Noise suppression and motion estimation in medical ultrasound imaging

Echocardiographic imaging is a primary modality in the diagnosis of heart disease. Compared to other imaging techniques, such as X-Ray, MRI, and PET, ultrasound imaging owes its great popularity to the fact that it is a safe and non-invasive procedure for visualizing the heart and vasculature. The ultrasound image however is corrupted by speckle, which is distinguished from Gaussian noise by its signal-dependent nature. This dissertation focuses on two important issues for the clinical applications of medical ultrasound images: speckle suppression and motion estimation. The dissertation first describes the statistics of speckle and ultrasound image models, which are important for performance evaluation and further algorithm development. Secondly, a novel speckle suppression approach is developed for the purpose of visualization enhancement and auto-segmentation improvement. This method is designed to utilize the favorable denoising properties of two frequently used techniques: wavelet and nonlinear diffusion. Speckle is iteratively reduced by the multiscale nonlinear diffusion via the framework of dyadic wavelet transform. With a noise adaptive feature, our algorithm is versatile for both envelop-detected and log-compressed ultrasound images. We validate our method using synthetic speckle images and real ultrasonic images. Performance improvement over other despeckling filters is quantified in terms of the quality indices. In summary, our algorithm provides very significant speckle suppression and edge enhancement for the purposes of visualization and automatic structure detection. We further extend the ultrasound statistical knowledge into the motion estimation, and develop a speckle tracking algorithm for myocardial wall motion estimation in intracardiac echocardiographic images. To achieve robust noise resistance, we employ maximum likelihood estimation while fully exploiting ultrasound speckle statistics, and treat the maximization of motion probability as the minimization of an energy function. Non-rigid myocardial deformation is estimated by optimizing this energy function within a framework of elastic registration. Accuracy of the method is evaluated by using a computer model and an animal model, which provides continuous intracardiac echocardiographic images as well as reference measurements for myocardial deformation. As a result, our approach achieves an accurate estimation of regional myocardial deformation from intracardiac echocardiography. This approach has important clinical implications for multimodal imaging during catheterization