标题： 定量磁化率成像在临床脑研究中的敏感性 显示英文标题

标题： Sensitivity of Quantitative Susceptibility Mapping in Clinical Brain Research

摘要： 背景：脑定量磁化率图（QSM）是一种先进的MRI技术，用于根据组织的磁敏感性评估组织特性，而磁敏感性会随着组织成分（如铁、钙和髓磷脂水平）的变化而变化。 QSM 包含多个处理步骤，并且每个步骤都有多种选择。 尽管 QSM 在检测和监测神经退行性疾病方面的应用日益增多，但 QSM 工作流程中算法选择对临床结果的影响尚未得到充分量化。 目的：本研究旨在评估背景场去除（BFR）、反卷积算法以及解剖学参考策略的选择对临床环境中检测深部灰质（DGM）磁敏感性在群体层面和纵向变化的敏感性和可重复性误差的影响。 方法：我们使用健康成年人的十年随访数据集比较了 378 种不同的 QSM 管线。 我们分析了这些管线对随着时间推移检测已知与年龄相关的 DGM 磁敏感性变化的敏感性。 结果：我们发现 QSM 管线检测磁敏感性变化的敏感性存在高度变异性。 研究表明，虽然大多数管线能够可靠地检测到变化，但 BFR 算法的选择和参考策略对结果的可重复性误差和敏感性有显著影响。 值得注意的是，使用 RESHARP 与 AMP-PE、HEIDI 或 LSQR 反演的管线显示出最高的总体敏感性。 结论：研究结果强调了 QSM 处理中算法选择对检测大脑生理变化准确性与可靠性的重要影响。 这对将 QSM 作为疾病进展生物标志物应用于临床研究和试验具有深远意义，表明应仔细考虑管线配置以优化临床结果。 显示英文摘要

摘要： Background: Quantitative susceptibility mapping (QSM) of the brain is an advanced MRI technique for assessing tissue characteristics based on magnetic susceptibility, which varies with the composition of the tissue, such as iron, calcium, and myelin levels. QSM consists of multiple processing steps, with various choices for each step. Despite its increasing application in detecting and monitoring neurodegenerative diseases, the impact of algorithmic choices in QSM's workflow on clinical outcomes has not been thoroughly quantified. Objective: This study aimed to evaluate how choices in background field removal (BFR), dipole inversion algorithms, and anatomical referencing impact the sensitivity and reproducibility error of QSM in detecting group-level and longitudinal changes in deep gray matter susceptibility in a clinical setting. Methods: We compared 378 different QSM pipelines using a 10-year follow-up dataset of healthy adults. We analyzed the sensitivity of pipelines to detect known aging-related susceptibility changes in the DGM over time. Results: We found high variability in the sensitivity of QSM pipelines to detect susceptibility changes. The study highlighted that while most pipelines could detect changes reliably, the choice of BFR algorithm and the referencing strategy substantially influenced the outcome reproducibility error and sensitivity. Notably, pipelines using RESHARP with AMP-PE, HEIDI or LSQR inversion showed the highest overall sensitivity. Conclusions: The findings underscore the critical influence of algorithmic choices in QSM processing on the accuracy and reliability of detecting physiological changes in the brain. This has profound implications for clinical research and trials where QSM is used as a biomarker for disease progression, highlighting that careful consideration should be given to pipeline configuration to optimize clinical outcomes.