高级检索
当前位置: 首页 > 详情页

Characterization of the nonlinear elastic properties of soft tissues using the supersonic shear imaging (SSI) technique: Inverse method, ex vivo and in vivo experiments

文献详情

资源类型:
WOS体系:
Pubmed体系:

收录情况: ◇ SCIE ◇ EI

单位: [1]Tsinghua Univ, Dept Engn Mech, AML, Inst Biomech & Med Engn, Beijing 100084, Peoples R China [2]Capital Med Univ, Beijing Friendship Hosp, Dept Ultrasound, Beijing 100050, Peoples R China
出处:
ISSN:

关键词: Dynamic elastography Supersonic shear imaging Stressed hyperelastic soft tissues In vivo experiments Clinical diagnosis

摘要:
Dynamic elastography has become a new clinical tool in recent years to characterize the elastic properties of soft tissues in vivo, which are important for the disease diagnosis, e.g., the detection of breast and thyroid cancer and liver fibrosis. This paper investigates the supersonic shear imaging (SSI) method commercialized in recent years with the purpose to determine the nonlinear elastic properties based on this promising technique. Particularly, we explore the propagation of the shear wave induced by the acoustic radiation force in a stressed hyperelastic soft tissue described via the Demiray-Fung model. Based on the elastodynamics theory, an analytical solution correlating the wave speed with the hyperelastic parameters of soft tissues is first derived. Then an inverse approach is established to determine the hyperelastic parameters of biological soft tissues based on the measured wave speeds at different stretch ratios. The property of the inverse method, e.g., the existence, uniqueness and stability of the solution, has been investigated. Numerical experiments based on finite element simulations and the experiments conducted on the phantom and pig livers have been employed to validate the new method. Experiments performed on the human breast tissue and human heel fat pads have demonstrated the capability of the proposed method for measuring the in viva nonlinear elastic properties of soft tissues. Generalization of the inverse analysis to other material models and the implication of the results reported here for clinical diagnosis have been discussed. (C) 2014 Elsevier B.V. All rights reserved.

基金:
语种:
被引次数:
WOS:
PubmedID:
中科院(CAS)分区:
出版当年[2014]版:
大类 | 1 区 工程技术
小类 | 1 区 计算机:跨学科应用 2 区 计算机:人工智能 2 区 工程:生物医学 2 区 核医学
最新[2025]版:
大类 | 1 区 医学
小类 | 1 区 计算机:人工智能 1 区 计算机:跨学科应用 1 区 工程:生物医学 1 区 核医学
JCR分区:
出版当年[2013]版:
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Q1 ENGINEERING, BIOMEDICAL
最新[2023]版:
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Q1 ENGINEERING, BIOMEDICAL Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子: 最新[2023版] 最新五年平均[2021-2025] 出版当年[2013版] 出版当年五年平均[2009-2013] 出版前一年[2012版] 出版后一年[2014版]

第一作者:
第一作者单位: [1]Tsinghua Univ, Dept Engn Mech, AML, Inst Biomech & Med Engn, Beijing 100084, Peoples R China
通讯作者:
推荐引用方式(GB/T 7714):
APA:
MLA:

资源点击量:1320 今日访问量:0 总访问量:816 更新日期:2025-04-01 建议使用谷歌、火狐浏览器 常见问题

版权所有:重庆聚合科技有限公司 渝ICP备12007440号-3 地址:重庆市两江新区泰山大道西段8号坤恩国际商务中心16层(401121)