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Quantitative chemical exchange saturation transfer (CEST) MRI of glioma using Image Downsampling Expedited Adaptive Least-squares (IDEAL) fitting

机译:使用图像下采样加速自适应最小二乘(IDEAL)拟合的胶质瘤定量化学交换饱和转移(CEST)MRI

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摘要

Chemical Exchange Saturation Transfer (CEST) MRI is sensitive to dilute metabolites with exchangeable protons, allowing tissue characterization in diseases such as acute stroke and tumor. CEST quantification using multi-pool Lorentzian fitting is challenging due to its strong dependence on image signal-to-noise ratio (SNR), initial values and boundaries. Herein we proposed an Image Downsampling Expedited Adaptive Least-squares (IDEAL) fitting algorithm that quantifies CEST images based on initial values from multi-pool Lorentzian fitting of iteratively less downsampled images until the original resolution. The IDEAL fitting in phantom data with superimposed noise provided smaller coefficient of variation and higher contrast-to-noise ratio at a faster fitting speed compared to conventional fitting. We further applied the IDEAL fitting to quantify CEST MRI in rat gliomas and confirmed its advantage for in vivo CEST quantification. In addition to significant changes in amide proton transfer and semisolid macromolecular magnetization transfer effects, the IDEAL fitting revealed pronounced negative contrasts of tumors in the fitted CEST maps at 2 ppm and −1.6 ppm, likely arising from changes in creatine level and nuclear overhauser effects, which were not found using conventional method. It is anticipated that the proposed method can be generalized to quantify MRI data where SNR is suboptimal.
机译:化学交换饱和转移(CEST)MRI对具有可交换质子的稀代谢产物敏感,从而可以对急性中风和肿瘤等疾病进行组织表征。由于其对图像信噪比(SNR),初始值和边界的强烈依赖,因此使用多池洛伦兹拟合进行CEST量化具有挑战性。本文中,我们提出了一种图像下采样加速自适应最小二乘(IDEAL)拟合算法,该算法基于迭代次数较少的下采样图像直到初始分辨率的多池洛伦兹拟合的初始值来量化CEST图像。与常规拟合相比,具有叠加噪声的幻像数据中的IDEAL拟合以较小的拟合速度提供了较小的变异系数和较高的对比度/噪声比。我们进一步将IDEAL拟合应用于大鼠神经胶质瘤中CEST MRI的定量,并证实了其在体内CEST定量中的优势。除了酰胺质子转移和半固态大分子磁化转移效应的显着变化外,IDEAL拟合还显示出拟合的CEST图中肿瘤的明显负对比,分别为2 ppm和-1.6 ppm,可能是肌酸水平变化和核超负荷效应引起的,使用常规方法找不到的。可以预期,所提出的方法可以被普遍用于量化SNR不理想的MRI数据。

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