The acoustic posterior shadowing effects of bubbles influence the accuracy for defining the location and range of ablated thermal lesions during focused ultrasound surgery when using ultrasonic monitoring imaging. This paper explored the feasibility of using Nakagami distribution to evaluate the ablated region induced by focused ultrasound exposures at different acoustic power levels in transparent tissue-mimicking phantoms. The mean value of the Nakagami parameter m was about 0.5 in the cavitation region and increased to around 1 in the ablated region. Nakagami images were not subject to significant shadowing effects of bubbles. Ultrasound-induced thermal lesions observed in the photos and Nakagami images were overshadowed by bubbles in the B-mode images. The lesion size predicted in the Nakagami images was smaller than that predicted in the photos due to the sub resolvable effect of Nakagami imaging at the interface. This preliminary study on tissue-mimicking phantom suggested that the Nakagami parameter m may have the potential use in evaluating the formation of ultrasound-induced thermal lesion when the shadowing effect of bubbles is strong while the thermal lesion was small. Further studies in vivo and in vitro will be needed to evaluate the potential application. (C) 2012 Acoustical Society of America. [http://dx.doi.org/10.1121/1.4711005]

Acoustic vortices provide a single-beam approach to manipulate objects with sizes from nanometers to millimeters, gaining increasing interest in recent years. The helical beam renders them good ability to trap particles in three dimensions stably. At the same time, the orbital angular momentum of acoustic vortices can be used to realize object rotation and data transmission. In this review, we summarize the generation and characterization of acoustic vortices. Furthermore, we present the application of acoustic vortices in particle manipulation, object rotation, acoustic communication, and especially in the biomedical field. Finally, perspectives on the future directions for acoustic vortex research are discussed.

Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists. Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules. Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460). Results: Nineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning- and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79–0.92], specificity 0.85 [95% CI 0.77–0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91–56.20]; deep learning: sensitivity 0.89 [95% CI 0.81–0.93], specificity 0.84 [95% CI 0.75–0.90], DOR 40.87 [95% CI 18.13–92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78–0.93] vs. 0.87 [95% CI 0.85–0.89], specificity 0.85 [95% CI 0.76–0.91] vs. 0.87 [95% CI 0.81–0.91], DOR 40.12 [95% CI 15.58–103.33] vs. DOR 44.88 [95% CI 30.71–65.57]). Conclusions: The CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.