Department of Engineering Science, University of Oxford, Oxford, UK.
Med Phys. 2013 Feb;40(2):021903. doi: 10.1118/1.4771682.
Current clinical practice for lung cancer diagnosis and staging requires the acquisition of a diagnostic computed tomography (CT) as well as positron emission tomography (PET)/CT volumes from a hybrid scanner, where the CT is used for attenuation correction (AC-CT). The PET and AC-CT images are implicitly aligned, however, image registration between the diagnostic CT and PET volumes is needed to relate the anatomical correspondences. This is an important but difficult task due to the absence of a direct or functional relationship between the intensities. Alternatively, here we propose the diagnostic CT can be aligned with the PET image through an indirect registration process that uses the AC-CT. The resultant deformation field can then be used to align the PET image to the diagnostic CT. The registration of the diagnostic CT to AC-CT registration still presents two major challenges: (a) it is a multimodal registration problem since the diagnostic CT is acquired after the injection of a contrast agent, and (b) the type and amplitude of the deformations require a registration process that includes physically motivated properties to achieve an accurate and physiologically plausible alignment.
The authors propose a new framework based on fluid registration including three physiologically motivated properties: (i) sliding motion of the lungs against the pleura; (ii) preservation of rigid structures; (iii) preservation of topology. The sliding motion is modeled using direction dependent regularization that decouples the tangential and the normal components of the external force term. The rigid shape of the bones is preserved using a spatially varying filter for the deformations. Finally, the topology is maintained using the concept of log-unbiased deformations. To solve the multimodal problem, the authors use local cross correlation (LCC) as the similarity measure.
The proposed framework is first evaluated on CT lung image pairs representing several phases of the respiratory cycle. The authors show that their proposed framework has a superior performance compared to the classic fluid registration, both in quantitative and qualitative terms. The authors then evaluate the framework using ten real patient scans, where the authors also demonstrate how their physiologically motivated registration framework can be successfully applied to the task of fusing diagnostic CT with the PET/CT image volumes.
The proposed registration framework has better results for the fusion of diagnostic CT with PET images in comparison to the classic fluid registration framework.
当前肺癌诊断和分期的临床实践需要获取诊断用计算机断层扫描（CT）以及来自混合扫描仪的正电子发射断层扫描（PET）/CT 体数据，其中 CT 用于衰减校正（AC-CT）。虽然 PET 和 AC-CT 图像是隐式对齐的，但需要对诊断 CT 和 PET 体数据之间进行图像配准，以确定解剖对应关系。由于强度之间没有直接或功能关系，因此这是一项重要但困难的任务。或者，这里我们提出可以通过使用 AC-CT 的间接配准过程将诊断 CT 与 PET 图像对齐。然后，可以使用所得变形场将 PET 图像与诊断 CT 对齐。将诊断 CT 与 AC-CT 配准仍然存在两个主要挑战：（a）这是一个多模态配准问题，因为诊断 CT 是在注射造影剂之后采集的；（b）变形的类型和幅度需要一个包含物理驱动属性的配准过程，以实现准确且符合生理的配准。
作者提出了一种基于流体制剂的新框架，该框架包含三个生理驱动属性：（i）肺相对于胸膜的滑动运动；（ii）刚性结构的保留；（iii）拓扑的保留。滑动运动使用依赖于方向的正则化进行建模，该正则化方法将外力项的切向和法向分量解耦。骨骼的刚性形状使用变形的空间变化滤波器来保留。最后，使用无偏对数变形的概念来维持拓扑结构。为了解决多模态问题，作者使用局部互相关（LCC）作为相似性度量。
该框架首先在代表呼吸周期几个阶段的 CT 肺图像对上进行评估。作者表明，与经典流体制剂相比，他们提出的框架在定量和定性方面都具有更好的性能。作者然后使用十次真实患者扫描评估了该框架，并演示了他们的生理驱动配准框架如何成功应用于将诊断 CT 与 PET/CT 图像体数据融合的任务。
与经典流体制剂框架相比，所提出的配准框架在将诊断 CT 与 PET 图像融合方面具有更好的结果。
