Reproducibility of [18F]MK-6240 kinetics in brain studies with shortened dynamic PET protocol in healthy/cognitively normal subjects.

IF 3 2区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING EJNMMI Physics Pub Date : 2024-09-27 DOI:10.1186/s40658-024-00679-3
Phelipi N Schuck, Xiuyuan H Wang, Emily B Tanzi, Sally Xie, Yi Li, Sadek A Nehmeh
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引用次数: 0

Abstract

Background: [18F]MK-6240 is a neurofibrillary tangles PET radiotracer that has been broadly used in aging and Alzheimer's disease (AD) studies. Majority of [18F]MK-6240 PET studies use dynamic acquisitions longer than 60 min to assess the tracer kinetic parameters. As of today, no consensus has been established on the optimum dynamic PET scan time. In this study, we assess the reproducibility of [18F]MK-6240 quantitative metrics using shortest dynamic PET protocols in cognitively normal subjects. PET metrics were measured through two-tissue compartment model (2TCM) and Logan model to estimate VT and DVR, as well as SUVR from 90 to 120 min (SUVR90 - 120 min) post-tracer injection for brain regions. 2TCM was carried out using the 120 min dynamic coffee break dataset (first scan from 0 to 60 min p.i., second scan from 90 to 120 min p.i.) and then repeated after stepwise shortening it by 5 min. The dynamic scan length that reproduced the 120 min dynamic scans-based VT to within 10% error was defined as the shortest acquisition time (SAT). The SAT SUVR90 - 120 min was deduced from the SAT dataset by extrapolation of each image pixel time-activity curve to 120 min. The reproducibility of the 120 min dynamic scans-based VT2TCM, DVR2TCM, DVRLogan, and SUVR using the SAT was assessed using Passing-Bablock analysis. The limits of reproducibility of each PET metrics were determined using Bland-Altman analysis.

Results: A dynamic SAT of 40 min yielded < 10% error in [18F]MK-6240 VT2TCM's for all brain regions, compared to those measured using the 120 min datasets. SAT-based analysis did not show statistically significant systemic or proportional biases in VT2TCM, DVR2TCM, DVRLogan, or SUVR compared to those deduced from the full dynamic dataset of 120 min. A mean difference between the 120 min- and SAT-based analysis of less than 4%, 10%, 15%, and 20% existed in the VT2TCM, DVR2TCM, DVRLogan, and SUVR respectively.

Conclusion: Kinetic modeling of [18F]MK-6240 PET can be accurately performed using dynamic scan times as short as 40 min. This can facilitate studies with [18F]MK-6240 PET and improve patients accrual. Further work would be necessary to confirm the reproducibility of these results for patients in dementia spectra.

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在健康/认知正常受试者中使用缩短的动态 PET 方案进行脑研究时,[18F]MK-6240 动力学的再现性。
背景:[18F]MK-6240是一种神经纤维缠结PET放射性示踪剂,已被广泛用于衰老和阿尔茨海默病(AD)研究。大多数[18F]MK-6240 PET 研究使用超过 60 分钟的动态采集来评估示踪剂动力学参数。迄今为止,人们尚未就 PET 动态扫描的最佳时间达成共识。在本研究中,我们在认知正常的受试者中使用最短的动态 PET 方案评估了 [18F]MK-6240 定量指标的可重复性。PET 指标通过双组织区室模型(2TCM)和 Logan 模型进行测量,以估算 VT 和 DVR,以及脑区示踪剂注射后 90 至 120 分钟(SUVR90 - 120 分钟)的 SUVR。2TCM 使用 120 分钟动态咖啡时间数据集(第一次扫描从 0 分钟到 60 分钟,第二次扫描从 90 分钟到 120 分钟),然后在逐步缩短 5 分钟后重复进行。将再现基于 120 分钟动态扫描的 VT 且误差在 10% 以内的动态扫描长度定义为最短采集时间 (SAT)。从 SAT 数据集推断出 SAT SUVR90 - 120 分钟,方法是将每个图像像素的时间-活动曲线外推至 120 分钟。使用 Passing-Bablock 分析评估了基于 120 分钟动态扫描的 VT2TCM、DVR2TCM、DVRLogan 和使用 SAT 的 SUVR 的重现性。使用Bland-Altman分析法确定了各项PET指标的可重复性极限:结果:与使用 120 分钟数据集测量的结果相比,40 分钟的动态 SAT 可得出所有脑区的 18F]MK-6240 VT2TCM。与 120 分钟全动态数据集相比,基于 SAT 的分析在 VT2TCM、DVR2TCM、DVRLogan 或 SUVR 方面未显示出明显的系统性或比例性偏差。在 VT2TCM、DVR2TCM、DVRLogan 和 SUVR 中,120 分钟分析与基于 SAT 的分析之间的平均差异分别小于 4%、10%、15% 和 20%:[18F]MK-6240 PET 的动力学建模可在短至 40 分钟的动态扫描时间内准确完成。这将有助于[18F]MK-6240 PET的研究,并提高患者的累积率。有必要开展进一步的工作,以确认这些结果对痴呆患者光谱的可重复性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
EJNMMI Physics
EJNMMI Physics Physics and Astronomy-Radiation
CiteScore
6.70
自引率
10.00%
发文量
78
审稿时长
13 weeks
期刊介绍: EJNMMI Physics is an international platform for scientists, users and adopters of nuclear medicine with a particular interest in physics matters. As a companion journal to the European Journal of Nuclear Medicine and Molecular Imaging, this journal has a multi-disciplinary approach and welcomes original materials and studies with a focus on applied physics and mathematics as well as imaging systems engineering and prototyping in nuclear medicine. This includes physics-driven approaches or algorithms supported by physics that foster early clinical adoption of nuclear medicine imaging and therapy.
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