Invention Grant
- Patent Title: Machine learning approach to beamforming
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Application No.: US15852106Application Date: 2017-12-22
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Publication No.: US11832969B2Publication Date: 2023-12-05
- Inventor: Muyinatu Bell , Austin Reiter
- Applicant: THE JOHNS HOPKINS UNIVERSITY
- Applicant Address: US MD Baltimore
- Assignee: The Johns Hopkins University
- Current Assignee: The Johns Hopkins University
- Current Assignee Address: US MD Baltimore
- Agency: JOHNS HOPKINS TECHNOLOGY VENTURES
- Main IPC: A61B5/00
- IPC: A61B5/00 ; A61B8/08 ; G06N3/08 ; A61B8/00 ; G06T5/00 ; G06N3/084 ; G06N3/045 ; G06N20/00 ; A61B34/20

Abstract:
An embodiment according to the present invention includes a method for a machine-learning based approach to the formation of ultrasound and photoacoustic images. The machine-learning approach is used to reduce or remove artifacts to create a new type of high-contrast, high-resolution, artifact-free image. The method of the present invention uses convolutional neural networks (CNNs) to determine target locations to replace the geometry-based beamforming that is currently used. The approach is extendable to any application where beamforming is required, such as radar or seismography.
Public/Granted literature
- US20180177461A1 MACHINE LEARNING APPROACH TO BEAMFORMING Public/Granted day:2018-06-28
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