Next-Generation Thalamic Nuclei Visualization and Segmentation Methods

下一代丘脑核可视化和分割方法

• 批准号: 10745839
• 负责人: Manojkumar Saranathan
• 金额: $ 49.79万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10745839
• 关键词: Next; Generation; Thalamic; Nuclei; Visualization

PROJECT SUMMARY/ABSTRACT The thalamus is associated with critical neurological functions like regulation of consciousness, sleep, arousal, and alertness in addition to relaying signals to the cortex. It is divided into multiple functionally specialized units called thalamic nuclei which have been implicated in several psychiatric and neurodegenerative diseases such as essential tremor, Parkinson’s disease, and schizophrenia. Automated segmentation of thalamic nuclei from MRI data is not commonplace, due to their poor visibility in conventional MRI. Segmentation methods based on Diffusion Tensor Imaging (DTI) have been limited by the spatial resolution of the echo-planar imaging (EPI) acquisition and poor diffusion anisotropy in the grey-matter dominated thalamus. As a result, most neuroimaging studies treat the thalamus as a single entity, characterizing whole volume changes and using the whole thalamus as a seed for connectivity analyses, significantly reducing sensitivity to nuclear-specific changes in pathology. We have developed automated multi- atlas as well as deep-learning based thalamic nuclei segmentation techniques based on a novel white-matter- nulled contrast scheme. The purpose of this grant is to develop the next-generation methods for thalamic visualization and segmentation using multi-contrast imaging and cutting-edge image processing techniques and testing it on pediatric as well as geriatric populations. This will be achieved using the following aims: a) Development of a novel fast motion-robust multi-contrast imaging sequence which will provide co-registered susceptibility weighted and MPRAGE images with different contrasts (e.g. white-matter and CSF-nulled). b) Acquisition and creation of age-stratified atlases using the proposed multi-contrast sequence c) Development of a multi-contrast deep-learning based automatic segmentation scheme d) Development of a contrast-synthesis strategy to segment conventional MPRAGE and SWI data but leveraging the multi-contrast atlas developed e) Documenting changes in anatomical, functional and structural connectivity using data from publicly available OASIS (geriatric) and ABIDE (pediatric) databases using the proposed segmentation methods. The segmentation methods developed here can be used characterize thalamic atrophy in normal aging and in disease populations with high sensitivity. The project is expected to yield new MR imaging biomarkers which could be used in future studies for the identification and evaluation of novel therapeutic targets.

项目概要/摘要 丘脑与重要的神经功能相关，例如意识、睡眠、觉醒、 除了向皮层传递信号外，还具有警觉性。它分为多个功能专业化的单位 称为丘脑核，与多种精神疾病和神经退行性疾病有关，例如 如特发性震颤、帕金森病和精神分裂症。 从 MRI 数据中自动分割丘脑核并不常见，因为它们在 常规 MRI。基于扩散张量成像（DTI）的分割方法受到以下因素的限制： 平面回波成像 (EPI) 采集的空间分辨率和灰质中较差的扩散各向异性 丘脑占主导地位。因此，大多数神经影像学研究将丘脑视为一个单一的实体， 表征整个体积变化并使用整个丘脑作为连接分析的种子， 显着降低对病理学核特异性变化的敏感性。我们开发了自动化多 图集以及基于新型白质的基于深度学习的丘脑核分割技术 归零对比度方案。这笔赠款的目的是开发下一代丘脑方法 使用多重对比成像和尖端图像处理技术进行可视化和分割 并对儿童和老年人群进行测试。这将通过以下目标来实现： a) 开发一种新颖的快速运动鲁棒性多对比成像序列，该序列将提供共同配准 具有不同对比度的磁敏感加权图像和 MPRAGE 图像（例如白质和 CSF 无效）。 b）使用所提出的多对比序列获取和创建年龄分层图集 c) 开发基于多对比深度学习的自动分割方案 d) 开发对比合成策略来分割传统的 MPRAGE 和 SWI 数据，但 利用开发的多对比图集 e) 使用公开数据记录解剖、功能和结构连接的变化 使用所提出的分割方法的 OASIS（老年）和 ABIDE（儿科）数据库。 这里开发的分割方法可用于表征正常衰老和 具有高敏感性的疾病人群。该项目预计将产生新的 MR 成像生物标志物 可用于未来的研究，以识别和评估新的治疗靶点。