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基于卷积神经网络的方法和LCModel对in vivo磁共振光谱的量化进行比较

Yu-Long Huang¹, Yi-Ru Lin¹, Shang-Yueh Tsai^2,3

¹Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.

Magma (New York, N.Y.)

|September 15, 2023

概括

使用卷积神经网络 (CNN) 的深度学习方法有效地从磁共振光谱 (MRS) 数据中量化机构单位 (IU) 中的代谢物度. 标准误差 (SE) 作为可靠的误差指数,可与绝对克拉默-拉奥下限 (CRLB) 相比.

科学领域:

神经成像是一种神经成像.
生物化学生物化学
计算生物学计算生物学

背景情况:

在磁共振光谱学 (MRS) 中精确的代谢物量化对于跨个体和随时间的可靠比较至关重要.
深度学习 (DL) 算法越来越多地应用于MRS数据处理,需要兼容的量化策略.

研究的目的:

评估基于卷积神经网络 (CNN) 的方法,用于量化机构单位 (IU) 中的代谢物度.
评估CNN方法是否具有光谱正常化和线性回归,准确地反映了不同信号噪声比率 (SNR) 和线宽 (LW) 的大脑区域中的代谢物变化.
引入基于标准误差 (SE) 的误差指数,用于代谢物量化信心.

主要方法:

在体内MRS光谱采集了43名受试者在三个大脑区域使用3T系统.
基于CNN的方法,包括光谱信号规范化和线性回归,被开发用于代谢物量化.
标准误差 (SE) 被计算为代谢物预测的误差指数.

主要成果:

通过CNN量化的代谢物度 (IU) 显示了与LCModel可比的范围,皮尔森的相关系数从0.24到0.78.7之间.
代谢物的SE与克拉默-拉奥下限 (CRLB) (r=0.46) 和绝对CRLB (r=0.81) 正相关.

结论:

基于CNN的方法,与拟议的缩放,有效量化体内MRS光谱和代谢物度在IU.
SE作为一个有价值的错误指数,提供了对预测代谢物不确定性的洞察,类似于绝对CRLB.

关键词:

卷积神经网络是一种卷积神经网络.磁共振光谱学磁共振光谱学代谢物的度代谢物的度

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