Deep learning‐based method for reducing residual motion effects in diffusion parameter estimation
Ting Gong, Qiqi Tong, Zhiwei Li, Hongjian He, Hui Zhang, Jianhui Zhong
Abstract
Purpose
Conventional motion‐correction techniques for diffusion MRI can introduce motion‐level–dependent bias in derived metrics. To address this challenge, a deep learning‐based technique was developed to minimize such residual motion effects.
Methods
The data‐rejection approach was adopted in which motion‐corrupted data are discarded before model‐fitting. A deep learning‐based parameter estimation algorithm, using a hierarchical convolutional neural network (H‐CNN), was combined with motion assessment and corrupted volume rejection. The method was designed to overcome the limitations of existing methods of this kind that produce parameter estimations whose quality depends strongly on a proportion of the data discarded. Evaluation experiments were conducted for the estimation of diffusion kurtosis and diffusion‐tensor–derived measures at both the individual and group levels. The performance was compared with the robust approach of iteratively reweighted linear least squares (IRLLS) after motion correction with and without outlier replacement.
Results
Compared with IRLLS, the H‐CNN‐based technique is minimally sensitive to motion effects. It was tested at severe motion levels when 70% to 90% of the data are rejected and when random motion is present. The technique had a stable performance independent of the numbers and schemes of data rejection. A further test on a data set from children with attention‐deficit hyperactivity disorder shows the technique can potentially ameliorate spurious group‐level difference caused by head motion.
Conclusion
This method shows great potential for reducing residual motion effects in motion‐corrupted diffusion‐weighted–imaging data, bringing benefits that include reduced bias in derived metrics in individual scans and reduced motion‐level–dependent bias in population studies employing diffusion MRI.