Automated Quality Evaluation and Harmonization for Multisite ASL MRI Data

摘要 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。阿尔茨海默病和相关的局部脑血流量改变被很好地记录下来 痴呆症(ADRD)，有助于ADRD的预后、诊断和鉴别诊断。动脉自旋标记 (ASL)核磁共振灌注成像(Mri)是唯一一种无创的局部脑血流成像方法。 并已越来越多地被用于多部位临床ADRD研究。然而，ASL信号容易发生 生理噪音和其他非理想性产生的伪影。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协议导致在 站点特定的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。这个 本R21/R33研究的目的是为多点临床研究开发ASL研究框架 旨在i)开发自动化指数，以评定可在以下方面重复使用的CBF地图的质量 用于质量控制(QC)以及确定可能的来源的更主观的目视检查的场所 伪影，以及ii)协调多站点ASL数据以消除站点影响。R21阶段将专注于方法 使用来自单一供应商平台的ASL数据进行原型制作和验证。ASL CBF质量专家评级 将使用地图和CBF地图的预定特征来训练深度学习(DL)算法，该算法可以 自动生成质量指数。将使用合成数据和专家对DL算法进行验证 阅读结果以及交叉验证。将使用另一种DL算法来识别可能的源 输入CBF地图中的伪影。原型协调将基于以前用于 结构磁共振成像，但具有ASL特定的修改，涉及数学操作，以保存更多 生物效应，并避免不协调。R33阶段将极大地扩展方法 在R21阶段开发，可推广到多个供应商平台。数据驱动的下行链路网络，AS 与在R21阶段中使用的预定特征相反，它将提供合成和体素方式 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 丢弃整个卷。我们还将扩大协调能力，以涵盖来自看不见的数据 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 这些研究策略的使用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造成的损害。这个新颖的项目利用了我们几十年来在 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。我们已经提供了 大量的试点数据表明拟议工作的可行性。由此产生的研究基础设施将是 使用Github和我们的开源软件工具包ASLtbx免费分发以供研究使用。

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.