Towards optimal diffusion MRI tractography and validation

连接体，一个描述神经元连接图的新术语，是现代神经影像学的核心。事实上，它正在创建一个新的计算机科学领域，称为神经信息学。神经退行性疾病、发育障碍、脑肿瘤和创伤性脑损伤影响到加拿大大部分人口。目前还不清楚白色物质中的纤维连接是如何被这些疾病改变、退化和破坏的。扩散磁共振成像（dMRI）是唯一能够对白色物质的神经结构进行成像并更好地了解大脑如何连接的非侵入性技术。因此，扩散MRI是一种在毫米尺度上测量连接体的短距离和长距离连接的工具。 绘制白色物质连接体的关键部分是通过dMRI纤维束成像计算的测量值。纤维束成像是重建白色纤维束的计算机化过程。不幸的是，目前的纤维束成像技术不控制算法产生的无效纤维束。到目前为止，使用纤维束成像的出版作品缺乏准确性是众所周知的，但大多被忽视，因为没有公共数据库来验证技术。越来越多的证据表明，纤维束成像错误可能会导致错误的连接解释。 因此，我的研究计划的主要目标是开发一种最先进的扩散MRI处理管道，以提高纤维束成像的可重复性和准确性。一个短期目标是提出一个独特的验证机制，新的数据集，从幻影离体数据集和扫描/重新扫描真实的数据集，添加到Tractometer，一个新的在线评估系统，将包括创新的连接性指标。一个中期目标是开发一个新的全球纤维束成像框架的基础上，国家的最先进的处理步骤。这种优化的弥散MRI处理管道将整合采集、图像校正和预处理、局部建模、纤维束成像和可视化工具等所有步骤，以研究解剖连接性和组织微观结构。作为一个长期的目标，我将提出新的显微纤维束成像技术，以验证纤维束成像方法，使用新的逼真的幻影模拟，新的MRI和光学相干断层扫描采集在体外和体内的小动物模型，致力于图像白色物质在微观尺度。 具有用于纤维束成像协议的验证的机制的潜在价值是非常高的。这将对整个神经科学产生重大影响。在我的监督下的学生将在一个多学科领域，由国家的最先进的设备，丰富的本地和国际合作在计算机科学和医学的前沿包围。

The connectome, a novel term for the neuronal map of connections, is at the heart of modern neuroimaging. It is in fact creating a new field of Computer Science called neuroinformatics. Neurodegenerative diseases, developmental disorders, brain tumors and traumatic brain injuries affect a large proportion of the Canadian population. It remains unknown how fiber connections in the white matter are altered, degenerated and damaged by these disorders. Diffusion magnetic resonance imaging (dMRI) is the only non-invasive technique able to image the neural architecture of the white matter and better understand how the brain is wired. Diffusion MRI is thus a tool to measure the connectome for short and long distances connections at the millimeter scale. A crucial part of mapping the white matter connectome are measurements computed from dMRI tractography. Tractography is the computerized process of reconstructing white matter fiber bundles. Unfortunately, current tractography techniques do not control for invalid tracts produced by algorithms. As of today, the lack of accuracy in published works using tractography is well known but has mostly been overlooked because there are no public databases to validate techniques. There is mounting evidence that tractography errors can lead to wrong connectivity interpretations. The main objective of my research program is thus to develop a state-of-the-art diffusion MRI processing pipeline to improve the reproducibility and accuracy of tractography. A short-term objective is to propose a unique validation mechanism with new datasets, from phantom ex vivo datasets and scan/rescan real datasets, added on the Tractometer, a novel online evaluation system that will include innovative connectivity metrics. A mid-term objective is to develop a novel global tractography framework based on state-of-the-art processing steps. This optimized diffusion MRI processing pipeline will integrate all steps from acquisition, image correction and preprocessing, local modeling, fiber tractography and visualization tools to study anatomical connectivity and tissue microstructure. As a long-term objective, I will propose new micro-tractography techniques to validate fiber tractography methods using novel realistic phantom simulations, new MRI and optical coherence tomography acquisitions on ex vivo and in vivo small animal models dedicated to image white matter at the microscopic scale. The potential value of having a mechanism for validation of tractography protocols is very high. This will have a significant impact on neurosciences at large. Students under my supervision will be in a multi-disciplinary area, surrounded by state-of-the-art equipment, rich local and international collaborations at the frontiers of Computer Science and Medecine.