Sparse signal separation with an off-line learned dictionary for clutter reduction in echocardiography

Clutter is an artifact in cardiac ultrasound that obscures parts of the heart. A cluttered signal is seen as a superposition of tissue, clutter and noise components. In this work, we introduce two novel methods for reducing clutter by separating these components using Morphological Component Analysis, where each component has a sparse representation under some dictionary. The clutter dictionary is trained using data acquired from the right side of the chest, overcoming any assumption about the clutter behavior. The tissue dictionary is trained from off-line tissue data in one method, and adaptively from the patient data in the other. These methods are shown to be robust to the input data characteristics and yield state-of-the-art performance.