A feed-forward neural network algorithm to detect thermal lesions induced by high intensity focused ultrasound in tissue

Parisa Rangraz, Hamid Behnam, Naser Shakhssalim, Jahan Tavakkoli



Non-invasive ultrasound surgeries such as high intensity focused ultrasound have been developed to treat tumors or to stop bleeding. In this technique, incorporation of a suitable imaging modality to monitor and control the treatments is essential so several imaging methods such as X-ray, Magnetic resonance imaging and ultrasound imaging have been proposed to monitor the induced thermal lesions. Currently, the only ultrasound imaging technique that is clinically used for monitoring this treatment is standard pulse-echo B-mode ultrasound imaging. This paper describes a novel method for detecting high intensity focused ultrasound -induced thermal lesions using a feed forward neural-network. This study was carried on in vitro animal tissue samples. Backscattered radio frequency signals were acquired in real-time during treatment in order to detect induced thermal lesions. Changes in various tissue properties including tissue’s attenuation coefficient, integrated backscatter, scaling parameter of Nakagami distribution, frequency dependent scatterer amplitudes and tissue vibration derived from the backscattered radio frequency data acquired 10 minutes after treatment regarding to before treatment were used in this study. These estimated parameters were used as features of the neural network. Estimated parameters of two sample tissues including two thermal lesions and their segmented B-mode images were used along with the pathological results as training data for the neural network. The results of the study shows that the trained feed forward neural network could effectively detect  thermal lesions in vitro. Comparing the estimated size of the thermal lesion (9.6mm×8.5mm) using neural network with the actual size of that from physical examination (10.1mm×9mm) shows that we could detect high intensity focused ultrasound thermal lesions with the difference of 0.5mm×0.5mm.


HIFU lesion detection, Tissue mechanical and acoustical parameters, Nakagami distribution, Feed forward neural network.

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