Zhang Hao, Gang Grace J, Dang Hao, Sussman Marc S, Lin Cheng Ting, Siewerdsen Jeffrey H, Stayman J Webster
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
Department of Surgery, Johns Hopkins University, Baltimore, MD 21205, USA.
Proc SPIE Int Soc Opt Eng. 2018 Mar;10573. doi: 10.1117/12.2293135.
Prior-image-based reconstruction (PIBR) is a powerful tool for low-dose CT, however, the nonlinear behavior of such approaches are generally difficult to predict and control. Similarly, traditional image quality metrics do not capture potential biases exhibited in PIBR images. In this work, we identify a new bias metric and construct an analytical framework for prospectively predicting and controlling the relationship between prior image regularization strength and this bias in a reliable and quantitative fashion.
Bias associated with prior image regularization in PIBR can be described as the fraction of actual contrast change (between the prior image and current anatomy) that appears in the reconstruction. Using local approximation of the nonlinear PIBR objective, we develop an analytical relationship between local regularization, fractional contrast reconstructed, and true contrast change. This analytic tool allows prediction bias properties in a reconstructed PIBR image and includes the dependencies on the data acquisition, patient anatomy and change, and reconstruction parameters. Predictions are leveraged to provide reliable and repeatable image properties for varying data fidelity in simulation and physical cadaver experiments.
The proposed analytical approach permits accurate prediction of reconstructed contrast relative to a gold standard based on exhaustive search based on numerous iterative reconstructions. The framework is used to control regularization parameters to enforce consistent change reconstructions over varying fluence levels and varying numbers of projection angles - enabling bias properties that are less location- and acquisition-dependent.
While PIBR methods have demonstrated a substantial ability for dose reduction, image properties associated with those images have been difficult to express and quantify using traditional metrics. The novel framework presented in this work not only quantifies this bias in an intuitive fashion, but it gives a way to predict and control the bias. Reliable and predictable reconstruction methods are a requirement for clinical imaging systems and the proposed framework is an important step translating PIBR methods to clinical application.
基于先验图像的重建（PIBR）是低剂量CT的一种强大工具，然而，此类方法的非线性行为通常难以预测和控制。同样，传统的图像质量指标无法捕捉PIBR图像中表现出的潜在偏差。在这项工作中，我们确定了一种新的偏差指标，并构建了一个分析框架，以前瞻性地、可靠且定量地预测和控制先验图像正则化强度与这种偏差之间的关系。
PIBR中与先验图像正则化相关的偏差可描述为出现在重建中的实际对比度变化（在先验图像和当前解剖结构之间）的比例。利用非线性PIBR目标的局部近似，我们建立了局部正则化、重建的分数对比度和真实对比度变化之间的分析关系。这种分析工具允许预测重建的PIBR图像中的偏差特性，并包括对数据采集、患者解剖结构和变化以及重建参数的依赖性。利用这些预测为模拟和物理尸体实验中不同数据保真度提供可靠且可重复的图像特性。
所提出的分析方法允许基于大量迭代重建的穷举搜索，相对于金标准准确预测重建对比度。该框架用于控制正则化参数，以在不同的注量水平和不同的投影角度数量下强制进行一致的变化重建——实现对位置和采集依赖性较小的偏差特性。
虽然PIBR方法已显示出显著的剂量降低能力，但与这些图像相关的图像特性一直难以用传统指标来表达和量化。这项工作中提出的新颖框架不仅以直观的方式量化了这种偏差，而且还提供了一种预测和控制偏差的方法。可靠且可预测的重建方法是临床成像系统的要求，所提出的框架是将PIBR方法转化为临床应用的重要一步。
