title: A 2D strain estimator with numerical optimization method for soft-tissue elastography.
abstract: Elastography is a bioelasticity-based imaging modality which has been proved to be a potential evaluation tool to detect the tissue abnormalities. Conventional method for elastography is to estimate the displacement based on cross-correlation technique firstly, then strain profile is calculated as the gradient of the displacement. The main problem of this method arises from the fact that the cross-correlation between pre- and post-compression signals will be decreased because of the signal's compression-to-deformation. It may constrain the estimation of the displacement. Numerical optimization, as an efficient tool to estimate the non-rigid deformation in image registration, has its potential to achieve the elastogram. This paper incorporates the idea of image registration into elastography and proposes a radio frequency (RF) signal registration strain estimator based on the minimization of a cost function using numerical optimization method with Powell algorithm (NOMPA). To evaluate the proposed scheme, the simulation data with a hard inclusion embedded in the homogeneous background is produced for analysis. NOMPA can obtain the displacement profiles and strain profiles simultaneously. When compared with the cross-correlation based method, NOMPA presents better signal-to-noise ratio (SNR, 32.6+/-1.5 dB vs. 23.8+/-1.1 dB) and contrast-to-noise ratio (CNR, 28.8+/-1.8 dB vs. 21.7+/-0.9 dB) in axial normal strain estimation. The in vitro experiment of porcine liver with ethanol-induced lesion is also studied. The statistic results of SNR and CNR indicate that strain profiles by NOMPA performs better anti-noise and target detectability than that by cross-correlation based method. Though NOMPA carry a heavier computational burden than cross-correlation based method, it may be an useful method to obtain 2D strains in elastography.
authors: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
authors: Ke
authors: K
authors: Liu
authors: Pengfei
authors: P
authors: Zhang
authors: Jinhua
authors: J
authors: Shao
authors: Xinjian
authors: X
authors: Zhu
authors: Yun
authors: Y
authors: Zhang
authors: Jing
authors: J
authors: Bai
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D055119:Elastic Modulus / Q000502:physiology; D054459:Elasticity Imaging Techniques / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D066298:In Vitro Techniques; D008099:Liver / Q000000981:diagnostic imaging* / Q000502:physiology*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013314:Stress, Mechanical; D013552:Swine