Oubel E, Tobon-Gomez C, Hero A O, Frangi A F
Computational Imaging Laboratory, Pompeu Fabra University, Barcelona, Spain.
Med Image Comput Comput Assist Interv. 2005;8(Pt 2):271-8. doi: 10.1007/11566489_34.
Tagged Magnetic Resonance Imaging (MRI) is currently the reference MR modality for myocardial motion and strain analysis. NMI-based non rigid registration has proven to be an accurate method to retrieve cardiac deformation fields. The use of alphaMI permits higher dimensional features to be implemented in myocardial deformation estimation through image registration. This paper demonstrates that this is feasible with a set of Haar wavelet features of high dimension. While we do not demonstrate performance improvement for this set of features, there is no significant degradation as compared to implementing the registration method with the traditional NMI metric. We use Entropic Spanning Graphs (ESGs) to estimate the alphaMI of the wavelet feature vectors WFVs since this is not possible with histograms. To the best of our knowledge, this is the first time that ESGs are used for non rigid registration.
标记磁共振成像(MRI)是目前用于心肌运动和应变分析的参考磁共振模态。基于归一化互信息(NMI)的非刚性配准已被证明是一种获取心脏变形场的准确方法。α互信息(alphaMI)的使用允许通过图像配准在心肌变形估计中实现更高维度的特征。本文证明了使用一组高维哈尔小波特征来实现这一点是可行的。虽然我们没有证明这组特征在性能上有所提升,但与使用传统NMI度量实现配准方法相比,也没有显著的性能下降。我们使用熵生成图(ESG)来估计小波特征向量(WFV)的alphaMI,因为用直方图无法做到这一点。据我们所知,这是首次将ESG用于非刚性配准。