Exploring tissue characterization by temperature induced changes on ultrasound backscattered energy

Abstract

Ultrasounds are widely used in medical imaging due to the fact of being non-invasive, relatively cheap, flexible and to allow real-time acquisitions. However it has some disadvantages, such as the moderate resolution and the low image contrast that greatly limits the image quality comparing to techniques as magnetic resonance or computed tomography. This lead, in the last years, to the development of multiple methodologies in an effort to provide traditional ultrasound images with better quality and amount of information. Example are the cases of elastography and Doppler Color Flow Imaging that provide information about tissues rigidity and the blood flow, respectively. Literature reports monotonic changes in the backscattered energy (CBE) with the increasing of medium temperature. This changes are characteristics of the type of scatterers present in the medium: scattering media composed by scatterers of muscular nature show a decreasing CBE with temperature, unlike scattering media composed by scatterers of lipid nature that show the inverse behavior. This properties where previously studied in order to map medium temperature during ultrasound therapy. This work aims to the study of a new methodology for non-invasive tissue characterization to potentially provide conventional images with additional information about media. This methodology is based in CBE expressed on intensity variations in conventional ultrasound images when the medium is submitted to a temperature increment. It was possible, using simulated ultrasound signals and images, with Matlab toolbox K-Wave, to develop the new methodology whose results are consistent with theoretical models present in Straube and Arthur studies, that modulated the expected backscattered energy received by a tissue volume. With data acquired ex-vivo, i.e., using samples of porcine muscle and fat, it was possible to obtain distinct CBE signatures for each one of them. These results could be the starting point of a new simple and cheap method for non-invasive tissue classification.