Investigative Magnetic Resonance Imaging

  • 제24권4호
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  • Pages.207-213
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  • 2020
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  • 2384-1095(pISSN)
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  • 2384-1109(eISSN)
대한자기공명의과학회 (Korean Society of Magnetic Resonance in Medicine)
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Exploring Generalization Capacity of Artificial Neural Network for Myelin Water Imaging

  • Lee, Jieun (Department of Electrical and Computer Engineering, Seoul National University) ;
  • Choi, Joon Yul (Department of Electrical and Computer Engineering, Seoul National University) ;
  • Shin, Dongmyung (Department of Electrical and Computer Engineering, Seoul National University) ;
  • Kim, Eung Yeop (Department of Radiology, Gil Medical Center, Gachon University College of Medicine) ;
  • Oh, Se-Hong (Division of Biomedical Engineering, Hankuk University of Foreign Studies) ;
  • Lee, Jongho (Department of Electrical and Computer Engineering, Seoul National University)
  • 투고 : 2020.04.10
  • 심사 : 2020.06.12
  • 발행 : 2020.12.31
⟨ 이전 논문 다음 논문 ⟩

초록

Purpose: To understand the effects of datasets with various parameters on pre-trained network performance, the generalization capacity of the artificial neural network for myelin water imaging (ANN-MWI) is explored by testing datasets with various scan protocols (i.e., resolution and refocusing RF pulse shape) and types of disorders (i.e., neuromyelitis optica and edema). Materials and Methods: ANN-MWI was trained to generate a T2 distribution, from which the myelin water fraction value was measured. The training and test datasets were acquired from healthy controls and multiple sclerosis patients using a multi-echo gradient and spin-echo sequence with the same scan protocols. To test the generalization capacity of ANN-MWI, datasets with different settings were utilized. The datasets were acquired or generated with different resolutions, refocusing pulse shape, and types of disorders. For all datasets, the evaluation was performed in a white matter mask by calculating the normalized root-mean-squared error (NRMSE) between the results from the conventional method and ANN-MWI. Additionally, for the patient datasets, the NRMSE was calculated in each lesion mask. Results: The results of ANN-MWI showed high reliability in generating myelin water fraction maps from the datasets with different resolutions. However, the increased errors were reported for the datasets with different refocusing pulse shapes and disorder types. Specifically, the region of lesions in edema patients reported high NRMSEs. These increased errors indicate the dependency of ANN-MWI on refocusing pulse flip angles and T2 characteristics. Conclusion: This study proposes information about the generalization accuracy of a trained network when applying deep learning to processing myelin water imaging.

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참고문헌

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