Wang W, Gang G J, Siewerdsen J H, Stayman J W
Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, USA 21205.
Proc SPIE Int Soc Opt Eng. 2018 Feb;10573. doi: 10.1117/12.2294546. Epub 2018 Mar 9.
Model based iterative reconstruction (MBIR) algorithms such as penalized-likelihood (PL) methods have data-dependent and shift-variant image properties. Predictors of local reconstructed noise and resolution have found application in a number of methods that seek to understand, control, and optimize CT data acquisition and reconstruction parameters in a prospective fashion (as opposed to studies based on exhaustive evaluation). However, previous MBIR prediction methods have relied on idealized system models. In this work, we develop and validate new predictors using accurate physical models specific to flat-panel CT systems.
Novel predictors for estimation of local spatial resolution and noise properties are developed for PL reconstruction that include a physical model for blur and correlated noise in flat-panel cone-beam CT (CBCT) acquisitions. Prospective predictions (e.g., without reconstruction) of local point spread function and and local noise power spectrum (NPS) model are applied, compared, and validated using a flat-panel CBCT test bench.
Comparisons between prediction and physical measurements show excellent agreement for both spatial resolution and noise properties. In comparison, traditional prediction methods (that ignore blur/correlation found in flat-panel data) fail to capture important data characteristics and show significant mismatch.
Novel image property predictors permit prospective assessment of flat-panel CBCT using MBIR. Such predictors enable standard and task-based performance assessments, and are well-suited to evaluation, control, and optimization of the CT imaging chain (e.g., x-ray technique, reconstruction parameters, novel data acquisition methods, etc.) for improved imaging performance and/or dose utilization.
基于模型的迭代重建（MBIR）算法，如惩罚似然（PL）方法，具有数据依赖性和图像属性的平移变化性。局部重建噪声和分辨率的预测器已在许多旨在以前瞻性方式理解、控制和优化CT数据采集与重建参数的方法中得到应用（与基于详尽评估的研究相反）。然而，先前的MBIR预测方法依赖于理想化的系统模型。在本研究中，我们利用针对平板CT系统的精确物理模型开发并验证新的预测器。
针对PL重建开发了用于估计局部空间分辨率和噪声属性的新型预测器，其中包括平板锥束CT（CBCT）采集中模糊和相关噪声的物理模型。使用平板CBCT测试平台对局部点扩散函数和局部噪声功率谱（NPS）模型进行前瞻性预测（例如，无需重建）、比较和验证。
预测结果与物理测量结果在空间分辨率和噪声属性方面均显示出极佳的一致性。相比之下，传统预测方法（忽略平板数据中的模糊/相关性）无法捕捉重要的数据特征，且存在显著不匹配。
新型图像属性预测器允许使用MBIR对平板CBCT进行前瞻性评估。此类预测器能够进行标准和基于任务的性能评估，非常适合于评估、控制和优化CT成像链（例如，X射线技术、重建参数、新型数据采集方法等），以提高成像性能和/或剂量利用率。
