mGRE) MRI data. Methods We train two convolutional neural networks (CNNs) to correct motion artifacts for high-quality estimation of quantitative B0-inhomogeneity-corrected R2* maps from mGRE sequences. The first CNN, LEARN-IMG, performs motion correction on complex mGRE images, to enable the subsequent computation of high-quality motion-free quantitative R2* (and any other mGRE-enabled) maps using the standard voxel-wise analysis or machine learning-based analysis. The second CNN, LEARN-BIO, is trained to directly generate motion- and B0-inhomogeneity-corrected quantitative R2* maps from motion-corrupted magnitude-only mGRE images by taking advantage of the biophysical model describing the mGRE signal decay. Results We show that both CNNs trained on synthetic MR images are capable of suppressing motion artifacts while preserving details in the predicted quantitative R2* maps. Significant reduction of motion artifacts on experimental in vivo motion-corrupted data has also been achieved by using our trained models. Conclusion Both LEARN-IMG and LEARN-BIO can enable the computation of high-quality motion- and B0-inhomogeneity-corrected R2* maps. LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of R2* maps, while LEARN-BIO directly performs motion- and B0-inhomogeneity-corrected R2* estimation. Both LEARN-IMG and LEARN-BIO jointly process all the available gradient echoes, which enables them to exploit spatial patterns available in the data. The high computational speed of LEARN-BIO is an advantage that can lead to a broader clinical application.

Automated summary

This study introduces two novel learning-based networks for removing motion artifacts in MRI data and estimating quantitative motion- and B0-inhomogeneity-corrected R2* maps. Experimental results demonstrate the effectiveness of these networks in suppressing motion artifacts and preserving image details. Additionally, these networks exploit spatial patterns in the data and offer high computational speed, making them valuable for clinical applications.