Suppr超能文献

用于[18F]-FEPPA的图像衍生输入函数:在定量人脑转运蛋白(18 kDa)中的应用。

Image derived input function for [18F]-FEPPA: application to quantify translocator protein (18 kDa) in the human brain.

作者信息

Mabrouk Rostom, Rusjan Pablo M, Mizrahi Romina, Jacobs Mark F, Koshimori Yuko, Houle Sylvain, Ko Ji Hyun, Strafella Antonio P

机构信息

Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada.

Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.

出版信息

PLoS One. 2014 Dec 30;9(12):e115768. doi: 10.1371/journal.pone.0115768. eCollection 2014.

Abstract

In [18F]-FEPPA positron emission topography (PET) imaging, automatic blood sampling system (ABSS) is currently the gold standard to obtain the blood time activity curve (TAC) required to extract the input function (IF). Here, we compare the performance of two image-based methods of IF extraction to the ABSS gold standard method for the quantification of translocator protein (TSPO) in the human brain. The IFs were obtained from a direct delineation of the internal carotid signal (CS) and a new concept of independent component analysis (ICA). PET scans were obtained from 18 healthy volunteers. The estimated total distribution volume (V(T)) by CS-IF and ICA-IF were compared to the reference V(T) obtained by ABSS-IF in the frontal and temporal cortex, cerebellum, striatum and thalamus regions. The V(T) values estimated using ICA-IF were more reliable than CS-IF for all brain regions. Specifically, the slope regression in the frontal cortex with ICA-IF was r² = 0.91 (p<0.05), and r² = 0.71 (p<0.05) using CS-IF.

摘要

在[18F]-FEPPA正电子发射断层扫描(PET)成像中,自动血液采样系统(ABSS)是目前获取提取输入函数(IF)所需血液时间-活度曲线(TAC)的金标准。在此,我们将两种基于图像的IF提取方法的性能与ABSS金标准方法进行比较,以定量人脑中转位蛋白(TSPO)。IF是通过直接描绘颈内动脉信号(CS)和独立成分分析(ICA)的新概念获得的。对18名健康志愿者进行了PET扫描。将CS-IF和ICA-IF估计的总分布容积(V(T))与ABSS-IF在额叶和颞叶皮质、小脑、纹状体和丘脑区域获得的参考V(T)进行比较。在所有脑区,使用ICA-IF估计的V(T)值比CS-IF更可靠。具体而言,额叶皮质中ICA-IF的斜率回归r² = 0.91(p<0.05),而CS-IF的r² = 0.71(p<0.05)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b47/4280118/d90199418eff/pone.0115768.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验