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Related Experiment Video

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A deep learning-based method for improving reliability of multicenter diffusion kurtosis imaging with varied

Qiqi Tong1, Ting Gong2, Hongjian He2

  • 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, Zhejiang, China; Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China.

Magnetic Resonance Imaging
|August 22, 2020
PubMed
Summary

A new deep learning framework harmonizes diffusion kurtosis imaging (DKI) data across multiple MRI scanners. This method reduces data variability, enhancing reliability for large-scale research projects using advanced imaging techniques.

Keywords:
Deep learningDiffusion kurtosis imagingDiffusion magnetic resonance imagingMulticenter harmonization

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Artificial Intelligence

Background:

  • Multicenter magnetic resonance imaging (MRI) studies face challenges due to data heterogeneity from varying hardware and software.
  • Ensuring reliable model parameter derivation from diverse quality image data is crucial, especially for advanced diffusion MRI techniques like diffusion kurtosis imaging (DKI).
  • Deep learning (DL) methods show promise for robust computation of diffusion-derived measures.

Purpose of the Study:

  • To develop and validate a DL-based framework for joint reconstruction and harmonization of multicenter DKI measures.
  • To reformulate DKI data from various acquisition protocols to a standardized state-of-the-art hardware level.
  • To assess the effectiveness of the proposed method in reducing inter-scanner variability and enhancing data reliability.

Main Methods:

  • A 3D hierarchical convolutional neural network framework was designed for DKI data harmonization.
  • The framework jointly reconstructs and harmonizes DKI measures from multicenter acquisitions.
  • Data from traveling subjects across different scanners and protocols were used for training and validation.

Main Results:

  • Significant reductions in inter-scanner variation for DKI measures: mean kurtosis (51.5%), axial kurtosis (65.9%), radial kurtosis (53.7%), and kurtosis fractional anisotropy (61.5%).
  • Enhanced data reliability for individual scanners, achieving the level of a reference scanner.
  • The harmonization network successfully reconstructed reliable DKI values despite high data variability.

Conclusions:

  • The proposed DL-based method is feasible for harmonizing DKI data in multicenter settings.
  • This approach simplifies protocol setup for scanners with differing hardware and software configurations.
  • The study demonstrates improved data consistency and reliability for large-scale neuroimaging research using DKI.