Research Of Medical Image Analysis And Dynamic Simulation Method In HIFU Therapy

High Intensity Focused Ultrasound(HIFU)therapy capitalizes on two properties of ultrasound,tissue penetration and deposition,by externally focusing ultrasound on tumor tissue.HIFU performs treatment by heat-ablating the target tumor tissue.Because of the safety and efficacy,HIFU has been increasingly applied to the treatment of cancerous growths,such as uterine fibroids,breast fibroadenoma,hepatocellular carcinoma(HCC)etc.However,during HIFU therapy,doctors need to mark the target region each time which leads to low efficient and time-consuming treatment.Moreover,the target tissue such as a tumor often moves and/or deforms during ultrasound-guided HIFU therapy because of the existence of an external force or respiration.As a result,HIFU may appear to be targeting diseased tissue when in fact it is impinging on healthy tissue,leading to serious complications.Therefore,it is needed to find effective solutions that improve both efficiency and safety of the HIFU therapy.To address the above two problems,this dissertation develops research of medical image analysis and dynamic simulation method in HIFU therapy,including fast and accurate ultrasound image segmentation,physics-based modeling and multi-phase coupling of fluid,elastic body and rigid body and the prediction of target tissue's dynamic boundary.The main works are carried out as follows.An accurate and efficient multi-scale and shape constrained localized region-based active contour model,called the MSLCV model,has been proposed to perform the segmentation of uterine fibroid in ultrasound images for HIFU therapy.By incorporating a new shape constraint into the localized region-based active contour,we have obtained a more precise segmentation result,avoiding the problems of boundary leakage and excessive contraction due to the low SNR,weak boundaries and intensity inhomogeneity of HIFU ultrasound images.Further,to overcome the shortcomings of the large computation time and the time-consuming nature of the segmentation process in the localized region-based active contour model,a multi-scale algorithm has been proposed that greatly improves the segmentation efficiency.Meanwhile,to solve the problem of the selection of localizing radius and initialization sensitivity,we have discussed and analyzed the adaptive selection of the localizing radius and the formation of a zero narrow band.Experimental results deomonstrate the MSLCV model provides accurate and efficient segmentation results.To solve the problems in segmenting ultrasound image of uterine fibroids during HIFU therapy,we propose an adaptive localized region-based fast active contour model by introducing HIFU image's global information in local region,named AGL-MSLCV model,which is more accurate as well as more efficient than MSLCV.The AGL-MSLCV model incorporates HIFU ultrasound image's global information in local region to form a locally global force.Meanwhile the gray level distribution uniformity around each pixel point on the evolution curve is calculated to dynamically determine the various application condition of HIFU image's global information in local region and the shape constrained information of the uterine fibroids in HIFU images,which is assigned to overcome the sensitivity of the initialized contour by applying the locally global force when segmenting HIFU ultrasound image of uterine fibroids.By using the calculated gray level distribution uniformity around each pixel point on the evolution curve,the adaptive localized region-based fast active contour model adaptively changes the local radius of the localized region,and then dynamically adjusts the size of localized region during the evolution process of the active contour curve,achieving more accurate and more efficient segmentation results of HIFU ultrasound image of uterine fibroids.By applying the same localized region to calculate the local forces of adjacent pixel points on the evolution curve,our method further improves the segmentation efficiency,finally achieving accurate and efficient segmentation of HIFU ultrasound image of uterine fibroids.The experimental results show that,compared with recently proposed MSLCV model,AGL-MSLCV model overcome the sensitivity of the initialized contour and improves the segmentation accuracy and increase the average segmentation efficient by 84.6%.To address the needs in dynamic simulation in HIFU therapy,we present a novel method that adaptively sampled agent particles for real-time coupling TLED nonlinear FEM solid with IISPH fluid.The agent particles were sampled with different resolution according to the local Gauss curvatures of solid surface model.The agent particles' support domain was designed as ellipsoid to avoid premature coupling between agent particles and fluid particles.To avoid penetration of fluid particles on boundaries in case of deformation,the agent particles were adaptively resampled after deformation of nonlinear FEM solid and its support domain was adaptively changed.To accurately handle the coupling,regularization method was adopted to calculate the particles' physical quantities on the boundary to obtain better coupling effects.The CUDA-based GPU parallel computing w utilized to accelerate the computation of coupling process between agent particles and fluid particles.The experimental results demonstrate that the proposed method can achieve significant improvement in time and memory,reducing the sampling particles and meanwhile obtaining real-time realistic fluid-solid coupling effect.We present a prediction framework for target tissue's dynamic boundary based on image analysis and multiphase coupling uring ultrasound-guided HIFU therapy.To accomplish this goal,we collect spatial sequences of ultrasound images of target tissue using the HIFU equipment and propose the GLCV model by utilizing edge information and adopting a localized active contour model to segment HIFU images effectively.The segmentation results are then used to reconstruct a 3D model of the target tissue.To predict the displacement and deformation that the target tissue undergoes when exposed to the external force,we propose a unified particles-based multiphase coupling method to model the environment of the target tissue undergoes external force and calculate the displacement and deformation of the target tissue.We estimate the essential parameters of the multiphase coupling model and validate the proposed method using the acquired experiment data.The experimental results show that the proposed target tissue dynamic boundary prediction framework can provode relatively accurate dynamic boundaries which lays the foundation for prediction of human tumor's dynamic boundaries in clinic HIFU therapy.