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4D Dynamic Image Reconstruction for Cardiac SPECT.

机译：心脏SPECT的4D动态图像重建。

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摘要

Imaging methods in nuclear medicine such as Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) attempt to grasp the functionality of imaged organs. Static SPECT aims to reconstruct 3D representation of the tracer's concentrations inside the imaged subject, while dynamic single photon emission computed tomography (dynamic SPECT) aims to estimate the 4D concentration distribution of a gamma-ray emitting tracer inside the patient as it changes with time. The temporal variation of the concentration function, represented by time activity curves (TACs), provides important information about radiotracer pharmacokinetics and about physiology of tissues and organs. Dynamic SPECT is the main focus of this research. Several issues are studied and addressed by this dissertation including the development of a dynamic reconstruction algorithm, a procedure for initializing the reconstruction algorithm, and a method for estimating the proper value of the regularization parameter.;First, we have developed and validated an algorithm for extracting voxel-by-voxel time activity curves directly from inconsistent projections applied in dynamic cardiac SPECT. The algorithm derived was based on the factor analysis of dynamic structures (FADS) approach and imposes prior information by applying several regularization functions. The anatomical information of the imaged subject is used to apply the proposed regularization functions adaptively in the spatial domain. The algorithm performance is validated by reconstructing dynamic simulated datasets using the NCAT phantom with a range of different input tissue time-activity dependent curves. The validated algorithm is then applied to reconstruct pre-clinical cardiac SPECT data from canine, murine, and human subjects.;Second, since dynamic SPECT is an underdetermined problem, initialization is important for the algorithm to converge to the right solution. Therefore, we have developed a procedure for initializing the algorithm using an initial reconstruction with a set of b-splines. In the reconstruction, we extract a set of initial time activity curves and use them for initialization.;During the dynamic reconstruction, the algorithm imposes several regularization functions. The weights (also called regularization parameters or hyper-parameters) of those functions are estimated dynamically from the data by adapting a method that optimizes for those parameters and updates them at every iteration.;With these improvements, we have successfully validated our method using numerically simulated data, and then applied it to three different types of real SPECT data acquired from slow gantry rotation. The results show that our developed algorithm is robust and has stable results.
机译：核医学中的成像方法，例如单光子发射计算机断层扫描（SPECT）和正电子发射断层扫描（PET），试图掌握成像器官的功能。静态SPECT的目的是重建成像对象内部示踪剂浓度的3D表示，而动态单光子发射计算机断层扫描（dynamic SPECT）则是随着时间的变化估计患者体内伽马射线发射示踪剂的4D浓度分布。时间活动曲线（TAC）表示浓度函数的时间变化，它提供了有关放射性示踪剂药代动力学以及组织和器官生理的重要信息。动态SPECT是这项研究的主要重点。本文研究并解决了几个问题，包括动态重建算法的开发，重建算法的初始化过程以及估计正则化参数的适当值的方法。直接从动态心脏SPECT中应用的不一致投影中提取逐个体素的时间活动曲线。得出的算法基于动态结构的因子分析（FADS）方法，并通过应用几个正则化函数施加先验信息。成像对象的解剖信息用于在空间域中自适应地应用建议的正则化函数。通过使用具有一系列不同输入组织时间-活动相关曲线的NCAT幻像重建动态模拟数据集，可以验证算法的性能。然后将经过验证的算法应用于从犬，鼠和人类受试者中重建临床前心脏SPECT数据。其次，由于动态SPECT是一个不确定的问题，因此初始化对于算法收敛到正确的解决方案很重要。因此，我们开发了一种使用带有一组b样条的初始重构来初始化算法的过程。在重建过程中，我们提取了一组初始时间活动曲线并将其用于初始化。在动态重建过程中，该算法强加了几个正则化函数。通过采用针对这些参数进行优化并在每次迭代中更新它们的方法，可以动态地从数据中估算出这些函数的权重（也称为正则化参数或超参数）。通过这些改进，我们已经成功地通过数值验证了我们的方法模拟数据，然后将其应用于从慢速龙门架旋转获取的三种不同类型的实际SPECT数据。结果表明，我们开发的算法是鲁棒的并且具有稳定的结果。

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  • 作者

    Abdalah, Mahmoud.;

  • 作者单位

    Florida Institute of Technology.;

  • 授予单位 Florida Institute of Technology.;
  • 学科 Computer science.;Radiology.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 136 p.
  • 总页数 136
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 农学（农艺学）;
  • 关键词

  • 入库时间 2022-08-17 11:53:33

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