Acquisition-independent machine learning for morphometric analysis of underrepresented aging populations with clinical and low-field brain MRI

项目摘要 职务名称： 采集无关机器学习用于代表性不足的老龄人口的形态测量分析 临床和低场脑部核磁共振成像 总结： 磁共振成像（MRI）通过提供一个窗口， 健康衰老和疾病中的大脑。这场革命的一个关键方面是能够获得 使用软件包如FreeSurfer（由 我们的实验室）、FSL、AFNI或SPM。这些软件包依赖于计算机算法，这些算法最适合各向同性数据， 1 mm MP-MRI扫描。不幸的是，临床扫描通常是高度各向异性的（例如，6 mm间距 在切片之间），从而排除了使用上述软件包的自动形态测定分析。图像 用便携式和非便携式低场扫描仪获取的图像也受到同样的限制。 无法处理临床MRI妨碍了从MRI中提取精确的形态测量结果。 临床MRI研究（定量成像），以及可能是唯一成像的低场扫描 在医疗服务不足的地区，例如，农村或发展中国家。关键是， 也排除了对世界各地医院PACS中数百万张扫描图的分析， 包括来自人群的大量图像和相关的临床元数据， 在神经影像学研究中代表性不足（例如，黑人，西班牙裔），从而阻碍了老龄化研究的进展。 在这个项目中，我们提出开发AI方法，可以将临床或低场MRI变成各向同性扫描 参考对比度（1 mm MP-100）。重要的是，该方法将：（i）自适应输入的数量 MR序列，以及它们的方向、对比度和分辨率;（ii）对年龄相关的 病理学（萎缩、白色病变）;和（iii）不需要再培训。这些功能将使应用程序 任何MR数据集，无需专门的硬件或机器学习专业知识。所得到的合成扫描可以 可用于广泛的现有形态测定分析，例如，分割、容量测定、配准、 纵向分析，皮质厚度和包裹，等等。该框架的另一个关键特征是 事实上，它产生了协调的图像，这减少了跨站点和脉冲序列的偏差。 我们将通过以下方式验证这些工具：（i）从一些公共数据集进行合成下采样扫描 覆盖不同人群;以及（ii）多模态MRI的专用、多样化、综合数据集， 包括配对研究、临床和低场扫描，专门为此项目采购。我们将仔细 评估我们开发的工具中的偏差，并尝试减轻它们。我们将把工具的最终版本应用于 一项对来自马萨诸塞州总医院的临床老龄队列的大规模研究，以及两项临床研究， 便携式MRI研究。这些工具和新的数据集都将通过FreeSurfer公开发布（60，000 全球许可证），从而使世界各地的研究人员能够分析具有样本量的大型临床数据集 远远高于目前的研究成果。因此，我们的工具承诺增加我们的 了解人类大脑在正常衰老和疾病中的作用，特别是在代表性不足的人群中。

Project Summary Title: Acquisition-independent machine learning for morphometric analysis of underrepresented aging populations with clinical and low-field brain MRI Summary: Magnetic resonance imaging (MRI) has revolutionized research of the human brain, by providing a window to the living brain in healthy aging and disease. A key aspect of this revolution has been the ability to obtain precise morphometric measurements from brain MRI using software packages like FreeSurfer (developed by our lab), FSL, AFNI, or SPM. These packages rely on computer algorithms that work best with isotropic data, 1 mm MP-RAGE scans. Unfortunately, clinical scans are generally highly anisotropic (e.g., 6 mm spacing between slices), precluding automatic morphometric analysis with the aforementioned packages. Images acquired with portable and non-portable low-field scanners also suffer from the same limitation. The inability to process clinical MRI prevents the extraction of precise morphometric measurements from MRI studies in the clinic (quantitative imaging), as well as from low-field scans that may be the only imaging alternative in medically underserved regions, e.g., rural areas or developing countries. Crucially, this inability also precludes the analysis of millions of scans that are sitting in the PACS of hospitals around the world, including large amounts of images and associated clinical metadata from populations that are typically underrepresented in neuroimaging studies (e.g., Black, Hispanic), thus hindering progress in aging research. In this project, we propose to develop AI methods that can turn clinical or low-field MRI into isotropic scans of reference contrast (a 1 mm MP-RAGE). Importantly, the methods will: (i) be adaptive to the number of input MR sequences, as well as their orientation, contrast and resolution; (ii) be robust against aging-related pathology (atrophy, white matter lesions); and (iii) not require retraining. These features will enable application any MR dataset without specialized hardware or machine learning expertise. The resulting synthetic scans can be used for a wide array of existing morphometrics analyses, e.g., segmentation, volumetry, registration, longitudinal analysis, cortical thickness and parcellation, and many more. Another key feature of the framework is the fact that it yields harmonized images, which reduces bias across sites and pulse sequences. We will validate the tools with: (i) synthetically downsampled scans from a number of public datasets covering a diverse population; and (ii) a dedicated, diverse, comprehensive dataset of multi-modal MRI, comprising paired research, clinical and low-field scans, acquired specifically for this project. We will carefully assess the biases in our developed tools and try to mitigate them. We will apply the final version of the tools to a large-scale study of a clinical aging cohort from Massachusetts General Hospital, as well as to two clinical studies with portable MRI. Both the tools and the new dataset will be made publicly through FreeSurfer (60,000 worldwide licenses), thus enabling researchers worldwide to analyze large clinical datasets with sample sizes much higher than those achieved in current research studies. Therefore, our tools promise to increase our understanding of the human brain in normal aging and in disease, particularly in underrepresented populations.