Nonlinear Statistical Reconstruction for Flat-Panel Cone-Beam CT with Blur and Correlated Noise Models.
Nonlinear Statistical Reconstruction for Flat-Panel Cone-Beam CT with Blur and Correlated Noise Models.
复制标题
具有模糊和相关噪声模型的平板锥束 CT 非线性统计重建。
DOI：
10.1117/12.2216126
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Stayman,JWebster
中科院分区：
文献类型：
--
作者：
Tilley,Steven;Siewerdsen,JeffreyH;Zbijewski,Wojciech;Stayman,JWebster
平板锥束CT(FP-CBCT)是一种很有前途的成像方式，部分原因是它在相对紧凑的扫描仪中具有高空间分辨率重建的潜力。尽管有这种潜力，FP-CBCT在分辨重要的细小尺度结构(例如，专用肢体扫描仪中的小梁细节和专用CBCT乳房X光检查中的微钙化)方面仍存在困难。基于模型的方法提供了一个在不更改任何硬件的情况下提高高分辨率性能的机会。以前基于线性化前向模型的工作表明，当同时对FP-CBCT系统的系统模糊和空间相关性特征进行建模时，性能有所改善。遗憾的是，线性化模型依赖于阶段性处理方法，该方法使调谐参数选择复杂化，并且可能限制最好的可实现的空间分辨率。在这项工作中，我们提出了一种替代方案，该方案利用了具有系统模糊和空间相关噪声的完全非线性正演模型。从该正向模型导出了一个基于似然的目标函数，并给出了求解该目标函数的迭代优化算法。使用数字肢体模型对所提出的方法进行了仿真研究，并对分辨率和噪声之间的权衡进行了定量评估。相关非线性模型的性能优于非相关非线性模型和分段线性化技术，在匹配的空间分辨率下，方差降低高达86%。此外，与线性相关模型(0.15 mm)和传统FDK模型(0.40 mm)相比，非线性模型可以获得更好的空间分辨率(相关：0.10 mm，非相关：0.11 mm)。这表明所提出的非线性方法可能是提高高分辨率临床应用的性能的重要工具。
Flat-panel cone-beam CT (FP-CBCT) is a promising imaging modality, partly due to its potential for high spatial resolution reconstructions in relatively compact scanners. Despite this potential, FP-CBCT can face difficulty resolving important fine scale structures (e.g, trabecular details in dedicated extremities scanners and microcalcifications in dedicated CBCT mammography). Model-based methods offer one opportunity to improve high-resolution performance without any hardware changes. Previous work, based on a linearized forward model, demonstrated improved performance when both system blur and spatial correlations characteristics of FP-CBCT systems are modeled. Unfortunately, the linearized model relies on a staged processing approach that complicates tuning parameter selection and can limit the finest achievable spatial resolution. In this work, we present an alternative scheme that leverages a full nonlinear forward model with both system blur and spatially correlated noise. A likelihood-based objective function is derived from this forward model and we derive an iterative optimization algorithm for its solution. The proposed approach is evaluated in simulation studies using a digital extremities phantom and resolution-noise trade-offs are quantitatively evaluated. The correlated nonlinear model outperformed both the uncorrelated nonlinear model and the staged linearized technique with up to a 86% reduction in variance at matched spatial resolution. Additionally, the nonlinear models could achieve finer spatial resolution (correlated: 0.10 mm, uncorrelated: 0.11 mm) than the linear correlated model (0.15 mm), and traditional FDK (0.40 mm). This suggests the proposed nonlinear approach may be an important tool in improving performance for high-resolution clinical applications.