Papież Bartłomiej W, Franklin James M, Heinrich Mattias P, Gleeson Fergus V, Brady Michael, Schnabel Julia A
University of Oxford, Institute of Biomedical Engineering, Department of Engineering Science, Oxford, United Kingdom.
University of Oxford, Department of Oncology, Oxford, United Kingdom.
J Med Imaging (Bellingham). 2018 Apr;5(2):024001. doi: 10.1117/1.JMI.5.2.024001. Epub 2018 Apr 4.
Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard approaches, such as Demons registration, mostly use Gaussian regularization for organ motion, which, though computationally efficient, rule out their application to intrinsically more complex organ motions, such as sliding interfaces. We propose regularization of motion based on supervoxels, which provides an integrated discontinuity preserving prior for motions, such as sliding. More precisely, we replace Gaussian smoothing by fast, structure-preserving, guided filtering to provide efficient, locally adaptive regularization of the estimated displacement field. We illustrate the approach by applying it to estimate sliding motions at lung and liver interfaces on challenging four-dimensional computed tomography (CT) and dynamic contrast-enhanced magnetic resonance imaging datasets. The results show that guided filter-based regularization improves the accuracy of lung and liver motion correction as compared to Gaussian smoothing. Furthermore, our framework achieves state-of-the-art results on a publicly available CT liver dataset.
可变形图像配准是医学成像中运动校正的关键组成部分，需要高效且能提供合理的空间变换，可靠地近似复杂人体器官运动的生物学特征。标准方法，如恶魔配准，大多对器官运动使用高斯正则化，虽然计算效率高，但排除了其在本质上更复杂的器官运动（如滑动界面）中的应用。我们提出基于超体素的运动正则化方法，该方法为诸如滑动等运动提供了一种保留不连续性的综合先验。更确切地说，我们用快速、保结构的引导滤波代替高斯平滑，以对估计的位移场进行高效、局部自适应的正则化。我们通过将其应用于具有挑战性的四维计算机断层扫描（CT）和动态对比增强磁共振成像数据集来估计肺和肝界面的滑动运动，从而阐述该方法。结果表明，与高斯平滑相比，基于引导滤波的正则化提高了肺和肝运动校正的准确性。此外，我们的框架在一个公开可用的CT肝脏数据集上取得了领先成果。
