Ultrasonic Nakagami imaging for automatically positioning and identifying the treated lesion induced by histotripsy

Histotripsy has been proposed as a non-invasive surgical procedure for clinical use that liquefies the tissue into acellular debris by utilizing the mechanical mechanism of bubbles. Accurate and reliable imaging guidance is essential for successful clinical histotripsy implementation. Nakagami imaging is a promising method to evaluate the microstructural change induced by high intensity focused ultrasound. However, practically, it is difficult for the Nakagami imaging to distinguish the treated lesion induced by histotripsy from the surrounding normal biological tissues. In this study, we introduce the use of noise-assisted correlation algorithm (NCA) in Nakagami images as a solution to suppress the background normal tissue and identify the treated lesion induced by histotripsy. Experiments are conducted on fresh porcine liver ex vivo by cavitation-cloud histotripsy. Results show that the contrast-to-noise ratio between the treated lesion and surrounding tissue corresponding to the Nakagami image after NCA and original Nakagami image is 3.434 and 0.505, respectively. The optimal artificial noise level is 1-fold of the background normal tissue amplitude, and the corresponding optimal threshold of correlation coefficient should be between 0.6 and 0.8 in the application of NCA. Therefore, the use of NCA in Nakagami image can suppress the background normal tissues without affecting the information of treated lesion for an appropriate artificial noise level and threshold used in the NCA. Moreover, the Nakagami images after the application of the NCA can also be used for automatically distinguishing and measuring the tissue fractionation accurately using binarization. The proposed Nakagami images overlaid on the B-mode images can provide a promising method for positioning and visualizing the treated lesion to achieve precise histotripsy treatment.