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首页> 外文学位 >Task-Based Imaging Performance in 3D X-Ray Tomography: Noise, Detectability, and Implications for System Design.
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Task-Based Imaging Performance in 3D X-Ray Tomography: Noise, Detectability, and Implications for System Design.

机译：3D X射线断层扫描中基于任务的成像性能：噪声，可检测性及其对系统设计的影响。

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摘要

Quantifying imaging performance is an important aspect in the development, optimization, and assessment of medical imaging systems. This thesis addresses new challenges in the characterization of imaging performance for advanced x-ray tomographic imaging technologies. Central to the work is a task-based cascaded systems analysis framework that encompasses aspects of system geometry, x-ray beam characteristics, dose, detector design, background anatomy, model observers, and the imaging task. The metrology throughout includes Fourier domain descriptors of spatial resolution (modulation transfer function, MTF), noise (noise-power spectrum, NPS), noise-equivalent quanta (NEQ), and task-based detectability. Central elements and advances of the work include: a task-based model for 3D imaging performance in tomosynthesis and cone-beam CT (CBCT); generalization of imaging performance metrics to include the influence of anatomical background clutter; validation of the model in comparison to human observer performance; extension to dual-energy (DE) tomographic imaging; analysis of non-stationary (i.e., spatially varying) signal and noise characteristics; and extension to model-based statistical image reconstruction. In each case, the analytical framework demonstrates the importance of task-based assessment and the capability for system optimization in a fairly broad scope of clinical applications ranging from breast to abdominal and musculoskeletal imaging. The validity of the framework in describing "local" signal and noise characteristics is demonstrated under conditions of strong nonstationarity, ranging from simple phantoms to complex anthropomorphic scenes. In addition to providing a framework for system design and optimization, the analysis opens potential new opportunities in task-based imaging and statistical reconstruction, with examples demonstrated in the design of optimal regularization in iterative reconstruction.
机译：量化成像性能是医学成像系统开发，优化和评估的重要方面。本论文解决了高级X射线断层扫描成像技术在表征成像性能方面的新挑战。这项工作的核心是一个基于任务的级联系统分析框架，其中包括系统几何，X射线束特性，剂量，检测器设计，背景解剖结构，模型观察者和成像任务等方面。整个度量包括空间分辨率（调制传递函数，MTF），噪声（噪声功率谱，NPS），噪声等效量（NEQ）和基于任务的可检测性的傅立叶域描述符。这项工作的核心要素和进展包括：基于任务的断层合成和锥束CT（CBCT）3D成像性能模型；成像性能指标的一般化，以包括解剖背景杂波的影响；与人类观察员的表现相比，对模型的验证；扩展到双能断层成像；分析非平稳（即空间变化）的信号和噪声特征;并扩展到基于模型的统计图像重建。在每种情况下，分析框架都证明了在从乳房到腹部和肌肉骨骼成像的相当广泛的临床应用中，基于任务的评估的重要性和系统优化的能力。在强烈的非平稳性条件下，从简单的幻像到复杂的拟人化场景，证明了框架在描述“本地”信号和噪声特征方面的有效性。除了为系统设计和优化提供框架外，该分析还为基于任务的成像和统计重建提供了潜在的新机会，并在迭代重建的最佳正则化设计中演示了示例。

著录项

  • 作者

    Gang, Jianan.;

  • 作者单位

    University of Toronto (Canada).;

  • 授予单位 University of Toronto (Canada).;
  • 学科 Biomedical engineering.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 239 p.
  • 总页数 239
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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