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Surface Mesh Reconstruction from Cardiac MRI Contours ?

机译:从心脏MRI轮廓重建表面网格?

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We introduce a tool to build a surface mesh able to deal with sparse, heterogeneous, non-parallel, cross-sectional, non-coincidental contours and show its application to reconstruct surfaces of the heart. In recent years, much research has looked at creating personalised 3D anatomical models of the heart. These models usually incorporate a geometrical reconstruction of the anatomy in order to better understand cardiovascular functions as well as predict different cardiac processes. As MRIs are becoming the standard for cardiac medical imaging, we tested our methodology on cardiac MRI data from standard acquisitions. However, the ability to accurately reconstruct heart anatomy in three dimensions commonly comes with fundamental challenges—notably, the trade-off between data fitting and expected visual appearance. Most current techniques can either require contours from parallel slices or, if multiple slice orientations are used, require an exact match between these contours. In addition, some methods introduce a bias by the use of prior shape models or by trade-offs between the data matching terms and the smoothing terms. Our approach uses a composition of smooth approximations towards the maximization of the data fitting, ensuring a good matching to the input data as well as pleasant interpolation characteristics. To assess our method in the task of cardiac mesh generations, we evaluated its performance on synthetic data obtained from a cardiac statistical shape model as well as on real data. Using a statistical shape model, we simulated standard cardiac MRI acquisitions planes and contour data. We performed a multi-parameter evaluation study using plausible cardiac shapes generated from the model. We also show that long axes contours as well as the most extremal slices (basal and apical) contain the most amount of structural information, and thus should be taken into account when generating anatomically relevant geometrical cardiovascular surfaces. Our method is both used on epicardial and endocardial left ventricle surfaces as well as on the right ventricle.
机译:我们介绍了一种工具来构建能够处理稀疏,异构,不平行,横截面,巧合的轮廓的表面网格,并展示了其在重建心脏表面中的应用。近年来,许多研究着眼于创建心脏的个性化3D解剖模型。这些模型通常包含解剖结构的几何重构,以便更好地了解心血管功能并预测不同的心脏过程。随着MRI成为心脏医学成像的标准,我们对来自标准采集的心脏MRI数据的方法进行了测试。但是,在三个维度上准确重建心脏解剖结构的能力通常会带来根本性的挑战-尤其是在数据拟合和预期的视觉外观之间进行权衡。当前大多数技术可能需要来自平行切片的轮廓,或者,如果使用多个切片方向,则需要这些轮廓之间的精确匹配。另外,一些方法通过使用先前的形状模型或通过数据匹配项和平滑项之间的折衷引入了偏差。我们的方法使用平滑逼近的组合来使数据拟合最大化,从而确保与输入数据的良好匹配以及令人愉悦的插值特性。为了评估我们在心脏网格生成任务中的方法,我们根据从心脏统计形状模型获得的合成数据以及实际数据评估了其性能。使用统计形状模型,我们模拟了标准的心脏MRI采集平面和轮廓数据。我们使用从模型生成的合理的心脏形状进行了多参数评估研究。我们还表明,长轴轮廓以及最远端的切片(基端和根尖)包含最多的结构信息,因此在生成与解剖学相关的几何心血管表面时应予以考虑。我们的方法同时用于心外膜和心内膜左心室表面以及右心室。

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