Synthesis of higher-B0 CEST Z-spectra from lower-B0 data via deep learning and singular value decomposition.

IF 2.7 4区 医学 Q2 BIOPHYSICS NMR in Biomedicine Pub Date : 2024-08-07 DOI:10.1002/nbm.5221
Mengdi Yan, Chongxue Bie, Wentao Jia, Chuyu Liu, Xiaowei He, Xiaolei Song
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Abstract

Chemical exchange saturation transfer (CEST) MRI at 3 T suffers from low specificity due to overlapping CEST effects from multiple metabolites, while higher field strengths (B0) allow for better separation of Z-spectral "peaks," aiding signal interpretation and quantification. However, data acquisition at higher B0 is restricted by equipment access, field inhomogeneity and safety issues. Herein, we aim to synthesize higher-B0 Z-spectra from readily available data acquired with 3 T clinical scanners using a deep learning framework. Trained with simulation data using models based on Bloch-McConnell equations, this framework comprised two deep neural networks (DNNs) and a singular value decomposition (SVD) module. The first DNN identified B0 shifts in Z-spectra and aligned them to correct frequencies. After B0 correction, the lower-B0 Z-spectra were streamlined to the second DNN, casting into the key feature representations of higher-B0 Z-spectra, obtained through SVD truncation. Finally, the complete higher-B0 Z-spectra were recovered from inverse SVD, given the low-rank property of Z-spectra. This study constructed and validated two models, a phosphocreatine (PCr) model and a pseudo-in-vivo one. Each experimental dataset, including PCr phantoms, egg white phantoms, and in vivo rat brains, was sequentially acquired on a 3 T human and a 9.4 T animal scanner. Results demonstrated that the synthetic 9.4 T Z-spectra were almost identical to the experimental ground truth, showing low RMSE (0.11% ± 0.0013% for seven PCr tubes, 1.8% ± 0.2% for three egg white tubes, and 0.79% ± 0.54% for three rat slices) and high R2 (>0.99). The synthesized amide and NOE contrast maps, calculated using the Lorentzian difference, were also well matched with the experiments. Additionally, the synthesis model exhibited robustness to B0 inhomogeneities, noise, and other acquisition imperfections. In conclusion, the proposed framework enables synthesis of higher-B0 Z-spectra from lower-B0 ones, which may facilitate CEST MRI quantification and applications.

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通过深度学习和奇异值分解,从低 B0 数据合成高 B0 CEST Z 光谱。
3 T 下的化学交换饱和转移(CEST)磁共振成像因多种代谢物的 CEST 效应重叠而导致特异性较低,而较高的场强(B0)可更好地分离 Z 光谱 "峰值",有助于信号解读和量化。然而,在较高的 B0 下获取数据受到设备接入、场不均匀性和安全问题的限制。在此,我们旨在利用深度学习框架,从 3 T 临床扫描仪获取的现成数据中合成更高 B0 的 Z 光谱。该框架由两个深度神经网络(DNN)和一个奇异值分解(SVD)模块组成,使用基于布洛赫-麦康奈尔方程的模型对模拟数据进行训练。第一个 DNN 识别 Z 频谱中的 B0 移位,并将其与正确频率对齐。经过 B0 校正后,低 B0 Z 频谱被精简到第二个 DNN,并通过 SVD 截断获得高 B0 Z 频谱的关键特征表示。最后,鉴于 Z 频谱的低秩属性,通过反 SVD 恢复了完整的高 B0 Z 频谱。本研究构建并验证了两个模型,一个是磷酸肌酸(PCr)模型,另一个是假体内模型。每个实验数据集包括 PCr 假体、蛋白假体和体内大鼠大脑,在 3 T 人体扫描仪和 9.4 T 动物扫描仪上依次获取。结果表明,合成的 9.4 T Z 光谱与实验地面实况几乎一致,显示出较低的 RMSE(7 个 PCr 管为 0.11% ± 0.0013%,3 个蛋白管为 1.8% ± 0.2%,3 个大鼠切片为 0.79% ± 0.54%)和较高的 R2(大于 0.99)。使用洛伦兹差分法计算的合成酰胺和 NOE 对比图也与实验结果十分吻合。此外,合成模型对 B0 不均匀性、噪声和其他采集缺陷具有鲁棒性。总之,所提出的框架能从低 B0 Z 谱合成高 B0 Z 谱,这将有助于 CEST MRI 的量化和应用。
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来源期刊
NMR in Biomedicine
NMR in Biomedicine 医学-光谱学
CiteScore
6.00
自引率
10.30%
发文量
209
审稿时长
3-8 weeks
期刊介绍: NMR in Biomedicine is a journal devoted to the publication of original full-length papers, rapid communications and review articles describing the development of magnetic resonance spectroscopy or imaging methods or their use to investigate physiological, biochemical, biophysical or medical problems. Topics for submitted papers should be in one of the following general categories: (a) development of methods and instrumentation for MR of biological systems; (b) studies of normal or diseased organs, tissues or cells; (c) diagnosis or treatment of disease. Reports may cover work on patients or healthy human subjects, in vivo animal experiments, studies of isolated organs or cultured cells, analysis of tissue extracts, NMR theory, experimental techniques, or instrumentation.
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