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

独立于采集的机器学习，通过临床和低场脑 MRI 对代表性不足的老龄化人群进行形态计量分析

• 批准号: 10739049
• 负责人: Juan Eugenio Iglesias Gonzalez
• 金额: $ 243.5万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10739049
• 关键词: Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Archives; Artificial Intelligence; Asian population; Atrophic; Biology; Black Populations; Black race; Brain; Brain imaging; Brain scan; Clinic; Clinical; Clinical Data; Clinical Research; Code; Companions; Computational algorithm; Computer software; Data; Data Set; Dedications; Dementia; Democracy; Detection; Developing Countries; Disease; Disparity; Documentation; Enrollment; General Hospitals; Hispanic; Hispanic Populations; Hospitals; Human; Image; Joints; Lesion; Licensing; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Massachusetts; Measurement; Medically Underserved Area; Metadata; Methods; Minority; Modeling; Modernization; Nerve Degeneration; Nigeria; Noise; Outcome; Pathology; Physicians; Physiologic pulse; Play; Policies; Population; Population Heterogeneity; Positioning Attribute; Process; Randomized; Reduce health disparities; Research; Research Personnel; Resolution; Retrospective Studies; Role; Safety; Sample Size; Sampling; Scanning; Signal Transduction; Site; Slice; Socioeconomic Factors; Source; System; Thick; Training; Uncertainty; Underrepresented Populations; Visual; Work; biobank; brain magnetic resonance imaging; cohort; cost; data sharing; deep learning; disorder subtype; diverse data; ethnic diversity; ethnoracial; health care availability; healthy aging; heterogenous data; high resolution imaging; improved; in vivo; longitudinal analysis; low and middle-income countries; machine learning algorithm; machine learning method; medically underserved; medically underserved population; meetings; morphometry; multimodal data; neural network; neuroimaging; normal aging; open source; portability; prevent; prospective; quantitative imaging; research study; rural area; tool; ultra high resolution; underserved area; usability; white matter

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.

项目摘要 标题： 无独立的机器学习用于代表性不足的老龄化人群的形态分析 与临床和低场大脑MRI 概括： 磁共振成像（MRI）通过提供一个窗口来彻底改变对人脑的研究 健康衰老和疾病中的活体大脑。这场革命的一个关键方面是获得 使用FreeSurfer等软件包从脑MRI进行精确的形态测量（由FreeSurfer开发） 我们的实验室，FSL，AFNI或SPM。这些软件包依赖于与各向同性数据最有效的计算机算法， 1毫米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间距 在切片之间），排除上述软件包的自动形态分析。图像 以便携式和不可便携式的低场扫描仪获得的也有相同的限制。 无法处理临床MRI可以防止从精确的形态测量中提取 诊所中的MRI研究（定量成像）以及可能是唯一成像的低场扫描 在医学上服务不足的地区，例如农村地区或发展中国家。至关重要的是，这种无能 还排除了对全球医院Pac中的数百万扫描的分析， 包括大量的图像和相关的临床元数据，通常是 神经影像学研究的代表性不足（例如黑色，西班牙裔），从而阻碍了衰老研究的进展。 在这个项目中，我们建议开发可以将临床或低场MRI变成各向同性扫描的AI方法 参考对比度（1 mm MP-RAGE）。重要的是，方法将：（i）适应输入数量 MR序列及其方向，对比度和分辨率； （ii）与衰老有关 病理（萎缩，白质病变）； （iii）不需要再培训。这些功能将启用应用程序 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 用于广泛的现有形态计分析，例如分割，体积，注册， 纵向分析，皮质厚度和划线等等。框架的另一个关键功能 它是它产生统一图像的事实，从而减少了跨站点和脉冲序列的偏差。 我们将使用以下方式验证工具：（i）从许多公共数据集中综合采样扫描 涵盖多样化的人口； （ii）多模式MRI的专用，多样化，全面的数据集， 由配对的研究，临床和低场扫描组成，专门为该项目获得。我们会谨慎 评估我们开发的工具中的偏见，并试图减轻它们。我们将将工具的最终版本应用于 一项大规模研究对马萨诸塞州综合医院的临床老化队列以及两个临床 使用便携式MRI研究。工具和新数据集都将通过FreeSurfer公开制造（60,000 全球许可证），从而使全球研究人员能够分析具有样本量的大型临床数据集 比当前的研究中所取得的成就要高得多。因此，我们的工具承诺会增加我们的 了解正常衰老和疾病中人脑的理解，特别是在代表性不足的人群中。