Research Of Quasi-static Ultrasound Elastography Algorithms Based On RF Signals

Cancer has been always threatening human's health,and early detection and treatment can greatly improve patient survival.Many cancers have no physiological or pathological changes in their early stages,only changes in tissue stiffness.While traditional medical imaging techniques such as ultrasound imaging X-ray,ultrasonography,magnetic resonance imaging and computed tomography can only obtain structure of tissue and cannot provide direct information on the hard or soft tissues.Elastography algorithm can directly provide the elastic information of tissues,and it has attracted wide attention since it was proposed.Ultrasound elastography is non-invasive,simple and inexpensive.Due to these advantages,it has been widely used in clinic and become a hot research in the field of medical imaging.According to different types of excitation,ultrasound elastography can be divided into two main categories: quasi-static ultrasound elastography and dynamic ultrasound elastography.Quasi-static ultrasound elastography is widely used because of high resolution,real-time and without the need to additionally improve the traditional ultrasound equipment.At present,quasi-static ultrasound elastography algorithms still face some challenges: the problem that incorrect matching points lead to poor elastography,real-time problem,and the problem that window coefficients cannot be adaptively organized.In this paper,the principle of ultrasound elastography algorithm is analyzed in detail.The acquisition and processing of ultrasonic RF signal,several common displacement and strain estimation algorithms are introduced.In order to solve the problem of poor imaging results caused by incorrect matching points in quasi-static ultrasound elastography algorithm,some improved elastography algorithms based on mutual information are proposed in this paper.The mutual information and phase difference are fused to form a new elastography algorithm(algorithm 1),and then the method is combined with dynamic programming(algorithm 2).For the real-time problem of the ultrasound elastography,the algorithm 2 is implemented by multi-threading.Algorithm 2 has a large computational complexity,in which the core steps are relatively independent,so CUDA-based parallel computing is adopted.Aiming at the problem that window parameters cannot be adaptively with the different tissue,this paper proposes two elastography algorithms with adaptive window parameters based on particle swarm optimization algorithm.One is based cross-correlation with particle swarm optimization algorithm(algorithm 3)and the other is based on improved mutual information combined with particle swarm optimization algorithm(algorithm 4).In this paper,experiments are designed to verify the four proposed algorithms,and the cross-correlation-based elastography,dynamic programming-based elastography and different fixed window parameters are used to conduct the control experiments.The experimental results show that the algorithm 1 and the algorithm 2 have better imaging quality than the traditional elastography algorithms.Algorithms 3 and algorithm 4 are the attempts of elastography algorithm with variable window parameters.The experimental results show that different imaging algorithms and different organizations require different suitable window parameters for imaging.The elastic imaging algorithm with adaptive window length changes the window parameters with the changes of organization data,which can better realize the elastography.