Compressive sensing ultrasound imaging using overcomplete dictionaries
The application of compressive sensing (CS) to medical ultrasound (US) imaging is a very recent field and the few existing studies mostly focus on fixed sparsifying transforms. In contrast to previous work, we propose a new approach based on the use of learned overcomplete dictionaries. Such dictionaries allow for much sparser representations of the signals since they are optimized for a particular class of images such as US images. In this study, the dictionary was learned using the K-SVD algorithm on patches extracted from the image to be reconstructed for an initial validation. Experiments were performed on experimental beamformed RF data acquired by imaging a general-purpose phantom. CS reconstruction was performed by removing 25% to 75% of the original samples according to a uniform law. Reconstructions using a K-SVD dictionary previously trained dictionary on experimental US images indicate minimal information loss, thus showing the potential of the overcomplete dictionaries.