不同平行传输方法在三维心脏数据变形研究中的比较

IF 1.3 4区 数学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Paolo Piras, Nicolas Guigui, Valerio Varano
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引用次数: 0

摘要

比较不同跳动心脏的变形是一项具有挑战性的工作。在临床中,受损情况通常是通过(局部和整体)变形参数来识别的,因此,如果能过滤掉个体间最初的形态(形状+大小)差异,就能更有效地比较同一序列空间中不同个体在一次跳动过程中心脏变形的特殊性质。如果考虑到心脏运动轨迹本身的形状,情况就更加如此。微分几何学中一种名为 "平行传输 "的几何机制可以满足这一需求。近年来,一些平行传输方法已被应用于通过超声心动图、CT 扫描或磁共振获取的心脏病学数据。与此同时,人们也在努力比较应用于各种玩具示例和真实变形数据的不同平行传输算法。在这里,我们面临的问题是,在右心室变形的背景下,将大量使用的 LDDMM 平行传输与最近提出的黎曼 "TPS 空间 "进行比较。利用局部张量诊断和基于全局能量和形状距离的参数,我们探索了传输数据中原始变形的保持情况,这些数据分别属于一名健康受试者和三名受法洛四联症、房间隔缺损和肺动脉高压影响的受试者。我们还在一个更大的数据集中对 82 名健康受试者和 21 名肥厚型心肌病患者的左心室进行了同样的处理。我们还在一个更大的数据集中对 82 名健康受试者和 21 名肥厚型心肌病患者的左心室进行了同样的研究。特别是,我们将 TPS 空间与经典的 LDDMM 和能够处理球面差异的改进型 LDDMM 进行了对比。我们的研究结果表明,TPS 空间比传统 LDDMM 更有优势。修改后的 LDDMM 表现类似,因为它能更好地保持所选的诊断方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comparison of Different Parallel Transport Methods for the Study of Deformations in 3D Cardiac Data

Comparison of Different Parallel Transport Methods for the Study of Deformations in 3D Cardiac Data

Comparing the deformations of different beating hearts is a challenging operation. As in clinics the impaired condition is often recognized upon (local and global) deformation parameters, the particular nature of heart deformation during one beat can be compared among different individuals in the same ordination space more effectively if initial inter-individual form (shape + size) differences are filtered out. This is even more true if the shape of cardiac trajectory itself is under consideration. This need is satisfied by applying a geometric machinery named “parallel transport” in the field of differential geometry. In recent years several parallel transport methods have been applied to cardiological data acquired via echocardiography, CT scan or magnetic resonance. Concomitantly, some efforts were made for comparing different parallel transport algorithms applied to a variety of toy examples and real deformational data. Here we face the problem of comparing the heavily used LDDMM parallel transport with the recently proposed Riemannian “TPS space” in the context of the deformation of the right ventricle. Using local tensors diagnostics and global energy-based and shape distance-based parameters, we explored the maintenance of original deformations in transported data in four systo-diastolic deformations belonging to one healthy subject and three individuals affected by tetralogy of Fallot, atrial septal defect and pulmonary hypertension. We also do the same in a larger dataset relative to the left ventricle of 82 heathly subjects and 21 patients affected by hypertrophic cardiomyopathy. We also do the same in a larger dataset relative to the left ventricle of 82 heathly subjects and 21 patients affected by hypertrophic cardiomyopathy. In particular, we contrasted the TPS space with classic LDDMM and a modified LDDMM able to manage spherical differences. Our results point toward a neat superiority of TPS space over classic LDDMM. The modified LDDMM performs similarly as it maintains better the chosen diagnostics.

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来源期刊
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision 工程技术-计算机:人工智能
CiteScore
4.30
自引率
5.00%
发文量
70
审稿时长
3.3 months
期刊介绍: The Journal of Mathematical Imaging and Vision is a technical journal publishing important new developments in mathematical imaging. The journal publishes research articles, invited papers, and expository articles. Current developments in new image processing hardware, the advent of multisensor data fusion, and rapid advances in vision research have led to an explosive growth in the interdisciplinary field of imaging science. This growth has resulted in the development of highly sophisticated mathematical models and theories. The journal emphasizes the role of mathematics as a rigorous basis for imaging science. This provides a sound alternative to present journals in this area. Contributions are judged on the basis of mathematical content. Articles may be physically speculative but need to be mathematically sound. Emphasis is placed on innovative or established mathematical techniques applied to vision and imaging problems in a novel way, as well as new developments and problems in mathematics arising from these applications. The scope of the journal includes: computational models of vision; imaging algebra and mathematical morphology mathematical methods in reconstruction, compactification, and coding filter theory probabilistic, statistical, geometric, topological, and fractal techniques and models in imaging science inverse optics wave theory. Specific application areas of interest include, but are not limited to: all aspects of image formation and representation medical, biological, industrial, geophysical, astronomical and military imaging image analysis and image understanding parallel and distributed computing computer vision architecture design.
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