A Machine Learning Approach to Beamforming

Case ID:
C14467
Disclosure Date:
11/20/2016
Description:

Unmet Need
Given their ability to aid in real-time navigation of our complex anatomy, ultrasound and photoacoustic images are popular options for image guidance during surgery. However, noise in these images complicate surgical procedures, particularly noise caused by reflection and reverberation artifacts from highly echogenic structures, which often appear as true signals. If unidentifiable as noise rather than true signals, these artifacts cause irreversible damage to surrounding structures such as nerves and major blood vessels, leading to complications such as paralysis and patient death. Currently, none of the widely utilized ultrasound and photoacoustic image formation techniques account for these problematic reflection artifacts, which can arise from multiple factors that are intimately linked, yet difficult to model based on acoustic propagation theories alone.
 

Technology Overview
The technology takes a revolutionary machine learning-based approach to the formation of ultrasound and photoacoustic images. The approach is extendable to multiple applications where beamforming is required (e.g. radar, seismology).
 

Stage of Development
Developed method for removing artifacts in imaging by leveraging state-of-the-art machine learning techniques.

Patent Information:
Title App Type Country Serial No. Patent No. File Date Issued Date Expire Date Patent Status
A Machine Learning Approach to Beamforming PRO: Provisional United States 62/437,941 12/22/2016     Expired
A Machine Learning Approach to Beamforming ORD: Ordinary Utility United States 15/852,106 12/22/2017     Pending
Inventors:
Category(s):
For Information, Contact:
Jon Gottlieb
jgottl10@jhu.edu
410-614-0300
Save This Technology:
2017 © Johns Hopkins Technology Ventures. All Rights Reserved. Powered by Inteum