GE Healthcare, London, ON N6A 4V2, Canada.
IEEE Trans Biomed Eng. 2010 Aug;57(8):2001-10. doi: 10.1109/TBME.2010.2048752. Epub 2010 May 24.
Tracking heart motion plays an essential role in the diagnosis of cardiovascular diseases. As such, accurate characterization of dynamic behavior of the left ventricle (LV) is essential in order to enhance the performance of motion estimation. However, a single Markovian model is not sufficient due to the substantial variability in typical heart motion. Moreover, dynamics of an abnormal heart could be very different from that of a normal heart. This study introduces a tracking approach based on multiple models, each matched to a different phase of the LV motion. First, the algorithm adopts a graph cut distribution matching method to tackle the problem of segmenting LV cavity from cardiac MR images, which is acknowledged as a difficult problem because of low contrast and photometric similarities between the heart wall and papillary muscles within the LV cavity. Second, interacting multiple model (IMM), an effective estimation algorithm for Markovian switching system, is devised subsequent to the segmentations to yield state estimates of the endocardial boundary points. The IMM also yields the model probability indicating the model that most closely matches the LV motion. The proposed method is evaluated quantitatively by comparison with independent manual segmentations over 2280 images acquired from 20 subjects, which demonstrated competitive results in comparisons with related recent methods.
心脏运动追踪在心血管疾病的诊断中起着至关重要的作用。因此，为了提高运动估计的性能，准确描述左心室（LV）的动态行为至关重要。然而，由于典型心脏运动的变化很大，单个马尔可夫模型是不够的。此外，异常心脏的动力学可能与正常心脏有很大的不同。本研究提出了一种基于多个模型的跟踪方法，每个模型都与 LV 运动的不同阶段相匹配。首先，该算法采用图割分布匹配方法来解决从心脏磁共振图像中分割 LV 腔的问题，由于 LV 腔内的心脏壁和乳头肌之间的低对比度和光度相似性，这是一个公认的难题。其次，交互多模型（IMM）是一种用于马尔可夫切换系统的有效估计算法，在分割之后设计该算法以生成心内膜边界点的状态估计。IMM 还生成模型概率，指示最接近 LV 运动的模型。该方法通过与 20 名受试者采集的 2280 张图像的独立手动分割进行定量评估，与相关的最新方法相比，该方法具有竞争力的结果。
