ON THE SONOELASTICITY AND SONIFICATION IMAGING THEORIES WITH APPLICATION TO COOPERATIVE SURGICAL ROBOTS

Abstract

Sonoelasticity imaging uses low-frequency vibrations in tissue and Doppler vibration pattern to detect small inhomogeneities in tissues. The sonification theory translates the tissue data into acoustic signals and reverses back the sound into images by solving an inverse problem. In contrast to sonoelasticity which is limited to low examination frequencies imposed by the high attenuation of acoustic waves, the sonification theory is extending the range of frequencies to areas of medical interest. In addition, sonoelasticity is limited to detection of only small inhomogeneities in tissue due to the Born approximation, while the sonification theory is extending to detect the tumors of different sizes. Fundamental theoretical considerations of both theories are reviewed in this paper with application to cooperative surgical robots. For implementing an effective control, detailed images of the tumor and its neighboring regions are required.