School of Computer Science and Engineering, Seoul National University, Korea.
Med Phys. 2010 Aug;37(8):4307-17. doi: 10.1118/1.3460316.
This article proposes an accurate and fast deformable registration method between end-exhale and end-inhale CT scans that can handle large lung deformations and accelerate the registration process.
The density correction method is applied to reduce the density difference between two CT scans due to respiration and gravity. The lungs are globally aligned by affine registration and nonlinearly deformed by a demons algorithm using a combined gradient force and active cells. The use of combined gradient force allows a fast convergence in the lung regions with a weak gradient of the target image by taking into account the gradient of the source image. The use of active cells helps to accelerate the registration process and reduce the degree of deformation folding because it avoids unnecessary computation of the displacement for well-matched lung regions.
The proposed method was tested with end-exhale and end-inhale CT scans acquired from eight normal subjects. The performance of the proposed method was evaluated through comparisons of methods that use a target gradient force or a combined gradient force, as well as methods with and without active cells. The proposed method with combined gradient force led to significantly higher accuracy compared to the method with target gradient force. For the entire lung, the proposed method provided a mean landmark error of 2.8 +/- 1.5 mm. For the lower 30% part of the lungs, the Dice similarity coefficient and normalized cross correlation of the proposed method were higher than the original demon algorithm by 2.3% (p=0.0172) and 2.2% (p=0.0028), respectively. The proposed method with an active cell led to fewer voxels with negative Jacobian values and a 55% decrease of processing time compared to the method without an active cell.
The results show that the proposed method can accurately register lungs with large deformations and can considerably reduce the processing time. The proposed deformable registration technique can be used for quantitative assessments of air trapping in obstructive lung disease and for tumor motion tracking during the planning of radiotherapy treatments.
本文提出了一种准确快速的端呼气和端吸气 CT 扫描之间的变形配准方法，能够处理大的肺变形并加速配准过程。
应用密度校正方法来减少由于呼吸和重力导致的两个 CT 扫描之间的密度差异。通过仿射配准对肺进行全局对齐，并通过使用组合梯度力和活动细胞的 demons 算法对肺进行非线性变形。使用组合梯度力可以考虑到源图像的梯度，从而在目标图像梯度较弱的肺区域中快速收敛。使用活动细胞有助于加速配准过程并减少变形折叠的程度，因为它避免了对匹配良好的肺区域进行不必要的位移计算。
该方法在 8 个正常受试者的端呼气和端吸气 CT 扫描上进行了测试。通过比较使用目标梯度力或组合梯度力的方法、以及使用和不使用活动细胞的方法，评估了所提出方法的性能。与使用目标梯度力的方法相比，使用组合梯度力的方法导致了显著更高的准确性。对于整个肺，所提出的方法提供了 2.8±1.5mm 的平均标志点误差。对于肺的下部 30%部分，所提出的方法的 Dice 相似系数和归一化互相关分别比原始 demon 算法高 2.3%（p=0.0172）和 2.2%（p=0.0028）。与没有活动细胞的方法相比，使用活动细胞的方法导致具有负雅可比值的体素更少，并且处理时间减少了 55%。
结果表明，所提出的方法可以准确地配准具有大变形的肺，并且可以大大减少处理时间。所提出的变形配准技术可用于阻塞性肺疾病的空气捕获的定量评估以及放射治疗计划中肿瘤运动的跟踪。
