3T时的化学交换饱和转移(CEST)MRI由于多种代谢物的重叠CEST效应而具有低特异性,而更高的场强(B0)允许更好地分离Z光谱峰,“辅助信号解释和量化。然而,较高B0的数据采集受到设备访问的限制,现场不均匀性和安全问题。在这里,我们的目标是使用深度学习框架从3T临床扫描仪获得的现成数据中合成更高B0的Z光谱。使用基于Bloch-McConnell方程的模型对仿真数据进行了培训,该框架包括两个深度神经网络(DNN)和一个奇异值分解(SVD)模块。第一DNN识别Z频谱中的B0移位,并且将它们对准以校正频率。B0校正后,较低的B0Z光谱被简化为第二个DNN,投射到较高B0Z光谱的关键特征表示中,通过SVD截断获得。最后,从逆SVD中恢复了完整的较高B0Z光谱,考虑到Z谱的低秩性质。本研究构建并验证了两个模型,磷酸肌酸(PCr)模型和假体内模型。每个实验数据集,包括PCr幻影,蛋清幻影,和体内大鼠的大脑,在3T人类和9.4T动物扫描仪上顺序获得。结果表明,合成的9.4TZ光谱几乎与实验的地面事实相同,显示低RMSE(七个PCr管的0.11%±0.0013%,三个蛋清管1.8%±0.2%,和三个大鼠切片的0.79%±0.54%)和高R2(>0.99)。合成的酰胺和NOE对比图,使用洛伦兹差计算,也很好地与实验相匹配。此外,合成模型对B0不均匀性表现出鲁棒性,噪音,和其他收购缺陷。总之,所提出的框架能够从较低B0的Z光谱合成较高B0的Z光谱,这可能有助于CESTMRI量化和应用。
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.