high‑intensity focused ultrasound lesion detection using adaptivecompressive sensing based on empirical mode decomposition

Background: the main goal of ultrasound therapy is to have clinical effects in the tissue without damageto the intervening and surrounding tissues. treatments have been developed for both in vitro and in clinicalapplications. hifu therapy is one of these. non-invasive surgeries, such as hifu, have been developedto treat tumors or to stop bleeding. in this approach, an adequate imaging method for monitoring andcontrolling the treatment is required. methods: in this paper, an adaptive compressive sensing representationof ultrasound rf echo signals is presented based on empirical mode decomposition (emd). according tothe different numbers of intrinsic mode functions (imfs) produced by the emd, the ultrasound signals isadaptively compressive sampled in the source and then adaptively reconstructed in the receiver domains.in this paper, a new application of compressive sensing based on emd (cs-emd) in the monitoring ofhigh-intensity focused ultrasound (hifu) treatment is presented. non-invasive surgeries such as hifuhave been developed for various therapeutic applications. in this technique, a suitable imaging method isnecessary for monitoring of the treatment to achieve adequate treatment safety and efficacy. so far, severalmethods have been proposed, such as ultrasound radiofrequency (rf) signal processing techniques, andimaging methods such as x-ray, mri, and ultrasound to monitor hifu lesions. results:in this paper, acs-emd method is used to detect the hifu thermal lesion dimensions using different types of wavelettransform. the results of the processing on the real data demonstrate the potential for this technique inimage-guided hifu therapy. conclusions: in this study, a new application of compressive sensing in thefield of monitoring of the hifu treatment is presented. to the best of our knowledge, so far no studies oncompressive sensing have been carried out in the monitoring of the hifu. based on the results obtained,it was showed that the number of measurements and intrinsic mode functions have the function of noisereduction. in addition, results were shown that the successful reconstruction of the compressive sensingsignals can be gained using a threshold based algorithm. to this end, in this paper it was shown that byselecting an suitable number of measurements, the sparse transform, and a thresholding algorithm, we canachieve a more accurate detection of the hifu thermal lesion size.