Key Problems In HIFU Treatment Effect Real-Time Estimation Based On Ultrasound Image

Noninvasive Estimation of Treatment Effect (NETE) is a problem that needs to be resolved in High Intensity Focused Ultrasound (HIFU). It is an advanced interdisciplinary topic which involves ultrasonics, biomedicine, computer science, intelligence information processing, digital image processing, mathematics, etc. It has a great application requirement.Since current schemes of NETE are difficult to estimate the treatment effect in real-time, a scheme that is based on ultrasound image is proposed for NETE. Three main problems in NETE have been studied. They are: 1) reducing the speckle noise in ultrasound image; 2) noninvasive temperature estimation for HIFU; 3) noninvasive detection of lesion in tissue.In order to reduce speckle noise in multiple copy ultrasound images, a method that is based on independent component analysis is proposed. The original image signal and speckle noise signal are separated by this method after supposing that the original image and noise are mutually independent in multiple copy images. Experimental results show that the proposed method can restrain effectively the speckle noise in ultrasound image.In tissue, temperature changes induced by HIFU exposures result in gray variation of the B-mode ultrasonic image that can be used to estimate temperature. Thus, a method is proposed to noninvasively estimate temperature based on Real-valued Discrete Gabor Transform (RDGT). The difference image is decomposed to two types of information using this method: the information that is correlated to temperature, and the other information, such as noise. In-vitro fresh pork and liver tissue are used in experiment. The experimental results show that temperature is approximately linear to energy of RDGT, and the new method has a high temperature resolution. The RDGT can be applied to estimate temperature for HIFU. Lesion in tissue, created by HIFU exposures, inevitably leads to some new interfaces in tissue and changes in B-mode ultrasound image. Feature of tissue lesion in difference-sub-image is extracted using Self-Organizing Maps (SOM) and the method combining Principal Component Analysis with Linear Discriminant Analysis (PCA-LDA), respectively. Fresh pork and liver in vitro are used in experiments. The experimental results show that the SOM method can get the best result for lesion recognition, the recognition rate of pork tissue and liver tissue are 96.1% and 91.7%, respectively. The recognition rate for PCA-LDA method is lower than that of SOM method because the feature of tissue lesion is linear indivisible.An effective clinic scheme is developed for NETE, and some problems are discussed. For example, nonuniform temperature field conduced by nonuniform energy field; tissue database construction; movement of HIFU transducer with ultrasonic transducer; rectification of HIFU parameters.