Chinese Journal of Magnetic Resonance >

2022 , Vol. 39 >Issue 2: 220 - 229

DOI: https://doi.org/10.11938/cjmr20202870

Review Articles & Perspectives

Evaluation of the Influence of Data Sampling Schemes on Neural Diffusion Models

  • Min-xiong ZHOU ,
  • Hui-ting ZHANG ,
  • Yi-da WANG ,
  • Guang YANG ,
  • Xu-feng YAO ,
  • An-kang GAO ,
  • Jing-liang CHENG ,
  • Jie BAI ,
  • Xu YAN
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  • 1. College of Medical Imaging & Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China
    2. MR Scientific Marketing, Siemens Healthcare, Shanghai 201318, China
    3. Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai 200062, China
    4. Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

Received date: 2020-11-05

  Online published: 2022-05-28

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Abstract

The joint application of multiple diffusion models on single sampled dataset is becoming a hot topic in clinical research. This study investigated the influence of the three data sampling schemes on the quantification of neural diffusion models. The three sampling schemes compared were QGrid, Free and MDDW on the Siemens scanners. The diffusion models involved were diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP) models. It was demonstrated that the results of NODDI and MAP were sensitive to the sampling schemes and the set of maximum b-value, while that of DTI and DKI were comparatively not sensitive to varying configurations. It was also shown that QGrid and Free schemes provided more consistent results. Thus the sampling scheme should be carefully selected in multi-center studies and studies with large sample size. QGrid and Free schemes are recommended for their advantages demonstrated in this study.

Key words： magnetic resonance imaging; data sampling scheme; diffusion model; diffusion tensor imaging (DTI); diffusion kurtosis imaging (DKI); neurite orientation dispersion and density imaging (NODDI); mean apparent propagator (MAP)

Cite this article

Min-xiong ZHOU , Hui-ting ZHANG , Yi-da WANG , Guang YANG , Xu-feng YAO , An-kang GAO , Jing-liang CHENG , Jie BAI , Xu YAN . Evaluation of the Influence of Data Sampling Schemes on Neural Diffusion Models[J]. Chinese Journal of Magnetic Resonance, 2022 , 39(2) : 220 -229 . DOI: 10.11938/cjmr20202870

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