IEEE Trans Med Imaging. 2014 Apr;33(4):882-90. doi: 10.1109/TMI.2013.2297333.
This paper presents a predictive framework for the statistical personalization of ventricular fibers. To this end, the relationship between subject-specific geometry of the left (LV) and right ventricles (RV) and fiber orientation is learned statistically from a training sample of ex vivo diffusion tensor imaging datasets. More specifically, the axes in the shape space which correlate most with the myocardial fiber orientations are extracted and used for prediction in new subjects. With this approach and unlike existing fiber models, inter-subject variability is taken into account to generate latent shape predictors that are statistically optimal to estimate fiber orientation at each individual myocardial location. The proposed predictive model was applied to the task of personalizing fibers in 10 canine subjects. The results indicate that the ventricular shapes are good predictors of fiber orientation, with an improvement of 11.4% in accuracy over the average fiber model.
本文提出了一种用于心室纤维统计个性化的预测框架。为此，从离体扩散张量成像数据集的训练样本中统计学习了左心室（LV）和右心室（RV）的特定主体几何形状与纤维方向之间的关系。更具体地说，从形状空间中提取与心肌纤维方向最相关的轴，并将其用于新主体的预测。通过这种方法，与现有的纤维模型不同，本方法考虑了个体间的可变性，以生成潜在的形状预测因子，这些预测因子在统计上是最优的，可以估计每个心肌位置的纤维方向。所提出的预测模型应用于个性化 10 只犬科动物的纤维任务。结果表明，心室形状是纤维方向的良好预测因子，其准确性比平均纤维模型提高了 11.4%。
