Towards optimal diffusion MRI tractography and validation
实现最佳扩散 MRI 纤维束成像和验证
基本信息
- 批准号:RGPIN-2015-05297
- 负责人:
- 金额:$ 2.62万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2018
- 资助国家:加拿大
- 起止时间:2018-01-01 至 2019-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.**
连接组是神经元连接图的一个新术语,是现代神经成像的核心。事实上,它正在创造一个叫做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、脑肿瘤和创伤性脑损伤影响了很大一部分加拿大人口。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)是唯一一种能够对白质的神经结构进行成像并更好地了解大脑是如何连接的非侵入性技术。因此,扩散核磁共振成像是一种测量连接体在毫米尺度上的短距离和长距离连接的工具。***绘制白质连接组的关键部分是通过dMRI束状图计算得出的测量结果。神经束造影是计算机化重建白质纤维束的过程。不幸的是,目前的尿道造影技术不能控制由算法产生的无效尿道。到目前为止,使用牵束造影的已发表作品缺乏准确性是众所周知的,但由于没有公共数据库来验证技术,因此大多被忽视。越来越多的证据表明,神经束造影错误可能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处理管道,以提高束状造影的可重复性和准确性。短期目标是利用新的数据集提出一种独特的验证机制,这些数据集来自幻影离体数据集和扫描/重新扫描真实数据集,并添加到Tractometer上,这是一种新颖的在线评估系统,将包括创新的连接指标。中期目标是根据最先进的处理步骤开发一种新的全球轨道成像框架。这个优化的扩散MRI处理管道将整合从采集、图像校正和预处理、局部建模、纤维束成像和可视化工具的所有步骤,以研究解剖连通性和组织微观结构。作为一个长期目标,我将提出新的微束造影技术来验证纤维束造影方法,使用新颖的逼真的幻影模拟,新的MRI和光学相干断层扫描获取离体和体内小动物模型,致力于在微观尺度上成像白质。***有一个机制来验证牵引造影协议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。在我的指导下,学生将在一个多学科的领域,被最先进的设备所包围,在计算机科学和医学的前沿有丰富的本地和国际合作
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Descoteaux, Maxime其他文献
Altered structural connectivity in olfactory disfunction after mild COVID-19 using probabilistic tractography.
- DOI:
10.1038/s41598-023-40115-7 - 发表时间:
2023-08-09 - 期刊:
- 影响因子:4.6
- 作者:
Bispo, Diogenes Diego de Carvalho;Brandao, Pedro Renato de Paula;Pereira, Danilo Assis;Maluf, Fernando Bisinoto;Dias, Bruna Arrais;Paranhos, Hugo Rafael;von Glehn, Felipe;de Oliveira, Augusto Cesar Penalva;Soares, Alexandre Anderson de Sousa Munhoz;Descoteaux, Maxime;Regattieri, Neysa Aparecida Tinoco - 通讯作者:
Regattieri, Neysa Aparecida Tinoco
TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography.
- DOI:
10.1038/s41597-022-01833-1 - 发表时间:
2022-11-25 - 期刊:
- 影响因子:9.8
- 作者:
Poulin, Philippe;Theaud, Guillaume;Rheault, Francois;St-Onge, Etienne;Bore, Arnaud;Renauld, Emmanuelle;de Beaumont, Louis;Guay, Samuel;Jodoin, Pierre-Marc;Descoteaux, Maxime - 通讯作者:
Descoteaux, Maxime
Tractometer: Towards validation of tractography pipelines
- DOI:
10.1016/j.media.2013.03.009 - 发表时间:
2013-10-01 - 期刊:
- 影响因子:10.9
- 作者:
Cote, Marc-Alexandre;Girard, Gabriel;Descoteaux, Maxime - 通讯作者:
Descoteaux, Maxime
The Role of the Pallidothalamic Fibre Tracts in Deep Brain Stimulation for Dystonia: A Diffusion MRI Tractography Study
- DOI:
10.1002/hbm.23450 - 发表时间:
2017-03-01 - 期刊:
- 影响因子:4.8
- 作者:
Rozanski, Verena Eveline;da Silva, Nadia Moreira;Descoteaux, Maxime - 通讯作者:
Descoteaux, Maxime
DORIS: A diffusion MRI-based 10 tissue class deep learning segmentation algorithm tailored to improve anatomically-constrained tractography.
- DOI:
10.3389/fnimg.2022.917806 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Theaud, Guillaume;Edde, Manon;Dumont, Matthieu;Zotti, Clement;Zucchelli, Mauro;Deslauriers-Gauthier, Samuel;Deriche, Rachid;Jodoin, Pierre-Marc;Descoteaux, Maxime - 通讯作者:
Descoteaux, Maxime
Descoteaux, Maxime的其他文献
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{{ truncateString('Descoteaux, Maxime', 18)}}的其他基金
Advanced developments of diffusion MRI tractography computational methods
扩散MRI纤维束成像计算方法的进展
- 批准号:
RGPIN-2020-04818 - 财政年份:2022
- 资助金额:
$ 2.62万 - 项目类别:
Discovery Grants Program - Individual
Advanced developments of diffusion MRI tractography computational methods
扩散MRI纤维束成像计算方法的进展
- 批准号:
RGPIN-2020-04818 - 财政年份:2021
- 资助金额:
$ 2.62万 - 项目类别:
Discovery Grants Program - Individual
Advanced developments of diffusion MRI tractography computational methods
扩散MRI纤维束成像计算方法的进展
- 批准号:
RGPIN-2020-04818 - 财政年份:2020
- 资助金额:
$ 2.62万 - 项目类别:
Discovery Grants Program - Individual
Towards optimal diffusion MRI tractography and validation
实现最佳扩散 MRI 纤维束成像和验证
- 批准号:
RGPIN-2015-05297 - 财政年份:2019
- 资助金额:
$ 2.62万 - 项目类别:
Discovery Grants Program - Individual
Towards optimal diffusion MRI tractography and validation
实现最佳扩散 MRI 纤维束成像和验证
- 批准号:
RGPIN-2015-05297 - 财政年份:2017
- 资助金额:
$ 2.62万 - 项目类别:
Discovery Grants Program - Individual
Towards optimal diffusion MRI tractography and validation
实现最佳扩散 MRI 纤维束成像和验证
- 批准号:
RGPIN-2015-05297 - 财政年份:2016
- 资助金额:
$ 2.62万 - 项目类别:
Discovery Grants Program - Individual
Towards optimal diffusion MRI tractography and validation
实现最佳扩散 MRI 纤维束成像和验证
- 批准号:
RGPIN-2015-05297 - 财政年份:2015
- 资助金额:
$ 2.62万 - 项目类别:
Discovery Grants Program - Individual
Computational diffusion magnetic resonance imaging: acquisition, modeling, processing and visualization to study brain connectivity and tissue microstructure
计算扩散磁共振成像:采集、建模、处理和可视化,以研究大脑连接和组织微观结构
- 批准号:
386741-2010 - 财政年份:2014
- 资助金额:
$ 2.62万 - 项目类别:
Discovery Grants Program - Individual
Computational diffusion magnetic resonance imaging: acquisition, modeling, processing and visualization to study brain connectivity and tissue microstructure
计算扩散磁共振成像:采集、建模、处理和可视化,以研究大脑连接和组织微观结构
- 批准号:
386741-2010 - 财政年份:2013
- 资助金额:
$ 2.62万 - 项目类别:
Discovery Grants Program - Individual
Computational diffusion magnetic resonance imaging: acquisition, modeling, processing and visualization to study brain connectivity and tissue microstructure
计算扩散磁共振成像:采集、建模、处理和可视化,以研究大脑连接和组织微观结构
- 批准号:
386741-2010 - 财政年份:2012
- 资助金额:
$ 2.62万 - 项目类别:
Discovery Grants Program - Individual
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