Abstract: A system and methods for machine learning driven contouring of cardiac ultrasound data is provided. A system may receive cardiac imaging data generated by an ultrasound device directed at a left ventricle of a heart. The imaging data may include a time dimension and a plurality of spatial dimensions. The system may align the image data with an axis that passes through an apex and base of the left ventricle. The system may predict, based on a machine learning model and the imaging data, locations of a plurality of anchor points over a cardiac cycle. The system may output the predicted locations of the anchor points.

Abstract: Disclosed herein is a photoacoustic tomography (PAT) probe to direct light to various regions within the tissue of interest to improve image quality and remove cumbersome artifacts at their source. Particularly, a rotating PAT probe and method of using thereof to improve the photoacoustic penetration depth and signal to noise ratio in biological samples. Signal intensity at region of interest is increased by fine-tuning the fiber orientation with respect to the ultrasound transducer. Additional PA filter is used to prevent in vivo probe-skin artifacts.

Abstract: Disclosed herein is a photoacoustic tomography (PAT) probe to direct light to various regions within the tissue of interest to improve image quality and remove cumbersome artifacts at their source. Particularly, a rotating PAT probe and method of using thereof to improve the photoacoustic penetration depth and signal to noise ratio in biological samples. Signal intensity at region of interest is increased by fine-tuning the fiber orientation with respect to the ultrasound transducer. Additional PA filter is used to prevent in vivo probe-skin artifacts.