Towards optimal diffusion MRI tractography and validation

神经连接体是神经元连接图谱的一个新术语，是现代神经成像的核心。事实上，它正在创造一个名为神经信息学的计算机科学新领域。神经退行性疾病、发育障碍、脑瘤和创伤性脑损伤影响着加拿大很大一部分人口。目前尚不清楚这些疾病是如何改变、退化和破坏白质中的纤维连接的。扩散磁共振成像(DMRI)是唯一一种能够成像白质的神经结构并更好地了解大脑是如何连接的非侵入性技术。因此，扩散磁共振成像是一种在毫米尺度上测量短距离和长距离连接的连接体的工具。*绘制白质连接体图的一个关键部分是从dMRI纤维束成像计算的测量结果。纤维束造影术是重建白质纤维束的计算机化过程。不幸的是，目前的轨道成像技术不能控制由算法产生的无效轨道。到今天为止，众所周知的是，使用跟踪照相术发表的作品缺乏准确性，但大多被忽视了，因为没有公共数据库来验证技术。越来越多的证据表明，声道造影术错误会导致错误的连通性解释。*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处理流水线，以提高声道造影术的重复性和准确性。一个短期目标是提出一种独特的验证机制，将新的数据集添加到Tractometer上，新的数据集来自幻影体外数据集和扫描/重新扫描真实数据集，Tractometer是一种新型的在线评估系统，将包括创新的连通性指标。一个新的中期目标是开发一个基于最先进的处理步骤的新的全球制版框架。这一优化的扩散磁共振成像处理流程将整合从采集、图像校正和预处理、局部建模、纤维束成像和可视化工具到研究解剖连通性和组织微观结构的所有步骤。作为一个长期目标，我将提出新的微束成像技术来验证纤维束成像方法，使用新的逼真体模模拟，新的MRI和光学相干层析成像技术在体外和活体小动物模型上致力于在微观尺度上成像白质。*具有验证纤维束成像协议的机制的潜在价值非常高。这将对整个神经科学产生重大影响。在我的指导下，学生将处于一个多学科领域，周围是最先进的设备，在计算机科学和医学的前沿领域有丰富的本地和国际合作。**

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.**