Back to results
US 2026/0211111 A1Published application
Adaptive ultrasound beamforming
AssigneeUniversity of Leeds
InventorsElliott Ian James SMITH, Steven FREEAR
Life of this patent
Estimating the term…
Abstract
Ultrasound beamforming is performed by a deep learning architecture incorporating convolution layers and known operators. The network is trained to combine multiple observations of complex signal data in an optimal way into a single complex signal. This is achieved using convolutional layers with learnable parameters and by embedding and an operator to perform multiple weighted sum operations of subsets of the original observations and average these to produce a single complex signal. Known operators including a forward-backward operator are optionally incorporated into the architecture.
Claims10
A method of generating beam formed ultrasound signals, the method comprising:
- receiving a plurality of first electrical signals from respective ultrasound transducer elements, wherein each said ultrasound transducer element is adapted to generate a respective said first electrical signal in response to ultrasound received from a location in an object;
- processing the first electrical signals, said processing including applying predetermined delay profiles;
- inputting the processed first electrical signals into a trained artificial neural network, the trained artificial neural network including a plurality of convolutional layers;
- determining, in the trained artificial neural network, apodization weights for beam forming the ultrasound signals;
- and
- outputting, from the trained artificial neural network, a beam formed signal representing scattered ultrasound received from said location in said object on the basis of a plurality of second electrical signals, wherein each said second electrical signal is based on a respective sum of products, wherein each said product is a product of a respective first input based on a respective processed first electrical signal and a respective second input based on a respective said apodization weight, and each said sum is based on less than all of said processed first electrical signals.
3 more elements
A method of providing a trained artificial neural network adapted to generate beam formed ultrasound signals, the method comprising:
- receiving input training data comprising a plurality of processed first electrical signals corresponding to a plurality of first electrical signals received from respective ultrasound transducer elements, wherein each said ultrasound transducer element is adapted to generate a respective said first electrical signal in response to ultrasound received from a location in an object, and wherein the processed first electrical signals correspond to said first electrical signals after application of predetermined delay profiles;
- receiving output training data comprising at least one of apodization weights and a plurality of output electrical signals determined by means of an adaptive beam forming method;
- and
- training the artificial neural network by using the input training data and the output training data;
- wherein the artificial neural network includes a plurality of convolutional layers and at least one beamforming operator layer;
- and
- wherein the artificial neural network is adapted to output a beam formed ultrasound signal representing scattered ultrasound received from said location in said object on the basis of a plurality of second electrical signals, wherein each said second electrical signal is based on a respective sum of products, wherein each said product is a product of a respective first input based on a respective processed first electrical signal and a respective second input based on a predetermined apodization weight, and each said sum is based on less than all of said processed first electrical signals.
4 more elements
A system for beam forming of ultrasound signals, the system comprising:
- an input portion for receiving a plurality of first electrical signals from respective ultrasound transducer elements, wherein each said ultrasound transducer element is adapted to generate a respective said first electrical signal in response to ultrasound received from a location in an object;
- processor means for processing the first electrical signals and for applying a trained artificial neural network to the processed first electrical signals, the neural network including a plurality of convolutional layers and at least one beamforming operator layer, wherein the processor means is adapted to:
- process the first electrical signals by applying predetermined delay profiles;
- input the processed first electrical signals into the trained artificial neural network;
- determine apodization weights for beam forming the ultrasound signals;
- output a beam formed signal representing scattered ultrasound received from said location in said object on the basis of a plurality of second electrical signals, wherein each said second electrical signal is based on a respective sum of products, wherein each said product is a product of a respective first input based on a respective processed first electrical signal and a respective second input based on a respective said apodization weight, and each said sum is based
- on less than all of said processed first electrical signals;
- and
- an output portion for outputting the beam formed ultrasound signal.
6 more elements
Dependencies are inferred from the claim text.