Department of Radiation Physics, the University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA; Department of Radiation Oncology, Proton Therapy Center, University of Cincinnati Medical Center, 7777 Yankee Road, Liberty Township, 45044, USA.
Department of Radiation Physics, the University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA.
Comput Med Imaging Graph. 2021 Mar;88:101849. doi: 10.1016/j.compmedimag.2020.101849. Epub 2020 Dec 29.
Intensity-based deformable registration with spatial-invariant regularization generally fails when distinct motion exists across different types of tissues. The purpose of this work was to develop and validate a new regularization approach for deformable image registration that is tissue-specific and able to handle motion discontinuities. Our approach was built upon a Demons registration framework, and used the image context supplementing the original spatial constraint to regularize displacement vector fields in iterative image registration process. The new regularization was implemented as a spatial-contextual filter, which favors the motion vectors within the same tissue type but penalizes the motion vectors from different tissues. This approach was validated using five public lung cancer patients, each with 300 landmark pairs identified by a thoracic radiation oncologist. The mean and standard deviation of the landmark registration errors were 1.3 ± 0.8 mm, compared with those of 2.3 ± 2.9 mm using the original Demons algorithm. Particularly, for the case with the largest initial landmark displacement of 15 ± 9 mm, the modified Demons algorithm had a registration error of 1.3 ± 1.1 mm, while the original Demons algorithm had a registration error of 3.6 ± 5.9 mm. We also qualitatively evaluated the modified Demons algorithm using two difficult cases in our routine clinic: one lung case with large sliding motion and one head and neck case with large anatomical changes in air cavity. Visual evaluation on the deformed image created by the deformable image registration showed that the modified Demons algorithm achieved reasonable registration accuracy, but the original Demons algorithm produced distinct registration errors.
基于强度的变形配准在存在不同组织类型之间的明显运动时通常会失败。本工作的目的是开发和验证一种新的变形图像配准正则化方法，该方法是组织特异性的，能够处理运动不连续性。我们的方法建立在 Demons 配准框架的基础上，利用图像上下文来补充原始空间约束，以在迭代图像配准过程中对位移矢量场进行正则化。新的正则化方法被实现为空间上下文滤波器，它有利于同一组织类型内的运动矢量，但惩罚来自不同组织的运动矢量。该方法使用五名公共肺癌患者进行了验证，每位患者都有 300 个由胸部放射肿瘤学家确定的标志点对。标志点配准误差的平均值和标准差为 1.3 ± 0.8 毫米，而使用原始 Demons 算法的误差为 2.3 ± 2.9 毫米。特别是，对于初始标志点位移最大为 15 ± 9 毫米的情况，改进的 Demons 算法的配准误差为 1.3 ± 1.1 毫米，而原始 Demons 算法的配准误差为 3.6 ± 5.9 毫米。我们还使用我们常规临床中的两个困难病例对改进的 Demons 算法进行了定性评估：一个肺病例有大的滑动运动，一个头颈部病例有空腔解剖结构的大变化。对变形图像配准生成的变形图像的视觉评估表明，改进的 Demons 算法实现了合理的配准精度，但原始 Demons 算法产生了明显的配准误差。
