权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI

开发基于超高场 7T MRI 的强大大脑测量工具

基本信息

批准号：
9977173
负责人：
Pew-Thian Yap
金额：
$ 43.82万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-09-17 至 2023-05-31
项目状态：
已结题

项目摘要

Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI Abstract: Summary. Neuroimaging can provide safe, non-invasive, and whole-brain measurements for large clinical and research studies of brain disorders. However, many disorders such as Alzheimer's Disease (AD) cause complex spatiotemporal patterns of brain alterations, which are often difficult to tease out due to limited image quality afforded by the popular 3T MRI scanners (with 20,000+ units available worldwide). Although 7T MRI scanners provide better image quality, these ultrahigh field scanners are not widely available (with only 40+ units available worldwide) and are also not used clinically. Thus, tools for reconstructing 7T-like high-quality MRI from 3T MRI scan are highly desirable. A means for achieving this is by learning the relationship between 3T and 7T MRI scans from training samples. This renewal project is dedicated to developing a set of novel learning-based methods to transfer image contrast and tissue/anatomical labels of 7T MRI of training subjects to 3T MRI of new subjects for 1) image quality enhancement, 2) high-precision tissue segmentation, 3) accurate anatomical ROI (region of interest) labeling, and eventually 4) early detection of brain disorders such as AD. Specifically, (Aim 1) to enhance the image quality of 3T MRI, we will develop a novel deep learning architecture to learn a complex multi-layer 3T-to-7T mapping from training subjects, each with coupled 3T and 7T MRI scans. This mapping will then be applied to reconstruct quality-enhanced 7T-like MRI scans from new 3T MRI scans. (Aim 2) For brain structural measurement (e.g., brain atrophies, and hippocampal volume shrinkage), a crucial step is brain tissue segmentation. We will thus develop a robust and accurate random forest tissue segmentation method, which maps 7T label information to 3T scans. The mapping function is trained using tissue labels generated for 7T scans, instead of 3T scans which often have limited image contrast. (Aim 3) To further quantify local atrophies in ROIs or even sub-ROIs (i.e., hippocampal subfields), we will develop a deformable multi-ROI segmentation method by employing (a) random forest to predict deformation from each image location to the target boundary by adaptive integration of multimodal (anatomical, structural & functional connectivity) information and (b) auto-context model to iteratively refine ROI segmentation results. Note that the adaptive integration of multimodal MRI data, especially resting-state fMRI (rs-fMRI), is critical to the segmentation of sub-ROIs such as hippocampal subfields, since local functional connectivity patterns can help distinguish boundaries between neighboring subfields that often have different cortico-cortical connections. (Aim 4) Finally, by integrating anatomical features from all accurately segmented ROIs/sub-ROIs and also structural & functional connectivity features between those segmented ROIs/sub-ROIs, we can more effectively detect early-stage brain disorders, i.e., the conversion of Mild Cognitive Impairment (MCI) to AD. We will integrate information from different imaging datasets and multiple imaging centers by using our novel multi-task learning approach for jointly learning the respective disease prediction models. Applications. These computational methods will find their applications in diverse fields, i.e., quantifying brain abnormalities associated with various neurological diseases (i.e., Alzheimer's disease and schizophrenia), measuring the effects of different pharmacological interventions on the brain, and finding associations between imaging and clinical scores.

开发强大的大脑测量工具，超高场7 T MRI 摘要：摘要神经成像可以为大型临床和实验室提供安全、非侵入性和全脑测量。大脑紊乱的研究。然而，许多疾病如阿尔茨海默病（AD）引起大脑变化的复杂时空模式，由于图像有限，通常很难梳理出来质量由流行的3 T MRI扫描仪提供（全球有20，000多台）。7T MRI 扫描仪提供更好的图像质量，这些双视场扫描仪并没有广泛使用（只有40+ 全球可用的单位），也不用于临床。因此，用于重建7 T样高质量来自3 T MRI扫描的MRI是非常理想的。实现这一点的一种方法是通过学习来自训练样本的3 T和7 T MRI扫描。这个更新项目致力于开发一套新颖的基于学习的方法，用于传输训练受试者的7 T MRI的图像对比度和组织/解剖标记对新受试者的3 T MRI进行1）图像质量增强，2）高精度组织分割，3）准确解剖学ROI（感兴趣区域）标记，以及最终4）AD等脑部疾病的早期检测。具体来说，（目标1）为了提高3 T MRI的图像质量，我们将开发一种新的深度学习方法。体系结构，从训练对象中学习复杂的多层3 T到7 T映射，每个对象都耦合3 T和 7 T MRI扫描。然后将该映射应用于从新的MRI图像重建质量增强的7 T样MRI扫描。 3 T MRI扫描。(Aim 2）对于大脑结构测量（例如，脑萎缩和海马体积收缩），关键的一步是脑组织分割。因此，我们将开发一个强大的和准确的随机森林组织分割方法，将7 T标签信息映射到3 T扫描。述映射功能被使用为7 T扫描而不是通常具有有限图像对比度的3 T扫描生成的组织标签进行训练。 (Aim 3）为了进一步量化ROI或甚至亚ROI中的局部萎缩（即，海马子域），我们将开发一种可变形的多感兴趣区域分割方法，采用（a）随机森林来预测通过多模态（解剖学，结构和功能连接性）信息和（B）自动上下文模型以迭代地细化ROI 分割结果。注意，多模态MRI数据的自适应整合，特别是静息状态fMRI，（rs-fMRI），是至关重要的分割的子ROI，如海马子域，因为局部功能连接性模式可帮助区分通常具有不同皮质-皮质连接(Aim 4）最后，通过整合来自所有精确分割的解剖特征， R 0 I/子R 0 I以及那些分段的R 0 I/子R 0 I之间的结构和功能连接特征，我们可以更有效地检测早期脑部疾病，即，轻度认知障碍的转化 (MCI)到AD。我们将整合来自不同成像数据集和多个成像中心的信息，我们的新的多任务学习方法，用于联合学习各自的疾病预测模型。应用.这些计算方法将在不同的领域中找到它们的应用，即，量化大脑与各种神经疾病相关的异常（即，阿尔茨海默病和精神分裂症），测量不同药物干预对大脑的影响，并发现成像和临床评分。