Ultrasound speckle reduction and image reconstruction using deep learning techniques
AssigneeThe Board of Trustees of the Leland Stanford Junior University
InventorsDongwoon Hyun, Jeremy J. Dahl, Kevin T. Looby, Leandra L. Brickson
Life of this patent
Estimating the term…
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.
Claims11
A method for ultrasound image reconstruction using a convolutional neural network (CNN), the method comprising:
- (a) training the CNN with a dataset comprising simulated transducer array channel signals containing simulated speckle as inputs, and corresponding simulated speckle-free B-mode images as outputs;
- wherein the training uses a loss function involving a norm between estimated B-mode images and simulated speckle-free B-mode images;
- wherein the estimated B-mode images are estimated by the CNN from the simulated transducer array channel signals;
- (b) measuring real-time RF signals taken directly from ultrasound transducer array elements prior to summation;
- (c) inputting the measured real-time RF signals to the CNN;
- (d) processing by the CNN the measured real-time RF signals to produce as output an estimated real-time B-mode image with reduced speckle.
Dependencies are inferred from the claim text.