Computational and phantom-based optimization of dc neuronal current imaging (dcNCI) with ultra-low-field nuclear magnetic resonance (ULF NMR)

该项目对超低场核磁共振（ULF NMR）神经元电流成像（NCI）的进一步发展和潜在的首次体内验证具有重要意义。NCI是脑功能成像的一种新模式，在空间和时间分辨率方面与其他现有的功能工具（如功能性磁共振成像（fMRI）和脑电图或脑磁图（EEG或MEG））互补。NCI直接测量脑内神经元电流产生的微弱和局部（<1mm）磁场对核磁共振信号的影响，因此不存在非唯一性。具有空间和时间模式的神经元磁场将为神经元活动的定位提供自然对比。我们专注于成像长期持续的大脑活动（~s），创造模式dcNCI，通过使用最新一代的ULF-NMR仪器。ulf区（~µT）优于高场区（~T），因为它消除了敏感性伪像，这是证明体内NCI的主要障碍。体感诱发的长时间大脑活动的偶极子强度和位置通过脑磁图估计，并通过包含单电流偶极子的简化幻影再现。在PTB时，使用一维相位编码方案将简化的模体用于初始核磁共振测量。这项dcNCI可行性研究实现了对大约150 nAm的小偶极电流的检测，大约比相应的大脑活动强度高3倍。这说明需要在对比噪声比（CNR）方面进行实质性改进。在这里，我们将首先开发一个基于计算电磁模型的框架，该模型能够模拟由核磁共振线圈和包含单个偶极源的幻体产生的磁场。利用核磁共振线圈设置和偶极源产生的验证场分布作为输入，通过求解Bloch方程，可以得到采用一维相位编码方案的dcNCI的数值模拟。通过MEG和ULF-NMR测量来验证计算模型是本项目不可或缺的一部分。该项目的第二部分包括构建更复杂的具有附加偶极源的幽灵及其相关的验证计算电磁模型。有了这个框架，我们将能够利用ULF NMR优化dcNCI序列，以获得最大的CNR，从而提高对最小可检测偶极子强度的灵敏度。此外，由于人体计算模型的可用性，优化的dcNCI序列将在现实大脑模型中针对扩展偶极子进行数值测试。该项目的输出将是一个多功能和经过验证的模拟工具，可用于预测和优化dcNCI序列，并有望首次在体内演示。

This project forms a vital part in the further development and potential first time in vivo demonstration of neuronal current imaging (NCI) using ultra-low-field nuclear magnetic resonance (ULF NMR) by means of optimized NMR sequences. NCI is a new modality for imaging brain function, complementary, in terms of both spatial and temporal resolution, to other existing functional tools such as functional Magnetic Resonance Imaging (fMRI) and Electro-or Magnetoencephalography (EEG or MEG). NCI measures directly the influence of weak and localized (<1mm) magnetic fields due to neuronal currents in the brain on NMR signals and hence does not suffer from non-uniqueness. The neuronal magnetic field with their spatial and temporal pattern will provide the natural contrast to localize neuronal activity.We focus on imaging long lasting brain activities (~s), coining the modality dcNCI, by using the latest generation of ULF-NMR instrumentation. The ULF-regime (~µT) is superior over the high field region (~T) as it eliminates susceptibility artefacts, a major obstacle in the demonstration of in vivo NCI. The dipole strength and position of somatosensory evoked, long lasting brain activities were estimated by MEG on volunteers and were reproduced with simplified phantoms containing a single current dipole. At PTB, the simplified phantom was used for initial NMR measurement using a 1D phase encoding scheme. This dcNCI feasibility study achieved the detection of small dipolar currents of about 150 nAm, about a factor 3 higher than the intensity of corresponding brain activities. This illustrated the need of substantial improvements in terms of Contrast-to-Noise ratio (CNR).Here, we will initially develop a framework based on computational electromagnetic models capable of simulating both the magnetic fields produced by the NMR coils and the phantom containing the single dipolar source. Numerical simulations of dcNCI using the 1D phase encoding scheme can be obtained by solving the Bloch equations using as input validated field distributions generated by both the NMR coil setup and the dipolar source. It is an integral part of this project to validate the computational model by MEG and ULF-NMR measurements.The second part of the project consists of the construction of more complex phantoms with additional dipolar sources and their associated validated computational electromagnetic models. With this framework we will be able to optimize the sequence for dcNCI with ULF NMR to obtain maximum CNR to improve the sensitivity with regard to the minimum detectable dipole strength. Moreover, due to the availability of computational models of the human body, optimized dcNCI sequences will be numerically tested against extended dipoles within realistic brain models.The output of this project will be a versatile and validated simulation tool usable for predicting and optimizing sequences for dcNCI with ULF NMR with the prospect of its first in vivo demonstration.