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

该项目形成了进一步开发和潜在的第一次在体内演示的神经元电流成像（NCI）的重要组成部分，使用超低场核磁共振（ULF NMR）通过优化的NMR序列。NCI是一种新的脑功能成像模式，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直接测量由于大脑中的神经元电流引起的弱和局部（<1 mm）磁场对NMR信号的影响，因此不会受到非唯一性的影响。神经元磁场及其空间和时间模式将提供自然对比以定位神经元活动。我们专注于使用最新一代的ULF-NMR仪器成像长持续的脑活动（~s），创造模态dcNCI。ULF方案（~μT）上级高场区域（~T），因为它消除了磁化率伪影，这是体内NCI证明的主要障碍。通过MEG对志愿者的躯体感觉诱发的、持久的脑活动的偶极子强度和位置进行估计，并且用包含单个电流偶极子的简化幻影再现。在PTB，简化体模用于使用1D相位编码方案的初始NMR测量。该dcNCI可行性研究实现了约150 nAm的小偶极电流的检测，约为相应脑活动强度的3倍。这说明需要在对比度噪声比（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NMR线圈和包含单个偶极源的体模产生的磁场。使用1D相位编码方案的dcNCI的数值模拟可以通过使用由NMR线圈设置和偶极源两者生成的经验证的场分布作为输入来求解布洛赫方程来获得。通过MEG和ULF-NMR测量验证计算模型是该项目的一个组成部分。该项目的第二部分包括构建更复杂的具有额外偶极源的模型及其相关的验证计算电磁模型。有了这个框架，我们将能够优化序列的dcNCI与ULF NMR获得最大的CNR，以提高灵敏度方面的最小可检测的偶极强度。此外，由于人体的计算模型的可用性，优化的dcNCI序列将在现实的大脑models.The输出的扩展偶极子进行数值测试将是一个多功能的和验证的模拟工具，可用于预测和优化序列的dcNCI与超低频NMR与其第一次在体内演示的前景。

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.