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首页> 外文期刊>Medical image analysis >A dynamic elastic model for segmentation and tracking of the heart in MR image sequences.
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A dynamic elastic model for segmentation and tracking of the heart in MR image sequences.

机译：用于在MR图像序列中分割和跟踪心脏的动态弹性模型。

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摘要

Strong prior models are a prerequisite for reliable spatio-temporal cardiac image analysis. While several cardiac models have been presented in the past, many of them are either too complex for their parameters to be estimated on the sole basis of MR Images, or overly simplified. In this paper, we present a novel dynamic model, based on the equation of dynamics for elastic materials and on Fourier filtering. The explicit use of dynamics allows us to enforce periodicity and temporal smoothness constraints. We propose an algorithm to solve the continuous dynamical problem associated to numerically adapting the model to the image sequence. Using a simple 1D example, we show how temporal filtering can help removing noise while ensuring the periodicity and smoothness of solutions. The proposed dynamic model is quantitatively evaluated on a database of 15 patients which shows its performance and limitations. Also, the ability of the model to capture cardiac motion is demonstrated on synthetic cardiac sequences. Moreover, existence, uniqueness of the solution and numerical convergence of the algorithm can be demonstrated.
机译：强大的先验模型是可靠的时空心脏图像分析的先决条件。尽管过去已经提出了几种心脏模型，但其中许多模型要么过于复杂，以至于无法仅凭MR图像来估计其参数，要么过于简化。在本文中，我们基于弹性材料的动力学方程和傅里叶滤波提出了一种新颖的动力学模型。动力学的明确使用使我们能够强制执行周期性和时间平滑性约束。我们提出一种算法来解决与模型在数值上适应图像序列相关的连续动力学问题。通过一个简单的一维示例，我们展示了时间滤波如何在确保解决方案的周期性和平滑性的同时帮助消除噪声。建议的动态模型在15位患者的数据库中进行了定量评估，显示了其性能和局限性。同样，在合成心脏序列上证明了模型捕获心脏运动的能力。此外，可以证明该解的存在性，唯一性和算法的数值收敛性。

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