Frequency and phase correction of GABA-edited magnetic resonance spectroscopy using complex-valued convolutional neural networks

IF 2.1 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Magnetic resonance imaging Pub Date : 2024-05-12 DOI:10.1016/j.mri.2024.05.008
Hanna Bugler , Rodrigo Berto , Roberto Souza , Ashley D. Harris
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Abstract

Purpose

To determine the significance of complex-valued inputs and complex-valued convolutions compared to real-valued inputs and real-valued convolutions in convolutional neural networks (CNNs) for frequency and phase correction (FPC) of GABA-edited magnetic resonance spectroscopy (MRS) data.

Methods

An ablation study using simulated data was performed to determine the most effective input (real or complex) and convolution type (real or complex) to predict frequency and phase shifts in GABA-edited MEGA-PRESS data using CNNs. The best CNN model was subsequently compared using both simulated and in vivo data to two recently proposed deep learning (DL) methods for FPC of GABA-edited MRS. All methods were trained using the same experimental setup and evaluated using the signal-to-noise ratio (SNR) and linewidth of the GABA peak, choline artifact, and by visually assessing the reconstructed final difference spectrum. Statistical significance was assessed using the Wilcoxon signed rank test.

Results

The ablation study showed that using complex values for the input represented by real and imaginary channels in our model input tensor, with complex convolutions was most effective for FPC. Overall, in the comparative study using simulated data, our CC-CNN model (that received complex-valued inputs with complex convolutions) outperformed the other models as evaluated by the mean absolute error.

Conclusion

Our results indicate that the optimal CNN configuration for GABA-edited MRS FPC uses a complex-valued input and complex convolutions. Overall, this model outperformed existing DL models.

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利用复值卷积神经网络对 GABA 编辑的磁共振光谱进行频率和相位校正。
目的:确定卷积神经网络(CNN)中的复值输入和复值卷积与实值输入和实值卷积相比,对 GABA 编辑的磁共振波谱(MRS)数据进行频率和相位校正(FPC)的意义:使用模拟数据进行消融研究,以确定使用 CNN 预测 GABA 编辑的 MEGA-PRESS 数据中频率和相位偏移的最有效输入(实数或复数)和卷积类型(实数或复数)。随后,使用模拟数据和体内数据将最佳 CNN 模型与最近提出的两种用于 GABA 编辑 MRS FPC 的深度学习(DL)方法进行了比较。所有方法均使用相同的实验装置进行训练,并通过 GABA 峰的信噪比(SNR)和线宽、胆碱伪影以及目视评估重建的最终差谱进行评估。统计意义和效应大小采用 Wilcoxon 符号秩检验进行评估:消融研究表明,在我们的输入张量模型中,使用由实数和虚数通道代表的输入复数值以及复数卷积对 FPC 最有效。总体而言,在使用模拟数据进行的比较研究中,根据平均绝对误差评估,我们的 CC-CNN 模型(接收复杂卷积的复值输入)优于其他模型:我们的研究结果表明,GABA 编辑 MRS FPC 的最佳 CNN 配置是使用复值输入和复杂卷积。总体而言,该模型的表现优于现有的 DL 模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Magnetic resonance imaging
Magnetic resonance imaging 医学-核医学
CiteScore
4.70
自引率
4.00%
发文量
194
审稿时长
83 days
期刊介绍: Magnetic Resonance Imaging (MRI) is the first international multidisciplinary journal encompassing physical, life, and clinical science investigations as they relate to the development and use of magnetic resonance imaging. MRI is dedicated to both basic research, technological innovation and applications, providing a single forum for communication among radiologists, physicists, chemists, biochemists, biologists, engineers, internists, pathologists, physiologists, computer scientists, and mathematicians.
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