Nondestructive Targeted Microbubble Detection Using a Dual-Frequency Beamforming Deep Neural Network

Targeted microbubbles (MBs)enable ultrasound molecular imaging (USMI)by binding to specific biomarkers and producing strong reflections to ultrasound. However, current USMI techniques are not easily translatable for clinical use. In particular, preclinical studies often utilize destruction-subtraction imaging, wherein a strong destructive pulse is used to destroy MBs to confirm their locations. This approach is potentially unsafe, and is intrinsically not real-time. In this work, a novel neural-network-based beamformer is proposed for the purpose of achieving safe and real-time USMI. The network was designed to utilize nondestructive channel data acquired at two distinct frequencies and to produce a pixel-wise estimate of MB probability. The network was trained using a total of 20 USMI datasets acquired in a mouse model of hepatocellular carcinoma and in microvessel flow phantoms. The network was then evaluated on 5 distinct datasets: a positive control, a negative control, and three previously unseen mouse tumors. Across the 5 datasets, the neural network achieved a mean AUC of 0.91 and DC of 0.56 compared to the destruction-subtraction images. These results demonstrate that a neural network can nondestructively distinguish MBs from background tissue and noise by exploiting characteristic differences in their fundamental and harmonic responses. The nondestructive dual-frequency DNN beamformer enables safe and real-time USMI and can aid in the translation to clinical applications.