Deformable models with sparsity constraints for cardiac motion analysis.
Deformable models with sparsity constraints for cardiac motion analysis.
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DOI：
10.1016/j.media.2014.03.002
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发表时间：
2014-08
影响因子：
10.9
通讯作者：
Axel L
中科院分区：
文献类型：
--
作者：
Yu Y;Zhang S;Li K;Metaxas D;Axel L
可变形模型集成了从图像外观线索中获得的自下而上的信息和自上而下的形状先验知识。它们在医学图像分析中得到了成功的应用。传统变形模型的一个局限性是，从图像数据中提取的信息可能存在严重误差，这对变形精度有不利影响。为了缓解这个问题，我们引入了一种新的可变形模型，它的灵感来自压缩感知，一种通过利用一些稀疏先验来精确重建信号的技术。在本文中，我们使用稀疏性约束来处理异常值或粗误差，并将它们与可变形模型无缝集成。提出的新公式应用于使用标记磁共振成像（tMRI）分析心脏运动，其中自动标记线跟踪结果由于图像质量差而非常嘈杂。我们的新的可变形模型跟踪心脏运动稳健，结果应变与那些从手动标签计算一致。
Deformable models integrate bottom-up information derived from image appearance cues and top-down priori knowledge of the shape. They have been widely used with success in medical image analysis. One limitation of traditional deformable models is that the information extracted from the image data may contain gross errors, which adversely affect the deformation accuracy. To alleviate this issue, we introduce a new family of deformable models that are inspired from the compressed sensing, a technique for accurate signal reconstruction by harnessing some sparseness priors. In this paper, we employ sparsity constraints to handle the outliers or gross errors, and integrate them seamlessly with deformable models. The proposed new formulation is applied to the analysis of cardiac motion using tagged magnetic resonance imaging (tMRI), where the automated tagging line tracking results are very noisy due to the poor image quality. Our new deformable models track the heart motion robustly, and the resulting strains are consistent with those calculated from manual labels.