Johns Hopkins University

Muyinatu Bell

Emad M Boctor

Peter Kazanzides

Austin Reiter

Alycen Wiacek

A computer-implemented method for training and using a neural network to predict a coherence function includes: training a neural network by mapping a plurality of different sets of training input samples to respective coherence function truths to generate a trained neural network; receiving an operational input sample; inputting the operational input sample into the trained neural network; obtaining, from the trained neural network, a coherence function mapped to the operational input sample in response to the inputting the operational input sample into the trained neural network; and executing a computer-based instruction based on obtaining the coherence function. The coherence function may be used to differentiate solid masses from fluid-filled masses.

Using machine learning techniques to obtain coherence functions

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.

Machine learning approach to beamforming

The present invention is directed to a method and system for photoacoustic imaging for guiding medical procedures. A transducer is placed near the site of the procedure. The optical fiber, coupled to an electromagnetic source, such as a laser, is attached to a medical device. During the procedure, the device and optical fiber are inserted into the procedure site where the optical fiber illuminates the procedure site, which has a thickness of approximately 2 mm. Photoacoustic images are acquired to visualize the procedure site as the procedure is proceeding in order to provide real-time guidance. This system is applicable to multiple surgical and interventional procedures, such as transsphenoidal surgery.

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Towson, MD, United States of America

Muyinatu Bell