Predicting Breath Hold Task Compliance From Head Motion.

IF 3.5 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Timothy B Weng, Gargi Porwal, Dhivya Srinivasan, Ben Inglis, Sabrina Rodriguez, David R Jacobs, Pamela J Schreiner, Farzaneh A Sorond, Stephen Sidney, Cora Lewis, Lenore Launer, Guray Erus, Ilya M Nasrallah, R Nick Bryan, Adrienne N Dula
{"title":"Predicting Breath Hold Task Compliance From Head Motion.","authors":"Timothy B Weng, Gargi Porwal, Dhivya Srinivasan, Ben Inglis, Sabrina Rodriguez, David R Jacobs, Pamela J Schreiner, Farzaneh A Sorond, Stephen Sidney, Cora Lewis, Lenore Launer, Guray Erus, Ilya M Nasrallah, R Nick Bryan, Adrienne N Dula","doi":"10.1002/jmri.70105","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Cerebrovascular reactivity reflects changes in cerebral blood flow in response to an acute stimulus and is reflective of the brain's ability to match blood flow to demand. Functional MRI with a breath-hold task can be used to elicit this vasoactive response, but data validity hinges on subject compliance. Determining breath-hold compliance often requires external monitoring equipment.</p><p><strong>Purpose: </strong>To develop a non-invasive and data-driven quality filter for breath-hold compliance using only measurements of head motion during imaging.</p><p><strong>Study type: </strong>Prospective cohort.</p><p><strong>Participants: </strong>Longitudinal data from healthy middle-aged subjects enrolled in the Coronary Artery Risk Development in Young Adults Brain MRI Study, N = 1141, 47.1% female.</p><p><strong>Field strength/sequence: </strong>3.0 Tesla gradient-echo MRI.</p><p><strong>Assessment: </strong>Manual labelling of respiratory belt monitored data was used to determine breath hold compliance during MRI scan. A model to estimate the probability of non-compliance with the breath hold task was developed using measures of head motion. The model's ability to identify scans in which the participant was not performing the breath hold were summarized using performance metrics including sensitivity, specificity, recall, and F1 score. The model was applied to additional unmarked data to assess effects on population measures of CVR.</p><p><strong>Statistical tests: </strong>Sensitivity analysis revealed exclusion of non-compliant scans using the developed model did not affect median cerebrovascular reactivity (Median [q1, q3] = 1.32 [0.96, 1.71]) compared to using manual review of respiratory belt data (1.33 [1.02, 1.74]) while reducing interquartile range.</p><p><strong>Results: </strong>The final model based on a multi-layer perceptron machine learning classifier estimated non-compliance with an accuracy of 76.9% and an F1 score of 69.5%, indicating a moderate balance between precision and recall for the identification of scans in which the participant was not compliant.</p><p><strong>Data conclusion: </strong>The developed model provides the probability of non-compliance with a breath-hold task, which could later be used as a quality filter or included in statistical analyses.</p><p><strong>Level of evidence: 1: </strong>TECHNICAL EFFICACY: Stage 3.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Magnetic Resonance Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/jmri.70105","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Cerebrovascular reactivity reflects changes in cerebral blood flow in response to an acute stimulus and is reflective of the brain's ability to match blood flow to demand. Functional MRI with a breath-hold task can be used to elicit this vasoactive response, but data validity hinges on subject compliance. Determining breath-hold compliance often requires external monitoring equipment.

Purpose: To develop a non-invasive and data-driven quality filter for breath-hold compliance using only measurements of head motion during imaging.

Study type: Prospective cohort.

Participants: Longitudinal data from healthy middle-aged subjects enrolled in the Coronary Artery Risk Development in Young Adults Brain MRI Study, N = 1141, 47.1% female.

Field strength/sequence: 3.0 Tesla gradient-echo MRI.

Assessment: Manual labelling of respiratory belt monitored data was used to determine breath hold compliance during MRI scan. A model to estimate the probability of non-compliance with the breath hold task was developed using measures of head motion. The model's ability to identify scans in which the participant was not performing the breath hold were summarized using performance metrics including sensitivity, specificity, recall, and F1 score. The model was applied to additional unmarked data to assess effects on population measures of CVR.

Statistical tests: Sensitivity analysis revealed exclusion of non-compliant scans using the developed model did not affect median cerebrovascular reactivity (Median [q1, q3] = 1.32 [0.96, 1.71]) compared to using manual review of respiratory belt data (1.33 [1.02, 1.74]) while reducing interquartile range.

Results: The final model based on a multi-layer perceptron machine learning classifier estimated non-compliance with an accuracy of 76.9% and an F1 score of 69.5%, indicating a moderate balance between precision and recall for the identification of scans in which the participant was not compliant.

Data conclusion: The developed model provides the probability of non-compliance with a breath-hold task, which could later be used as a quality filter or included in statistical analyses.

Level of evidence: 1: TECHNICAL EFFICACY: Stage 3.

从头部运动预测屏气任务依从性。
背景:脑血管反应性反映了脑血流量对急性刺激的反应,反映了大脑匹配血流量以满足需求的能力。带有屏气任务的功能性MRI可用于引发这种血管活性反应,但数据的有效性取决于受试者的依从性。确定屏气依从性通常需要外部监测设备。目的:开发一种非侵入性和数据驱动的质量过滤器,仅使用成像过程中头部运动的测量来实现屏气依从性。研究类型:前瞻性队列。参与者:来自参加青年人冠状动脉风险发展脑MRI研究的健康中年受试者的纵向数据,N = 1141,女性47.1%。场强/序列:3.0特斯拉梯度回声MRI。评估:使用人工标记呼吸带监测数据来确定MRI扫描期间的屏气依从性。利用头部运动的测量方法,建立了一个模型来估计不遵守屏气任务的概率。该模型识别参与者未进行屏气的扫描能力使用包括灵敏度、特异性、召回率和F1分数在内的性能指标进行了总结。将该模型应用于其他未标记数据,以评估CVR对种群测量的影响。统计检验:敏感性分析显示,与人工审查呼吸带数据(1.33[1.02,1.74])相比,使用开发的模型排除不符合扫描不影响中位脑血管反应性(中位[q1, q3] = 1.32[0.96, 1.71]),同时减少了四分位数范围。结果:基于多层感知器机器学习分类器的最终模型估计不合规的准确率为76.9%,F1得分为69.5%,表明识别参与者不合规扫描的精度和召回率之间存在适度平衡。数据结论:开发的模型提供了不遵守屏气任务的概率,可以稍后用作质量过滤器或包含在统计分析中。证据等级:1:技术功效:第3阶段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
9.70
自引率
6.80%
发文量
494
审稿时长
2 months
期刊介绍: The Journal of Magnetic Resonance Imaging (JMRI) is an international journal devoted to the timely publication of basic and clinical research, educational and review articles, and other information related to the diagnostic applications of magnetic resonance.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信