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

健壮的大脑测量工具的开发 超高场7T磁共振 摘要： 总结。神经成像可以提供安全、非侵入性和全脑的测量，用于大型临床和 脑部疾病的研究。然而，许多疾病，如阿尔茨海默病(AD)，会导致 大脑变化的复杂时空模式，通常由于图像有限而难以梳理出来 由流行的3T核磁共振扫描仪提供的质量(全球有20,000多台)。虽然7T磁共振成像 扫描仪提供更好的图像质量，这些超高场扫描仪还没有广泛使用(只有40多台 世界各地都有)，也不用于临床。因此，用于重建7T类高质量 3T 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 训练样本的3T和7T磁共振扫描。该更新项目致力于开发一套新颖的 基于学习的训练对象7T磁共振图像对比度和组织/解剖标记迁移方法 对3T磁共振成像的新课题1)图像质量增强，2)高精度组织分割，3)准确 解剖ROI(感兴趣区)标记，最终4)早期发现大脑疾病，如阿尔茨海默病。 具体地说，(目标1)为了提高3T MRI的图像质量，我们将开发一种新的深度学习 体系结构从培训对象学习复杂的多层3T到7T映射，每个对象都有耦合的3T和7T 7T磁共振扫描。然后，该映射将应用于重建质量增强的7T类MRI扫描 3T磁共振扫描。(目标2)用于脑结构测量(例如，脑萎缩和海马体体积 收缩)，关键的一步是脑组织分割。因此，我们将制定一个稳健和准确的随机 森林组织分割方法，将7T标签信息映射到3T扫描。映射函数为 使用为7T扫描生成的组织标签进行培训，而不是通常图像对比度有限的3T扫描。 (目标3)为了进一步量化感兴趣区甚至海马亚区(即海马区)的局部萎缩，我们将 提出了一种利用(A)随机森林进行预测的可变形多ROI分割方法 通过自适应积分多模式(解剖学， 结构和功能连通性)信息和(B)迭代地精炼ROI的自动上下文模型 分割结果。请注意，多模式MRI数据的自适应整合，特别是静息状态的fMRI (RS-fMRI)，是分割亚感兴趣区的关键，例如海马亚区，因为局部功能 连通性模式有助于区分通常具有不同 皮质-皮质连接。(目标4)最后，通过整合所有准确分割的解剖特征 ROI/子ROI以及那些分段的ROI/子ROI之间的结构和功能连接特征， 我们可以更有效地检测早期大脑障碍，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，使用 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。 申请。这些计算方法将在不同的领域得到应用，即对大脑进行量化 与各种神经疾病(即阿尔茨海默病和精神分裂症)相关的异常， 测量不同药物干预对大脑的影响，并找到两者之间的联系 影像和临床评分。

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.