1. Center for Advanced Medical Imaging Sciences, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA;
Theranostics. 2013 Oct 4;3(10):741-56. doi: 10.7150/thno.6815.
Quantitative measures of image quality and reliability are critical for both qualitative interpretation and quantitative analysis of medical images. While, in theory, it is possible to analyze reconstructed images by means of Monte Carlo simulations using a large number of noise realizations, the associated computational burden makes this approach impractical. Additionally, this approach is less meaningful in clinical scenarios, where multiple noise realizations are generally unavailable. The practical alternative is to compute closed-form analytical expressions for image quality measures. The objective of this paper is to review statistical analysis techniques that enable us to compute two key metrics: resolution (determined from the local impulse response) and covariance. The underlying methods include fixed-point approaches, which compute these metrics at a fixed point (the unique and stable solution) independent of the iterative algorithm employed, and iteration-based approaches, which yield results that are dependent on the algorithm, initialization, and number of iterations. We also explore extensions of some of these methods to a range of special contexts, including dynamic and motion-compensated image reconstruction. While most of the discussed techniques were developed for emission tomography, the general methods are extensible to other imaging modalities as well. In addition to enabling image characterization, these analysis techniques allow us to control and enhance imaging system performance. We review practical applications where performance improvement is achieved by applying these ideas to the contexts of both hardware (optimizing scanner design) and image reconstruction (designing regularization functions that produce uniform resolution or maximize task-specific figures of merit).
医学图像的质量和可靠性的定量评估对于定性解释和定量分析都至关重要。虽然从理论上讲，通过使用大量噪声实现来进行蒙特卡罗模拟，可以对重建图像进行分析，但这种方法的计算负担使其不切实际。此外，在临床情况下，由于通常无法获得多个噪声实现，因此这种方法的意义不大。实际的替代方法是计算图像质量度量的闭式解析表达式。本文的目的是回顾统计分析技术，这些技术使我们能够计算两个关键指标：分辨率（由局部脉冲响应确定）和协方差。基本方法包括定点方法，该方法在独立于所采用的迭代算法的固定点（唯一且稳定的解）处计算这些度量；以及基于迭代的方法，该方法的结果取决于算法、初始化和迭代次数。我们还探讨了这些方法中的一些方法在各种特殊情况下的扩展，包括动态和运动补偿图像重建。虽然讨论的大多数技术都是为发射断层扫描开发的，但一般方法也可以扩展到其他成像模式。除了实现图像特征描述外，这些分析技术还使我们能够控制和增强成像系统的性能。我们回顾了通过将这些思想应用于硬件（优化扫描仪设计）和图像重建（设计产生均匀分辨率或最大化特定任务的优劣指标的正则化函数）的上下文中，实现性能改进的实际应用。
