Taubmann Oliver, Maier Andreas, Hornegger Joachim, Lauritsch Günter, Fahrig Rebecca
Pattern Recognition Lab, Computer Science Department, Friedrich-Alexander-University Erlangen-Nuremberg, 91058 Erlangen, Germany and Erlangen Graduate School in Advanced Optical Technologies (SAOT), 91052 Erlangen, Germany.
Siemens Healthcare GmbH, 91301 Forchheim, Germany.
Med Phys. 2016 Feb;43(2):883-93. doi: 10.1118/1.4939878.
Detailed analysis of cardiac motion would be helpful for supporting clinical workflow in the interventional suite. With an angiographic C-arm system, multiple heart phases can be reconstructed using electrocardiogram gating. However, the resulting angular undersampling is highly detrimental to the quality of the reconstructed images, especially in nonideal intraprocedural imaging conditions. Motion-compensated reconstruction has previously been shown to alleviate this problem, but it heavily relies on a preliminary reconstruction suitable for motion estimation. In this work, the authors propose a processing pipeline tailored to augment these initial images for the purpose of motion estimation and assess how it affects the final images after motion compensation.
The following combination of simple, direct methods inspired by the core ideas of existing approaches proved beneficial: (a) Streak reduction by masking high-intensity components in projection domain after filtering. (b) Streak reduction by subtraction of estimated artifact volumes in reconstruction domain. (c) Denoising in spatial domain using a joint bilateral filter guided by an uncompensated reconstruction. (d) Denoising in temporal domain using an adaptive Gaussian smoothing based on a novel motion detection scheme.
Experiments on a numerical heart phantom yield a reduction of the relative root-mean-square error from 89.9% to 3.6% and an increase of correlation with the ground truth from 95.763% to 99.995% for the motion-compensated reconstruction when the authors' processing is applied to the initial images. In three clinical patient data sets, the signal-to-noise ratio measured in an ideally homogeneous region is increased by 37.7% on average. Overall visual appearance is improved notably and some anatomical features are more readily discernible.
The authors' findings suggest that the proposed sequence of steps provides a clear advantage over an arbitrary sequence of individual image enhancement methods and is fit to overcome the issue of lacking image quality in motion-compensated C-arm imaging of the heart. As for future work, the obtained results pave the way for investigating how accurately cardiac functional motion parameters can be determined with this modality.
详细分析心脏运动有助于支持介入手术室的临床工作流程。使用血管造影C形臂系统，可以通过心电图门控重建多个心脏相位。然而，由此产生的角度欠采样对重建图像的质量非常不利，尤其是在非理想的术中成像条件下。运动补偿重建先前已被证明可以缓解此问题，但它严重依赖于适合运动估计的初步重建。在这项工作中，作者提出了一种处理流程，专门用于增强这些初始图像以进行运动估计，并评估其对运动补偿后最终图像的影响。
受现有方法核心思想启发的以下简单、直接方法的组合被证明是有益的：(a) 在滤波后通过在投影域中屏蔽高强度分量来减少条纹。(b) 通过在重建域中减去估计的伪影体积来减少条纹。(c) 使用基于未补偿重建引导的联合双边滤波器在空间域中去噪。(d) 使用基于新型运动检测方案的自适应高斯平滑在时间域中去噪。
当作者的处理应用于初始图像时，对数字心脏模型的实验表明，运动补偿重建的相对均方根误差从89.9%降低到3.6%，与真实情况的相关性从95.763%提高到99.995%。在三个临床患者数据集中，在理想均匀区域测量的信噪比平均提高了37.7%。整体视觉外观有显著改善，一些解剖特征更容易辨别。
作者的研究结果表明，所提出的步骤序列比单个图像增强方法的任意序列具有明显优势，并且适合克服心脏运动补偿C形臂成像中图像质量不足的问题。至于未来的工作，所获得的结果为研究如何用这种方式准确确定心脏功能运动参数铺平了道路。
