title: Performance comparison of rigid and affine models for motion estimation using ultrasound radio-frequency signals.
abstract: Tissue motion estimation is widely used in many ultrasound techniques. Rigid-model-based and nonrigid-modelbased methods are two main groups of space-domain methods of tissue motion estimation. The affine model is one of the commonly used nonrigid models. The performances of the rigid model and affine model have not been compared on ultrasound RF signals, which have been demonstrated to obtain higher accuracy, precision, and resolution in motion estimation compared with B-mode images. In this study, three methods, i.e., the normalized cross-correlation method with rigid model (NCC), the optical flow method with rigid model (OFRM), and the optical flow method with affine model (OFAM), are compared using ultrasound RF signals, rather than the B-mode images used in previous studies. Simulations, phantom, and in vivo experiments are conducted to make the comparison. In the simulations, the root-mean-square errors (RMSEs) of axial and lateral displacements and strains are used to assess the accuracy of motion estimation, and the elastographic signal-tonoise ratio (SNRe) and contrast-to-noise ratio (CNRe) are used to evaluate the quality of axial strain images. In the phantom experiments, the registration error between the pre- and postdeformation RF signals, as well as the SNRe and CNRe of axial strain images, are utilized as the evaluation criteria. In the in vivo experiments, the registration error is used to evaluate the estimation performance. The results show that the affinemodel- based method (i.e., OFAM) obtains the lowest RMSE or registration error and the highest SNRe and CNRe among all the methods. The affine model is demonstrated to be superior to the rigid model in motion estimation based on RF signals.
authors.forename: Xiaochang
authors.initials: X
authors.lastname: Pan
authors.forename: Ke
authors.initials: K
authors.lastname: Liu
authors.forename: Jinghua
authors.initials: J
authors.lastname: Shao
authors.forename: Jing
authors.initials: J
authors.lastname: Gao
authors.forename: Lingyun
authors.initials: L
authors.lastname: Huang
authors.forename: Jing
authors.initials: J
authors.lastname: Bai
authors.forename: Jianwen
authors.initials: J
authors.lastname: Luo
mesh_terms: D000368:Aged; D000465:Algorithms; D002340:Carotid Artery Diseases / Q000000981:diagnostic imaging; D006801:Humans; D008875:Middle Aged; D008962:Models, Theoretical; D009068:Movement / Q000502:physiology*; D019047:Phantoms, Imaging; D059629:Signal-To-Noise Ratio; D014463:Ultrasonography / Q000295:instrumentation / Q000379:methods* / Q000592:standards*