A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging
A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging
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DOI：
10.1016/j.neuroimage.2020.117165
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发表时间：
2020-11-01
期刊：
影响因子：
5.7
通讯作者：
Heo, Hye-Young
中科院分区：
文献类型：
--
作者：
Kim, Byungjai;Schaer, Michael;Heo, Hye-Young
基于MT现象的半固体磁化转移对比（MTC）和化学交换饱和转移（CEST） MRI已显示出评估大脑发育、神经、精神和神经退行性疾病的潜力。然而，通常用于常规MTC成像的定性MT比（MTR）度量在定量评估半固体大分子质子交换速率和浓度方面受到限制。此外，通过MTR不对称分析测得的CEST信号不可避免地受到移动和半固态大分子的上场核Overhauser增强（NOE）信号的污染。为了解决这些问题，我们开发了一种MTC- mr指纹（MTC- mrf）技术来量化组织参数，这进一步允许在一定的CEST频率偏移下估计准确的MTC信号。采用伪随机射频饱和方案生成不同组织的独特MTC信号演变，并设计有监督的深度神经网络从测量的MTC- mrf信号中提取组织特性。通过详细的基于Bloch方程的数字模型和体内研究，我们证明了与传统的Bloch方程拟合方法相比，MTC- mrf可以以高精度和计算效率量化MTC特征，并为CEST和NOE成像提供基线参考信号。为了验证，利用深度学习方法估计的组织参数合成MTC-MRF图像，并与实验获得的MTC-MRF图像作为参考标准进行比较。提出的MTC- mrf框架可以在临床可接受的扫描时间内提供定量的3D MTC， CEST和NOE人脑成像。
Semisolid magnetization transfer contrast (MTC) and chemical exchange saturation transfer (CEST) MRI based on MT phenomenon have shown potential to evaluate brain development, neurological, psychiatric, and neurodegenerative diseases. However, a qualitative MT ratio (MTR) metric commonly used in conventional MTC imaging is limited in the assessment of quantitative semisolid macromolecular proton exchange rates and concentrations. In addition, CEST signals measured by MTR asymmetry analysis are unavoidably contaminated by upfield nuclear Overhauser enhancement (NOE) signals of mobile and semisolid macromolecules. To address these issues, we developed an MTC-MR fingerprinting (MTC-MRF) technique to quantify tissue parameters, which further allows an estimation of accurate MTC signals at a certain CEST frequency offset. A pseudorandomized RF saturation scheme was used to generate unique MTC signal evolutions for different tissues and a supervised deep neural network was designed to extract tissue properties from measured MTC-MRF signals. Through detailed Bloch equation-based digital phantom and in vivo studies, we demonstrated that the MTC-MRF can quantify MTC characteristics with high accuracy and computational efficiency, compared to a conventional Bloch equation fitting approach, and provide baseline reference signals for CEST and NOE imaging. For validation, MTC-MRF images were synthesized using the tissue parameters estimated from the deep-learning method and compared with experimentally acquired MTC-MRF images as the reference standard. The proposed MTC-MRF framework can provide quantitative 3D MTC, CEST, and NOE imaging of the human brain within a clinically acceptable scan time.