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

项目摘要 标题： 用于欠代表性老龄化人口形态计量分析的独立于获取的机器学习 结合临床和低场脑MRI 摘要： 磁共振成像(MRI)通过提供一个窗口，为人类大脑的研究带来了革命性的变化 健康衰老和疾病中活着的大脑。这场革命的一个关键方面是有能力获得 使用Freesurfer(由开发)等软件包对脑MRI进行精确的形态测量 我们的实验室)、FSL、AFNI或SPM。这些程序包依赖于对各向同性数据处理得最好的计算机算法， 1 mm MP-RAGE扫描。不幸的是，临床扫描通常是高度各向异性的(例如，6 mm间隔 切片之间)，排除了前述包的自动形态测量分析。图片 使用便携式和非便携式低场扫描仪获得的设备也受到同样的限制。 由于无法处理临床MRI，无法提取精确的形态测量数据 临床上的MRI研究(定量成像)，以及来自低场扫描的可能是唯一的成像 在医疗服务不足的地区，例如农村地区或发展中国家，可供选择。关键是，这种无能为力 也排除了对世界各地医院的PACS中数百万次扫描的分析， 包括来自人群的大量图像和相关联的临床元数据 在神经影像研究中的代表性不足(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，我们建议开发人工智能方法，将临床或低场MRI转换为各向同性扫描 参考对比度(1 mm MP-RAGE)。重要的是，这些方法将：(1)适应投入的数量 磁共振序列及其取向、对比度和分辨率；(2)对衰老相关的健壮 病理(萎缩、脑白质损害)；以及(Iii)不需要再培训。这些功能将使应用程序 任何没有专门硬件或机器学习专业知识的MR数据集。由此产生的合成扫描可以 可用于广泛的现有形态测量分析，例如分割、体积测量、配准 纵向分析、皮质厚度和分隔，以及更多。该框架的另一个关键功能 事实上，它产生了协调的图像，减少了跨部位和脉冲序列的偏差。 我们将通过以下方式验证这些工具：(I)从多个公共数据集中进行综合下采样扫描 覆盖不同人群；和(Ii)专用的、多样化的、全面的多模式核磁共振数据集， 包括配对研究、临床和低场扫描，专门为这个项目购买。我们会小心地 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 一项来自马萨诸塞州综合医院的临床老年队列的大规模研究，以及对两个临床老年队列的研究 使用便携式核磁共振进行研究。工具和新数据集都将通过免费浏览(60,000)公开 全球许可)，从而使世界各地的研究人员能够分析具有样本大小的大型临床数据集 比目前的研究成果高得多。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情况，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.