GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications.
GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications.
复制标题
DOI：
10.1117/1.jmi.5.2.024001
复制
发表时间：
2018-04
期刊：
影响因子：
--
通讯作者：
Schnabel JA
中科院分区：
文献类型：
--
作者：
Papież BW;Franklin JM;Heinrich MP;Gleeson FV;Brady M;Schnabel JA
可变形图像配准是医学成像中运动校正的关键组成部分，它需要高效，并提供可信的空间变换，可靠地近似复杂人体器官运动的生物学方面。标准方法，如恶魔配准，大多使用高斯正则化器官运动，这虽然计算效率很高，但排除了它们适用于本质上更复杂的器官运动，如滑动界面。我们提出了基于超体素的运动正则化，为滑动等运动提供了一个完整的不连续保持先验。更准确地说，我们用快速的、保持结构的、引导的滤波来代替高斯平滑，以提供估计位移场的有效的、局部自适应的正则化。我们通过将其应用于在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上估计肺和肝脏界面的滑动运动来说明该方法。结果表明，与高斯平滑相比，基于导引滤波的正则化方法提高了肺、肝运动校正的精度。此外，我们的框架在公开可用的CT肝脏数据集上实现了最先进的结果。
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.