动态PET聚类因子分解的物理幻像验证。

IF 3.2 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Medical physics Pub Date : 2025-05-31 DOI:10.1002/mp.17902
Valerie Kobzarenko, Suzanne L. Baker, Mustafa Janabi, Woon-Seng Choong, Grant T. Gullberg, Youngho Seo, Rostyslav Boutchko, Debasis Mitra
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引用次数: 0

摘要

背景:动态正电子发射断层扫描(PET)能够量化用于神经精神疾病调查的放射性示踪剂的生理参数。我们之前介绍了一种基于因子分析的算法,集群初始化因子分析(CIFA),旨在克服指定参考区域的问题。CIFA能够根据示踪剂动力学的差异自动提取多种模式下不同的放射性示踪剂结合分布,因此可以区分特异性和非特异性结合区域,而无需事先分割。目的:我们的目标是通过将算法的输出与独立基准进行比较,定量验证CIFA解决不同动态生物过程的能力。作为中间目标,我们的目标是创建一个能够模拟动态成像独特方面的物理幻影,并将该幻影作为评估CIFA的基准。方法:利用CIFA对劳伦斯伯克利国家实验室获得的18F-flortaucipir动态脑PET数据集进行重构。所得到的因子曲线是在一个专门为此目的构建的物理脑幻影中创建动态输入-时间-活动曲线(TAC)组合的基础。幻影代表三个组成部分:两个重叠的组织类型和自由放射性示踪剂,由小型液压元件组合而成。物理组件被单独扫描以生成图像库,使我们能够以规定的动态和真实的部分体积效果再现任何持续时间的扫描。幻影设计用于产生噪声实例,其中动态扫描与期望的活动tac混合,用于自由,非特异性结合和特异性结合放射性示踪剂。用CIFA估计了10种不同TAC相似度的不同动态模拟。结果:我们通过计算估计输出与地面真实组织tac和相应组织分布之间的Pearson相关系数,直接评估了CIFA在分析10个动态数据集中的性能。10个模型动力学中有7个捕获了实际预期组织TAC形状的全谱,特定结合组织的曲线相关性超过95%。结论:本工作制定了一个创新的过程,将物理幻像设计与PET图像相结合,以评估CIFA在从动态PET图像数据中提取动态tac中的应用。在大多数情况下,CIFA算法准确地再现了仿真数据的动态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Physical phantom validation of clustering-initiated factorization in dynamic PET

Background

Dynamic positron emission tomography (PET) enables the quantification of physiological parameters of radiotracers employed in the investigation of neuropsychiatric disorders. We previously introduced a factor analysis-based algorithm, Cluster-Initialized Factor Analysis (CIFA), designed to overcome the problem of specifying reference regions. CIFA is capable of automatically extracting distinct radiotracer binding distributions across many modalities based on the differences in tracer dynamics, and thus can distinguish regions of specific- and non-specific binding without requiring prior segmentation.

Purpose

Our goal is to quantitatively validate the ability of CIFA to resolve different dynamic biological processes by comparing the output of the algorithm to an independent benchmark. As an intermediate goal, we aim to create a physical phantom capable of modeling unique aspects of dynamic imaging and to use this phantom as the benchmark in evaluating CIFA.

Methods

CIFA was used to reconstruct 18F-flortaucipir dynamic brain PET datasets acquired at Lawrence Berkeley National Lab. The resulting factor curves served as the foundation for creating dynamic input time-activity curve (TAC) combinations in a physical brain phantom specifically constructed for this purpose. The phantom represented three components: two overlapping tissue types and free radiotracer, constructed with a combination of small hydraulic elements. The physical components were scanned separately to generate a library of images, allowing us to reproduce scans of any duration with prescribed dynamics and realistic partial volume effects. The phantom was designed to produce noisy instances with compartment mixing of dynamic scans with desired activity TACs for free, non-specifically bound, and specifically bound radiotracers. Ten distinct dynamic simulations with varying levels of TAC similarity were estimated with CIFA.

Results

We directly evaluated CIFA's performance in analyzing each of the 10 dynamic datasets by computing the Pearson correlation coefficient between the estimated outputs and the ground truth tissue TACs and corresponding tissue distributions. For seven out of 10 modeled dynamics, which captured the full spectrum of realistically expected tissue TAC shapes, the curve correlation of the specific binding tissue was above 95%.

Conclusions

This work formulated an innovative process by combining a physical phantom design with PET images for evaluating the application of CIFA in the extraction of dynamic TACs from dynamic PET image data. In most cases the CIFA algorithm accurately reproduced the dynamics of the phantom simulated data.

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来源期刊
Medical physics
Medical physics 医学-核医学
CiteScore
6.80
自引率
15.80%
发文量
660
审稿时长
1.7 months
期刊介绍: Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.
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