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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2020.8
- 수록면
- 1,668 - 1,672 (5page)
이용수
초록· 키워드
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Proton magnetic resonance spectroscopy (1H-MRS) is a noninvasive tool extracting biochemical information from brain. Quantification of metabolites from 1H‐MRS provides brain metabolic status. However, quantification of those concentrations remains a challenging task. Deep learning has been used successfully in magnetic resonance research field for classification, object recognition, noise removal and prediction. To apply deep learning into 1H-MRS quantification, we designed convolutional auto encoders (CAEs) that extract noise‐free, line-narrowed, baseline‐removed metabolite spectra for each degraded spectrum. We simulated data with combination of metabolite on phantom spectra. Also, the simulated data were varied with a wide range of metabolite concentration, SNR, linewidth, phase shift and frequency shift. We used ratios of metabolite concentration divided by total Creatine (tCr) concentration by our proposed method, LCModel and jMRUI. The tCr ratios of fourteen metabolites were within the reported concentration ranges by the proposed method. Our results were similar to LCModel, superior to jMRUI. We believed that deep learning on 1H-MRS has potential applicability not only for humans but al-so for mice spectra.
목차
Abstract
Ⅰ. Introduction
Ⅱ. Materials & Methods
Ⅳ. Results & Discussions
References
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UCI(KEPA) : I410-ECN-0101-2020-569-001133208