增强型动脉自旋标记成像在帕金森病脑灌注损伤中的应用研究
Application study of enhanced arterial spin labeling for the investigation of cerebral perfusion impairment in Parkinson's disease
王雪 1伍雅婷 1陆瑶 2张洪英 2尚松安 2吴晶涛2
作者信息
- 1. 116044 辽宁大连,大连医科大学研究生院;225001 江苏扬州,江苏省苏北人民医院影像科
- 2. 225001 江苏扬州,江苏省苏北人民医院影像科
- 折叠
摘要
目的:探讨磁共振增强型动脉自旋标记(eASL)技术对细化帕金森病(PD)患者脑血流灌注损伤的价值以及在PD患者与健康对照者(HC)中的分类效能.方法:前瞻性将2020年7月—2022年1月在本院就诊的34例PD患者及年龄、性别以及受教育年限相匹配的35例HCs纳入本研究.采用eASL和常规ASL技术对每例被试行颅脑MR灌注成像,并通过数据后处理获取eASL定量参数[校正脑血流量(CBF)、动脉通过时间(ATT)]和常规ASL定量参数(未校正CBF).采用双样本t检验比较各项灌注参数的组间差异,并应用Spearman相关分析对有显著差异脑区的灌注参数值与临床评分之间的相关性.进一步基于灌注参数构建机器学习模型,评估各分类模型对PD的诊断效能.结果:与未校正CBF相比,校正CBF能更为精准地检测出PD患者的运动相关责任脑区的灌注损伤,表现为PD患者右侧丘脑、双侧中央前回、左侧中央后回等脑区的CBF值增高以及右侧额中回的CBF值减低(FWE校正,P<0.001);而且,PD组左侧额中回的ATT值缩短(FWE校正,P<0.001).PD组左侧壳核、左侧中央前回及左侧中央后回的校正CBF值、左侧壳核的未校正CBF值均与运动功能评分呈显著正相关(P<0.05);右侧角回的校正CBF值、左侧额中回的ATT值均与认知功能评分呈正相关(P<0.05).未校正CBF模型在区分PD患者与HC受试者中的曲线下面积(AUC)为0.82,校正CBF模型的AUC为0.85,基于eASL的多参数联合模型的AUC为0.87.Delong检验显示联合模型的诊断效能优于未校正CBF模型(P<0.05).结论:eASL技术能够准确显示PD患者灌注损伤脑区,并可反映脑组织ATT的异常改变,多灌注参数的结合能具有较好地PD分类诊断效能,从而为PD的临床诊断提供了一定的支撑依据.
Abstract
Objective:To investigate the usefulness of enhanced arterial spin labeling (eASL)for refining cerebral perfusion impairment in Parkinson's patients (PD)and its classification performance in distinguishing patients with PD from healthy controls (HC)subjects.Methods:From July 2020 to January 2022,thirty-four patients with PD who were admitted to our hospital and thirty-five age,gen-der and years of education matched HC subjects were prospectively recruited in this study.Perfusion data from all subjects were acquired by both eASL and conventional ASL.The perfusion parameters were calculated by data processing,as follows:corrected cerebral blood flow (CBF)and arterial transit time (ATT)from eASL and uncorrected CBF from conventional ASL.Two-sample t-test was used to compare the intergroup differences of perfusion parameters,while Spearman rank correlation test was used to explore correlations between the perfusion parameter values of significantly different brain re-gion and scores of clinical assessments.Machine learning models were constructed based on perfusion indexes to evaluate the diagnostic performance of each classification model for PD.Results:Compared with the uncorrected CBF of conventional ASL,the corrected CBF of eASL was more accurately to ex-plore the perfusion impairment of motor-related brain regions,including increased perfusion in right thalamus,bilateral precentral gyrus,and left postcentral gyrus,and decreased perfusion in the right middle frontal gyrus (FWE corrected,P<0.001).And the ATT value of left middle frontal gyrus in the PD group was reduced (FWE corrected,P<0.001).In the PD group,the corrected CBF values in left putamen,left precentral gyrus and postcentral gyrus,and the uncorrected CBF values in left puta-men were all significantly positively correlated with motor function scores (P<0.05),and the correc-ted CBF values of the right angular gyrus and the ATT values in left middle frontal gyrus were posi-tively correlated with cognitive function scores (P<0.05).The area under the curve (AUC)of the uncorrected CBF model in distinguishing PD patients from HC subjects was 0.82,the AUC of the cor-rected CBF model was 0.85,and the AUC of the multiparametric combined model based eASL model was 0.87.The Delong test showed that the diagnostic efficacy of the combined model was significantly higher than that of the uncorrected CBF model (P<0.05).Conclusion:The eASL approach can accu-rately demonstrate the responsible brain regions of perfusion impairment in PD patients,while comple-mentarily provide aberrant changes in ATT,and the integration of multiple perfusion parameters can improve the classification performance,and provide supporting evidence for the clinical diagnosis of PD.
关键词
帕金森病/动脉自旋标记/脑血流量/灌注成像/磁共振成像Key words
Parkinson's disease/Arterial spin labeling/Cerebral blood flow/Perfusion imaging/Magnetic resonance imaging引用本文复制引用
基金项目
国家自然科学基金(82202120)
扬州市重点研发项目(YZ2022071)
扬州市重点研发项目(YZ2023082)
苏北人民医院科研项目(SBLC22004)
出版年
2024
NETL
NSTL
万方数据