Dang Hao, Siewerdsen Jeffrey H, Stayman J Webster
Departments of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA 21205.
Proc SPIE Int Soc Opt Eng. 2014 Feb 15;9033. doi: 10.1117/12.2043781.
Nearly all reconstruction methods are controlled through various parameter selections. Traditionally, such parameters are used to specify a particular noise and resolution trade-off in the reconstructed image volumes. The introduction of reconstruction methods that incorporate prior image information has demonstrated dramatic improvements in dose utilization and image quality, but has complicated the selection of reconstruction parameters including those associated with balancing information used from prior images with that of the measurement data. While a noise-resolution tradeoff still exists, other potentially detrimental effects are possible with poor prior image parameter values including the possible introduction of false features and the failure to incorporate sufficient prior information to gain any improvements. Traditional parameter selection methods such as heuristics based on similar imaging scenarios are subject to error and suboptimal solutions while exhaustive searches can involve a large number of time-consuming iterative reconstructions. We propose a novel approach that prospectively determines optimal prior image regularization strength to accurately admit specific anatomical changes without performing full iterative reconstructions. This approach leverages analytical approximations to the implicitly defined prior image-based reconstruction solution and predictive metrics used to estimate imaging performance. The proposed method is investigated in phantom experiments and the shift-variance and data-dependence of optimal prior strength is explored. Optimal regularization based on the predictive approach is shown to agree well with traditional exhaustive reconstruction searches, while yielding substantial reductions in computation time. This suggests great potential of the proposed methodology in allowing for prospective patient-, data-, and change-specific customization of prior-image penalty strength to ensure accurate reconstruction of specific anatomical changes.
几乎所有的重建方法都是通过各种参数选择来控制的。传统上，这些参数用于在重建图像体积中指定特定的噪声与分辨率权衡。引入包含先验图像信息的重建方法已在剂量利用和图像质量方面取得了显著改进，但却使重建参数的选择变得复杂，包括那些与平衡来自先验图像的信息和测量数据的信息相关的参数。虽然噪声与分辨率的权衡仍然存在，但如果先验图像参数值不佳，可能会产生其他潜在的有害影响，包括可能引入虚假特征以及未能纳入足够的先验信息以获得任何改进。传统的参数选择方法，如基于相似成像场景的启发式方法，容易出现误差和次优解，而穷举搜索可能涉及大量耗时的迭代重建。我们提出了一种新颖的方法，该方法前瞻性地确定最佳先验图像正则化强度，以准确地适应特定的解剖变化，而无需执行完整的迭代重建。这种方法利用对基于先验图像的隐含定义重建解的解析近似以及用于估计成像性能的预测指标。在体模实验中对所提出的方法进行了研究，并探讨了最佳先验强度的移位方差和数据依赖性。基于预测方法的最佳正则化显示与传统的穷举重建搜索结果非常吻合，同时显著减少了计算时间。这表明所提出的方法具有巨大潜力，可实现对先验图像惩罚强度进行前瞻性的患者、数据和变化特定的定制，以确保对特定解剖变化进行准确重建。
