Automated Quality Evaluation and Harmonization for Multisite ASL MRI Data

多站点 ASL MRI 数据的自动质量评估和协调

• 批准号: 10742638
• 负责人: Sudipto Dolui
• 金额: $ 24.51万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 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 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10742638
• 关键词: Address; Adopted; Aging; Algorithms; Alzheimer disease detection; Alzheimer' s Disease; Alzheimer' s disease related dementia; Automated Indexing; Biological; Biological Preservation; Brain; Brain imaging; Cerebrovascular Circulation; Clinical; Computer software; Data; Data Set; Diagnosis; Early Diagnosis; Ensure; Evaluation; Evaluation Indexes; Future; Goals; Head; Human; Image; Impaired cognition; Infrastructure; Longitudinal Studies; Magnetic Resonance Imaging; Manuals; Maps; Mathematics; Measures; Meta-Analysis; Methods; Modification; Morphologic artifacts; Motion; Noise; Patients; Perfusion Weighted MRI; Phase; Physiological; Probability; Prognosis; Protocols documentation; Quality Control; Reading; Reproducibility; Research; Research Infrastructure; Scanning; Signal Transduction; Site; Software Tools; Source; Statistical Data Interpretation; Techniques; Technology; Testing; Time; Training; Travel; Validation; Vendor; Visual; Work; age effect; arterial spin labeling; biomarker development; cerebrovascular; clinical research site; combat; data harmonization; data preservation; data quality; deep learning; deep learning algorithm; detection sensitivity; imaging modality; improved; indexing; interest; large datasets; learning network; mild cognitive impairment; multimodality; multiple data types; neuroimaging; novel; open source; preservation; prototype; research study; secondary analysis; sex; signal processing; tool; treatment response

SUMMARY Cerebral blood flow (CBF) is a fundamental physiological parameter reflecting cerebrovascular integrity and brain function. Regional CBF alterations are well documented in Alzheimer's disease and related dementias (ADRD) and can contribute to ADRD prognosis, diagnosis, and differentiation. Arterial Spin Labeled (ASL) perfusion magnetic resonance imaging (MRI) is the only non-invasive method for imaging regional CBF and has been increasingly adopted in multi-site clinical ADRD research. However, ASL signals are prone to artifact from physiological noise and other nonidealities. Additionally, while multi-site ASL data can provide the statistical power needed to assess regional CBF changes in the ADRD continuum, they are often acquired with different ASL protocols from different scanner vendors and software versions leading to bias and variance in site specific CBF values. Both factors can obscure biological effects of interest, even in large datasets. The purpose of this R21/R33 study is to develop an ASL research framework for multisite clinical research studies aimed at I) developing automated indices to rate the quality of CBF maps that can be used reproducibly in place of more subjective visual inspection for quality control (QC) as well as for identifying the likely source of artifacts, and II) harmonizing multisite ASL data to remove site effects. The R21 phase will focus on method prototyping and validations using ASL data from a single vendor platform. Expert ratings for quality of ASL CBF maps and predetermined features of CBF maps will be used to train a deep learning (DL) algorithm that can automatically generate a quality index. The DL algorithm will be validated using synthetic data and expert reading results as well as cross-validations. Another DL algorithm will be used to identify the probable source of artifacts in input CBF maps. Prototype harmonization will be based on approaches previously used for structural MRI, but with ASL specific modifications involving mathematical manipulations to preserve more biological effects and to avoid under-harmonization. The R33 phase will substantially expand the methods developed in the R21 phase to be generalizable to multiple vendor platforms. A data driven DL network, as opposed to predetermined features used in the R21 phase, will provide both a composite and a voxel-wise quality index for the input ASL CBF images; the latter allowing regional CBF data to be retained instead of discarding the whole volume. We will also expand the capability of harmonization to cover data from unseen scanners. Finally, we will demonstrate the benefit of these methods in ADRD research by testing hypothesis that use of these research strategies will increase sensitivity for differentiating patients with mild cognitive impairment from cognitively normal older subjects. This novel project leverages our decades of expertise in ASL technologies and ASL-based ADRD research and our access to multi-site ASL data. We have provided substantial pilot data to show feasibility of the proposed work. The resulting research infrastructure will be disseminated freely for research use using Github and our open-source software toolkit ASLtbx.

总结 脑血流量是反映脑血管完整性的基本生理参数 和大脑功能。区域CBF改变在阿尔茨海默病和相关疾病中有很好的记录。 痴呆（ADRD），并有助于ADRD的预后，诊断和分化。动脉自旋标记 (ASL)灌注磁共振成像（MRI）是唯一的非侵入性方法成像区域CBF 并且已经越来越多地被多中心临床ADRD研究采用。然而，ASL信号倾向于 来自生理噪声和其他非理想的伪影。此外，虽然多站点ASL数据可以提供 在ADRD连续体中评估区域CBF变化所需的统计能力，它们通常是通过 来自不同扫描仪供应商和软件版本的不同ASL协议导致 部位特定CBF值。这两个因素都可能掩盖感兴趣的生物学效应，即使在大型数据集中也是如此。的 本R21/R33研究的目的是为多中心临床研究开发ASL研究框架 目的是I）开发自动化指数，以评价CBF图的质量，这些图可以重复使用， 用于质量控制（QC）以及识别可能的 伪影，以及II）协调多站点ASL数据以消除站点影响。R21阶段将侧重于方法 使用来自单一供应商平台的ASL数据进行原型设计和验证。专家对ASL CBF质量的评价 图和CBF图的预定特征将用于训练深度学习（DL）算法，该算法可以 自动生成质量指数。DL算法将使用合成数据和专家验证 阅读结果以及交叉验证。另一种DL算法将用于识别可能的源 输入CBF图中的伪影。原型统一将基于以前用于 结构MRI，但ASL特定的修改涉及数学操作，以保留更多 生物效应，避免不协调。R33阶段将大幅扩展这些方法 在R21阶段开发，可推广到多个供应商平台。数据驱动的DL网络，如 与在R21阶段中使用的预定特征相反，将提供复合的和逐体素的 输入ASL CBF图像的质量指数;后者允许保留区域CBF数据，而不是 丢弃整个体积。我们还将扩大协调的能力，以涵盖来自未见过的数据。 扫描仪最后，我们将通过检验假设来证明这些方法在ADRD研究中的益处 使用这些研究策略将提高区分轻度认知障碍患者的敏感性， 认知正常的老年受试者的损害。这个新颖的项目利用了我们数十年的专业知识， ASL技术和基于ASL的ADRD研究以及我们对多站点ASL数据的访问。我们提供了 大量的试验数据，以显示拟议工作的可行性。由此产生的研究基础设施将 使用Github和我们的开源软件工具包ASLtbx免费传播供研究使用。