成像体模
弹性成像
重复性
非线性系统
弹性模量
材料科学
弹性(物理)
生物医学工程
迭代重建
图像分辨率
声学
光学
数学
物理
计算机科学
计算机视觉
超声波
统计
复合材料
医学
量子力学
作者
Daniel Gendin,Rohit Nayak,Yuqi Wang,Mahdi Bayat,Robert T. Fazzio,Assad A. Oberai,Timothy J. Hall,Paul E. Barbone,Azra Alizad,Mostafa Fatemi
标识
DOI:10.1109/tmi.2020.3036032
摘要
Compression elastography allows the precise measurement of large deformations of soft tissue in vivo. From an image sequence showing tissue undergoing large deformation, an inverse problem for both the linear and nonlinear elastic moduli distributions can be solved. As part of a larger clinical study to evaluate nonlinear elastic modulus maps (NEMs) in breast cancer, we evaluate the repeatability of linear and nonlinear modulus maps from repeat measurements. Within the cohort of subjects scanned to date, 20 had repeat scans. These repeated scans were processed to evaluate NEM repeatability. In vivo data were acquired by a custom-built, digitally controlled, uniaxial compression device with force feedback from the pressure-plate. RF-data were acquired using plane-wave imaging, at a frame-rate of 200 Hz, with a ramp-and-hold compressive force of 8N, applied at 8N/sec. A 2D block-matching algorithm was used to obtain sample-level displacement fields which were then tracked at subsample resolution using 2D cross correlation. Linear and nonlinear elasticity parameters in a modified Veronda-Westmann model of tissue elasticity were estimated using an iterative optimization method. For the repeated scans, B-mode images, strain images, and linear and nonlinear elastic modulus maps are measured and compared. Results indicate that when images are acquired in the same region of tissue and sufficiently high strain is used to recover nonlinearity parameters, then the reconstructed modulus maps are consistent.
科研通智能强力驱动
Strongly Powered by AbleSci AI