Multi-component heart reconstruction from volumetric imaging

C. Bajaj, S. Goswami
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引用次数: 12

Abstract

Computer Tomography (CT) and in particular super fast, 64 and 256 detector CT has rapidly advanced over recent years, such that high resolution cardiac imaging has become a reality. In this paper, we briefly introduce a framework that we have built to construct three dimensional (3D) finite-element and boundary element mesh models of the human heart directly from high resolution CT imaging data. Although, the overall IMAGING-MODELING framework consists of image processing, geometry processing and meshing algorithms, our main focus in this paper will revolve around three key geometry processing steps which are parts of the so-called IMAGING-MODELING framework. These three steps are geometry cleanup or CURATION, anatomy guided annotation or SEGMENTATION and construction of GENERALIZED OFFSET SURFACE. These three algorithms, due to the very nature of the computation involved, can also be thought as parts of a more generalized modeling technique, namely geometric modeling with distance function. As part of the results presented in the paper, we will show that our algorithms are robust enough to effectively deal with the challenges posed by the real-world patient CT data collected from our radiologist collaborators.
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容积成像的多分量心脏重建
计算机断层扫描(CT),特别是超快的64和256探测器CT近年来迅速发展,使高分辨率心脏成像成为现实。在本文中,我们简要介绍了我们建立的一个框架,该框架可以直接从高分辨率CT成像数据中构建人类心脏的三维(3D)有限元和边界元网格模型。尽管整个成像建模框架由图像处理、几何处理和网格算法组成,但我们在本文中的主要重点将围绕三个关键的几何处理步骤展开,这些步骤是所谓的成像建模框架的一部分。这三个步骤分别是几何清理或整理、解剖引导注释或分割以及广义偏移曲面的构造。由于所涉及的计算的本质,这三种算法也可以被认为是更广义的建模技术的一部分,即具有距离函数的几何建模。作为论文中提出的结果的一部分,我们将展示我们的算法足够强大,可以有效地处理从我们的放射科医生合作者收集的真实世界患者CT数据所带来的挑战。
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