Cai Meng, Byrne Mikel, Archibald-Heeren Ben, Metcalfe Peter, Rosenfeld Anatoly, Wang Yang
Icon Cancer Centre, Wahroonga, Australia.
Centre of Medical and Radiation Physics, University of Wollongong, Wollongong, Australia.
Phys Eng Sci Med. 2020 Dec;43(4):1161-1170. doi: 10.1007/s13246-020-00918-8. Epub 2020 Aug 19.
Cone-beam computed tomography (CBCT) is an important imaging modality for image-guided radiotherapy and adaptive radiotherapy. Feldkamp-Davis-Kress (FDK) method is widely adopted in clinical CBCT reconstructions due to its fast and robust application. While iterative algorithms have been shown to outperform FDK techniques in reducing noise and imaging dose, they are unable to correct projection-domain artefacts such as beam hardening and scatter. Empirical correction techniques require a holistic approach as beam hardening and scatter coexist in the measurement data. This multi-part proof of concept study conducted in MATLAB presents a novel approach to artefact reduction for CBCT image reconstruction. Firstly, we decoupled the beam hardening and scatter contributions originating from the imaging object and the bowtie filter. Next, a model was constructed to apply pixel-wise corrections to separately account for artefacts induced by the imaging object and the bowtie filter, in order to produce mono-energetic equivalent and scatter-compensated projections. Finally, the effectiveness of the correction model was tested on an offset phantom scan as well as a clinical brain scan. A conjugate-gradient least-squares algorithm was implemented over five iterations using FDK result as the initial input. Our proposed correction model was shown to effectively reduce cupping and shading artefacts in both phantom and clinical studies. This simple yet effective correction model could be readily implemented by physicists seeking to explore the benefits of iterative reconstruction.
锥形束计算机断层扫描(CBCT)是图像引导放疗和自适应放疗的一种重要成像方式。由于其应用快速且稳健,费尔德坎普-戴维斯-克雷斯(FDK)方法在临床CBCT重建中被广泛采用。虽然迭代算法在降低噪声和成像剂量方面已被证明优于FDK技术,但它们无法校正投影域伪影,如束硬化和散射。由于测量数据中束硬化和散射同时存在,经验校正技术需要一种整体方法。这项在MATLAB中进行的多部分概念验证研究提出了一种用于CBCT图像重建的新型伪影减少方法。首先,我们将源自成像对象和蝴蝶结滤波器的束硬化和散射贡献解耦。接下来,构建一个模型以逐像素应用校正,分别考虑由成像对象和蝴蝶结滤波器引起的伪影,以便生成单能等效和散射补偿投影。最后,在校准模体扫描以及临床脑部扫描上测试校正模型的有效性。使用FDK结果作为初始输入,通过共轭梯度最小二乘算法进行五次迭代。我们提出的校正模型在模体和临床研究中均显示能有效减少杯状和阴影伪影。这种简单而有效的校正模型可供寻求探索迭代重建益处的物理学家轻松实现。