Abstract: Ultrasound B-mode images are reconstructed directly from transducer channel signals using a convolutional neural network (CNN). The CNN is trained with a dataset including, as inputs, simulated transducer array channel signals containing simulated speckle and, as outputs, corresponding simulated speckle-free B-mode ground truth images. After training, measured real-time RF signals taken directly from an ultrasound transducer array elements prior to summation are input to the CNN and processed by the CNN to generate as output an estimated real-time B-mode image with reduced speckle.

Abstract: A method of nondestructively detecting targeted contrast agents in real-time is provided that includes using a neural network (NN) beamformer, where an input of the NN includes ultrasound transducer channel data from a dual-frequency pulse-echo acquisition from a medium that may contain targeted contrast agents, where an output of the NN is an image of pixel-wise probability of the targeted contrast agent presence, where the NN nondestructively distinguishes the targeted contrast agent from tissue and noise by exploiting characteristic differences in responses of the targeted contrast agent versus responses from the tissue and noise present in the channel data of the dual-frequencies, where the NN is trained to operate according to destructive-subtraction ultrasound molecular imaging datasets that are used as a ground truth.

Abstract: Ultrasound B-mode images are reconstructed directly from transducer channel signals using a convolutional neural network (CNN). The CNN is trained with a dataset including, as inputs, simulated transducer array channel signals containing simulated speckle and, as outputs, corresponding simulated speckle-free B-mode ground truth images. After training, measured real-time RF signals taken directly from an ultrasound transducer array elements prior to summation are input to the CNN and processed by the CNN to generate as output an estimated real-time B-mode image with reduced speckle.