Improved Nondestructive Ultrasound Molecular Imaging With Lightweight Convolutional Neural Network

Ultrasound molecular imaging (USMI) is an imaging approach that utilizes targeted microbubbles (MBs) to highlight biomarkers of disease. While differential targeted enhancement (DTE) is the current state-of-the-art for USMI, its reliance on destructive pulses hinders real-time clinical application. We have developed a neural network-based nondestructive USMI, validated in vivo using a transgenic mouse model of spontaneous breast cancer. To enhance training efficacy despite a limited animal number (N=14), we utilized several augmentation strategies including the use of several targeted MB types for each animal to generate independent image and texture patterns, alternative DTE approaches (sham and injection DTE), and random patch selection, overall resulting in a total of 15,350 patches to train the network. The resulting nondestructive USMI produces an image of the pixelwise MB classification score of the presence of targeted MBs. Our nondestructive USMI achieved a correlation coefficient of 0.954 with DTE, a continuous dice coefficient of 0.863 for a molecular signal coverage of the lesion over 20%, and a higher AUC than DTE (0.954 vs. 0.845) compared to the reference image developed from the contrast enhanced ultrasound (CEUS) image and manual lesion contour. Nondestructive imaging during continuous motion of the transducer under elevation sweeps yielded fewer artifacts and higher AUC than DTE (0.953 vs. 0.892), compared to the reference image. This demonstrates the potential of free-hand and real-time nondestructive imaging. Overall, nondestructive imaging showed comparable performance to DTE under stationary conditions and superior performance to DTE under transducer motion, indicating its clinical imaging potential.