Regularisation With a Dictionary of Lines for Medical Ultrasound Image Deconvolution

Abstract

Lines and boundaries are important structures in medical ultrasound images as they can help differentiate between tissue types, organs, and membranes. A typical example is in lung ultrasonography, where the presence of so-called B-lines is indicative of lung status in ventilated critically ill patients or of fluid overload in patients on dialysis. In order to be able to quantify such linear features, deconvolution is typically necessary, in order to enhance the generally poor ultrasound image quality. This paper presents a novel deconvolution technique for restoring ultrasound images. Our approach employs a standard inverse problem formulation involving a penalty term for enforcing a sparse solution, but augmented with an additional term aimed at promoting linear features. Specifically, we regularise our solution using the Radon transform, which effectively acts as a dictionary of lines. The resulting optimisation problem can then be addressed using both con-vex and non-convex techniques. We evaluated our approach on real B-mode ultrasound images and our results show that the proposed method outperforms existing techniques by up to 30% in terms of contrast-to-noise ratio.