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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Published on: July 28, 2013

Deep learning-based method for reducing residual motion effects in diffusion parameter estimation.

Ting Gong^1,2, Qiqi Tong¹, Zhiwei Li³

¹Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.

Magnetic Resonance in Medicine

|October 15, 2020

Summary

This summary is machine-generated.

A novel deep learning method minimizes motion bias in diffusion MRI scans. This hierarchical convolutional neural network (H-CNN) approach offers stable, reliable diffusion metric estimation, even with significant data loss.

Keywords:

diffusion kurtosis imaging diffusion tensor imaging head motion neural network

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Area of Science:

Neuroimaging
Medical Image Analysis
Machine Learning

Background:

Diffusion MRI is susceptible to motion artifacts, leading to biased quantitative metrics.
Existing motion correction methods may introduce their own biases or be sensitive to data rejection rates.

Purpose of the Study:

To develop and evaluate a deep learning-based technique to minimize residual motion effects in diffusion MRI.
To improve the accuracy and reliability of diffusion MRI metrics, particularly in the presence of head motion.

Main Methods:

A hierarchical convolutional neural network (H-CNN) was developed for parameter estimation.
The H-CNN approach incorporates motion assessment and corrupted volume rejection.
Performance was evaluated against iteratively reweighted linear least squares (IRLLS) with varying data rejection rates.

Main Results:

The H-CNN technique demonstrated minimal sensitivity to motion effects, outperforming IRLLS.
Stable performance was observed across severe data rejection levels (70-90%) and random motion.
The method showed potential to reduce spurious group-level differences in studies of conditions like ADHD.

Conclusions:

The H-CNN-based method effectively reduces residual motion effects in diffusion-weighted imaging.
This technique offers reduced bias in individual scan metrics and improved population-level analyses.
It holds significant potential for enhancing the robustness of diffusion MRI studies.