Development of the Connectome II MRI Scanner

我们正在开发一种新的基于扩散核磁共振的脉冲序列、采集和信号建模框架，使我们能够“看到”或检测活体人脑中的精细结构，这些结构比潜在的体素大小小三个数量级，目前使用临床核磁共振技术是看不见的。具体来说，我们获取的图像具有各向同性体素，每侧约1.5 mm，但试图观察微观物体的特征，如轴突直径和轴突直径分布等，这需要约2微米的空间分辨率。我们完成这一壮举的一种方法是开发先进的数学/物理模型，描述观察到的MR信号与所研究的各种微观结构参数之间的关系。校正各种可能模糊或损坏这些图像的伪影也很重要，这些伪影会导致对成像量的不正确估计。然后，我们试图推断这些信号的生物物理基础。一种转换到连接体2.0的方法是AxCaliber MRI，这是我们在美国国立卫生研究院发明和开发的一种方法，但它在传统MRI扫描仪上的分辨率有限。新的连接体2.0扫描仪允许人们检测更细的轴突直径。我们正在迁移的另一种方法是平均表观传播子（MAP） MRI，这是一种测量组织中扩散水分子的净位移分布或平均传播子的方法。这提供了水在活体脑组织中不同微环境的信息。我们转换到连接体扫描仪的另一种方法是时间尺度MRI，它需要在不同的扩散时间获得平均表观传播（MAP） MRI数据。这种方法使我们能够推断分层组织组织的某些特征，例如组织可能的分形维数，我们可以利用这些特征来提供介观和微观尺度的信息。我们也在研究各种用于临床翻译的多脉冲场梯度（mPFG） MRI方法，其中一些我们之前在实验室开发过，我们正在广泛审查，并努力迁移到这个强大的新临床扫描平台。我们开创的一种新的mPFG方法是估计每个体素内的扩散张量分布（DTD）的方法，该方法可用于研究水输送过程的异质性。这在实验设计、计算处理速度和支撑其数学基础方面得到了极大的增强。我们也一直在开发NHP地图集，允许组织学图像数据合并并与MRI数据进行比较，使我们能够测试和审查我们开发的各种MRI方法。在接下来的几年里，我们还需要进行更多的审查和测试，以确保我们的采集和建模管道的准确性和精确性，以便在这项拨款的“未来几年”为临床应用和测试做好准备，届时Connectome 2.0原型扫描仪将完成，并准备对正常志愿者和临床受试者进行扫描。

We are developing a novel diffusion MRI-based pulse sequence, acquisition, and signal modeling framework to enable us to "see" or detect fine-scale structures in the living human brain that are three orders of magnitude smaller than the underlying voxel size, and which are currently invisible using clinical MRI techniques. To be concrete, we acquire images with isotropic voxels that are about 1.5 mm on each side, but attempt to observe features of microscopic objects, such as axon diameters and axon diameter distributions, etc., which require a spatial resolution of about 2 microns. One way we accomplish this feat is to develop advanced mathematical/physical models describing the relationship between the observed MR signal and the various microstructural parameters under investigation. It is also important to correct for various artifacts that can blur or corrupt these images, leading to incorrect estimates of imaging quantities. Then, we attempt to infer the biophysical basis of these signals. One method to translate to the Connectome 2.0 is AxCaliber MRI, an approach we invented and developed at the NIH, but which was limited in its resolution on conventional MRI scanners. The new Connectome 2.0 scanner allows one to detect axons with finer axon diameters. Another approach we are migrating is mean apparent propagator (MAP) MRI, a method that measures the net displacement distribution or average propagator of diffusing water molecules in tissue. This provides information about the different microenvironments water finds itself in within living brain tissue. Another approach we translating to the Connectome scanner is Time-Scaling MRI, which entails obtaining Mean Apparent Propagator (MAP) MRI data at different diffusion times. This approach allows us to infer certain features of hierarchical tissue organizations, such as the possible fractal dimension of tissues, that we can exploit to provide mesoscopic and microscale information. We also are investigating various multiple-pulsed field gradient (mPFG) MRI methods for clinical translation, some of which we have previously developed in our lab, which we are extensively vetting, and working to migrate to this powerful new clinical scanning platform. A new mPFG methodology we have been pioneering is a way to estimate the diffusion tensor distribution (DTD) within each voxel, which can be used to study the heterogeneity of water transport processes. This has been greatly enhanced in terms of experimental design, computational speed of processing, and shoring up its mathematical underpinnings. We have also been developing NHP atlases that allow histological image data to be merged and compared with MRI data to enable us to test and vet various MRI methods we develop. In the coming years, much additional vetting and testing will be required to ensure the accuracy and precision of our acquisition and modeling pipelines so that they are ready for clinical implementation and testing in the "out years" of this grant, when the prototype Connectome 2.0 scanner will be completed and ready for scanning of normal volunteers and clinical subjects.