用行波扩展法测量横向各向同性生物组织的生物力学特性

IF 10.7 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Medical image analysis Pub Date : 2025-01-09 DOI:10.1016/j.media.2025.103457
Shengyuan Ma, Zhao He, Runke Wang, Aili Zhang, Qingfang Sun, Jun Liu, Fuhua Yan, Michael S. Sacks, Xi-Qiao Feng, Guang-Zhong Yang, Yuan Feng
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引用次数: 0

摘要

纤维嵌入生物组织的各向异性力学特性对于理解其发育、衰老、疾病进展和对治疗的反应至关重要。然而,使用弹性成像准确、快速地评估体内力学各向异性仍然具有挑战性。为了解决在涉及复杂波动方程的反问题中实现精度和效率的困境,我们提出了一个利用行波展开模型的计算框架。该框架利用了横向各向同性材料的独特波特性和物理上有意义的算子组合。推导了反演的解析解,并根据实际情况进行了工程优化。使用模拟、离体肌肉组织和体内人类白质的测量结果验证了确定体内各向异性生物力学特性的框架,突出了其测量各种纤维嵌入生物组织的潜力。
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Measurement of biomechanical properties of transversely isotropic biological tissue using traveling wave expansion
The anisotropic mechanical properties of fiber-embedded biological tissues are essential for understanding their development, aging, disease progression, and response to therapy. However, accurate and fast assessment of mechanical anisotropy in vivo using elastography remains challenging. To address the dilemma of achieving both accuracy and efficiency in this inverse problem involving complex wave equations, we propose a computational framework that utilizes the traveling wave expansion model. This framework leverages the unique wave characteristics of transversely isotropic material and physically meaningful operator combinations. The analytical solutions for inversion are derived and engineering optimization is made to adapt to actual scenarios. Measurement results using simulations, ex vivo muscle tissue, and in vivo human white matter validate the framework in determining in vivo anisotropic biomechanical properties, highlighting its potential for measurement of a variety of fiber-embedded biological tissues.
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
期刊最新文献
Corrigendum to "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge" [Medical Image Analysis, April 2022, Volume 77, 102333]. Editorial for Special Issue on Foundation Models for Medical Image Analysis. Few-shot medical image segmentation with high-fidelity prototypes. The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction. DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks.
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