Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy

• Published in: Magma (New York, N.Y.) Vol. 37; no. 3; pp. 477 - 489
• Main Authors: Huang, Yu-Long, Lin, Yi-Ru, Tsai, Shang-Yueh
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.07.2024
• Subjects: Adult; Algorithms; Aspartic Acid - analogs & derivatives; Aspartic Acid - metabolism; Basic Science - MR spectroscopy methods; Biomedical Engineering and Bioengineering; Brain - diagnostic imaging; Brain - metabolism; Computer Appl. in Life Sciences; Deep Learning; Female; Health Informatics; Humans; Imaging; Linear Models; Magnetic Resonance Spectroscopy - methods; Male; Medicine; Medicine & Public Health; Neural Networks, Computer; Radiology; Research Article; Signal-To-Noise Ratio; Solid State Physics; Young Adult; Magnetic resonance spectroscopy; Metabolite concentrations; Convolutional neural network
• ISSN: 1352-8661; 1352-8661
• DOI: 10.1007/s10334-023-01120-z

Background Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful. Materials and methods This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system. Results The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson’s correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and  absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content. Conclusion In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.