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Abstract #2847

Deep learning-based target metabolite-specific signal isolation and big data-driven measurement uncertainty estimation in 1H-MRS of the brain

HyeongHun Lee1 and Hyeonjin Kim1,2
1Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea, 2Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea

We developed convolutional-neural-networks(CNNs) for each individual metabolites capable of spectrally isolating target metabolite signal for quantification on simulated rat brain spectra at 9.4T. Although heuristically and empirically developed, a method of predicting measurement uncertainty is also proposed by exploiting the spectral isolation capability of the CNNs and the availability of big data. The quantitative accuracy of the proposed method was higher than that of the LC model. The measurement uncertainty predicted by the proposed method was highly correlated with the ground-truth error. The proposed method may be used for metabolite quantification with measurement uncertainty estimation in rat brain at 9.4T.

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