Hodneland Erlend, Lundervold Arvid, Rørvik Jarle, Munthe-Kaas Antonella Z
Department of Biomedicine, University of Bergen, Bergen, Norway.
Department of Biomedicine, University of Bergen, Bergen, Norway; Department of Radiology, Haukeland University Hospital, Bergen, Norway.
Comput Med Imaging Graph. 2014 Apr;38(3):202-10. doi: 10.1016/j.compmedimag.2013.12.007. Epub 2013 Dec 21.
Dynamic MR image recordings (DCE-MRI) of moving organs using bolus injections create two different types of dynamics in the images: (i) spatial motion artifacts due to patient movements, breathing and physiological pulsations that we want to counteract and (ii) signal intensity changes during contrast agent wash-in and wash-out that we want to preserve. Proper image registration is needed to counteract the motion artifacts and for a reliable assessment of physiological parameters. In this work we present a partial differential equation-based method for deformable multimodal image registration using normalized gradients and the Fourier transform to solve the Euler-Lagrange equations in a multilevel hierarchy. This approach is particularly well suited to handle the motion challenges in DCE-MRI time series, being validated on ten DCE-MRI datasets from the moving kidney. We found that both normalized gradients and mutual information work as high-performing cost functionals for motion correction of this type of data. Furthermore, we demonstrated that normalized gradients have improved performance compared to mutual information as assessed by several performance measures. We conclude that normalized gradients can be a viable alternative to mutual information regarding registration accuracy, and with promising clinical applications to DCE-MRI recordings from moving organs.
使用团注注射对移动器官进行动态磁共振图像记录（DCE - MRI）会在图像中产生两种不同类型的动态变化：（i）由于患者移动、呼吸和生理脉动导致的空间运动伪影，我们希望抵消这些伪影；（ii）对比剂注入和洗脱期间的信号强度变化，我们希望保留这些变化。需要进行适当的图像配准来抵消运动伪影并可靠地评估生理参数。在这项工作中，我们提出了一种基于偏微分方程的可变形多模态图像配准方法，该方法使用归一化梯度和傅里叶变换在多级层次结构中求解欧拉 - 拉格朗日方程。这种方法特别适合处理DCE - MRI时间序列中的运动挑战，并在来自移动肾脏的十个DCE - MRI数据集上得到了验证。我们发现，归一化梯度和互信息都可作为此类数据运动校正的高性能代价函数。此外，通过几种性能指标评估，我们证明归一化梯度相比互信息具有更好的性能。我们得出结论，就配准精度而言，归一化梯度可以成为互信息的可行替代方案，并且在对来自移动器官的DCE - MRI记录进行临床应用方面具有广阔前景。
