German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. Medical Faculty, Ruprecht-Karls-University, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany.
Phys Med Biol. 2018 Feb 2;63(3):035032. doi: 10.1088/1361-6560/aaa16d.
We propose a phase-to-amplitude resampling (PTAR) method to reduce motion blurring in motion-compensated (MoCo) 4D cone-beam CT (CBCT) image reconstruction, without increasing the computational complexity of the motion vector field (MVF) estimation approach. PTAR is able to improve the image quality in reconstructed 4D volumes, including both regular and irregular respiration patterns. The PTAR approach starts with a robust phase-gating procedure for the initial MVF estimation and then switches to a phase-adapted amplitude gating method. The switch implies an MVF-resampling, which makes them amplitude-specific. PTAR ensures that the MVFs, which have been estimated on phase-gated reconstructions, are still valid for all amplitude-gated reconstructions. To validate the method, we use an artificially deformed clinical CT scan with a realistic breathing pattern and several patient data sets acquired with a TrueBeam integrated imaging system (Varian Medical Systems, Palo Alto, CA, USA). Motion blurring, which still occurs around the area of the diaphragm or at small vessels above the diaphragm in artifact-specific cyclic motion compensation (acMoCo) images based on phase-gating, is significantly reduced by PTAR. Also, small lung structures appear sharper in the images. This is demonstrated both for simulated and real patient data. A quantification of the sharpness of the diaphragm confirms these findings. PTAR improves the image quality of 4D MoCo reconstructions compared to conventional phase-gated MoCo images, in particular for irregular breathing patterns. Thus, PTAR increases the robustness of MoCo reconstructions for CBCT. Because PTAR does not require any additional steps for the MVF estimation, it is computationally efficient. Our method is not restricted to CBCT but could rather be applied to other image modalities.
我们提出了一种相位到幅度重采样(PTAR)方法,用于减少运动补偿(MoCo)4D 锥形束 CT(CBCT)图像重建中的运动模糊,而不会增加运动矢量场(MVF)估计方法的计算复杂度。PTAR 能够提高重建 4D 容积中的图像质量,包括规则和不规则呼吸模式。PTAR 方法从初始 MVF 估计的稳健相位门控过程开始,然后切换到相位自适应幅度门控方法。这种切换意味着 MVF 重采样,使其与幅度相关。PTAR 确保在相门控重建上估计的 MVFs 仍然适用于所有幅度门控重建。为了验证该方法,我们使用具有真实呼吸模式的人工变形临床 CT 扫描和使用 TrueBeam 集成成像系统(美国加利福尼亚州帕洛阿尔托的 Varian Medical Systems)获得的几个患者数据集。在基于相位门控的artifact-specific cyclic motion compensation (acMoCo) 图像中,仍然在膈膜区域周围或膈膜上方的小血管处发生运动模糊,通过 PTAR 显著减少。此外,图像中的小肺结构更加清晰。这在模拟和真实患者数据中都得到了证明。膈膜清晰度的量化证实了这些发现。与传统的基于相位门控的 MoCo 图像相比,PTAR 可提高 4D MoCo 重建的图像质量,特别是对于不规则的呼吸模式。因此,PTAR 提高了 MoCo 重建对 CBCT 的鲁棒性。由于 PTAR 不需要为 MVF 估计添加任何其他步骤,因此它具有计算效率。我们的方法不仅限于 CBCT,而是可以应用于其他图像模态。