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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.9
- 수록면
- 356 - 362 (7page)
- DOI
- 10.4283/JMAG.2021.26.3.356
이용수
초록· 키워드
오류제보하기
The present study aims to develop a deep learning (DL) model to quantify metabolites. To apply DL to metabolite quantification using ¹H-MRS data, Convolutional autoencoder (CAE) were designed to extract line‐narrowed, baseline‐removed, and noise-free metabolite spectra for each metabolite. Fifty thousand simulation data were generated by varying the SNR (4-12), linewidth (6-22 Hz), phase shift (± 5°), and frequency shift (± 5 Hz) on phantom spectra. The data were divided into 45,000 simulation data for training and 5,000 test data, and the mean absolute percent errors (MAPEs) were used to evaluate the performance of the CAE. The average MAPE of the metabolites was 13.64 ± 11.38 %. Fourteen metabolites were within the reported concentration ranges. These findings showed that the proposed method had similar or improved performance than conventional methods. The proposed method using DL was the recent and up-to-date quantification one and has clinically potential applicability.
목차
1. Introduction
2. Materials and Methods
3. Results
4. Discussions
5. Conclusions
References
참고문헌 (23)
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