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.

Abstract: 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.

Abstract: A may comprise a first optical fiber and an optical interface connected to the first optical fiber. The optical interface may be configured to transfer laser light from the first optical fiber to a second optical fiber in a drill bit. The second optical fiber may propagate the laser light to a face of the drill bit to create an ultrasonic emission.

Abstract: 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.

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.

Abstract: 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.