Abstract: Disclosed herein systems, processors, or computer-readable media configured with instructions to: receive transmission and/or reflection images of a tissue of a subject, wherein the images are generated from acoustic signals derived from acoustic waveforms transmitted through the tissue; provide a set of prognostic parameters associated with a user selected region of interest; wherein the set of prognostic parameters comprises sound propagation metrics characterizing sound propagation within a tissue; wherein the set of prognostic parameters corresponds to inputs into a tissue classifier model; wherein the set of prognostic parameters comprises a plurality of subsets of related feature groupings; and determine a type of tissue of the subject based on said plurality of subsets of related feature groupings using the classifier model, wherein the type of tissue is a cancerous tumor, a fibroadenoma, a cyst, a nonspecific benign mass, and an unidentifiable mass.

Abstract: The present invention provides improved methods and systems for generating enhanced images of a volume of tissue.

Abstract: This application presents a system and related methods that analyze a volume of tissue using ultrasound waveform tomography imaging. By using frequency-domain waveform tomography techniques and a gradient descent algorithm, the system can reconstruct the sound speed distributions of a volume of tissue, such as breast tissue, of varying densities with different types of lesions. By allowing sound speed to have an imaginary component that characterizes sound attenuation, the system can classify the different types of lesions with a fine granularity.

Abstract: Disclosed herein systems, processors, or computer-readable media configured with instructions to: receive transmission and/or reflection images of a tissue of a subject, wherein the images are generated from acoustic signals derived from acoustic waveforms transmitted through the tissue; provide a set of prognostic parameters associated with a user selected region of interest; wherein the set of prognostic parameters comprises sound propagation metrics characterizing sound propagation within a tissue; wherein the set of prognostic parameters corresponds to inputs into a tissue classifier model; wherein the set of prognostic parameters comprises a plurality of subsets of related feature groupings; and determine a type of tissue of the subject based on said plurality of subsets of related feature groupings using the classifier model, wherein the type of tissue is a cancerous tumor, a fibroadenoma, a cyst, a nonspecific benign mass, and an unidentifiable mass.

Abstract: A method and system for generating an enhanced image of a volume of tissue, the method comprising: emitting acoustic waveforms toward the volume of tissue; detecting a set of acoustic signals derived from acoustic waveforms interacting with the volume of tissue; generating, from the set of acoustic signals, an initial model representing a distribution of an acoustomechanical parameter across a region of the volume of tissue; extracting a set of frequency components, from the set of acoustic signals; generating a first simulated wavefield with a first frequency component of the set of frequency components; generating an updated model of the initial model with the first simulated wavefield; iteratively refining the updated model with a set of simulated wavefields associated with the set of frequency components until a threshold condition is satisfied, thereby producing a final model; and generating the enhanced image from the final model of the volume of tissue.

Abstract: The present invention provides improved methods and systems for generating enhanced images of a volume of tissue.

Abstract: The present invention provides improved methods and systems for generating enhanced images of a volume of tissue.

Abstract: This application presents a system and related methods that analyze a volume of tissue using ultrasound waveform tomography imaging. By using frequency-domain waveform tomography techniques and a gradient descent algorithm, the system can reconstruct the sound speed distributions of a volume of tissue, such as breast tissue, of varying densities with different types of lesions. By allowing sound speed to have an imaginary component that characterizes sound attenuation, the system can classify the different types of lesions with a fine granularity.

Abstract: The present invention provides improved methods and systems for generating enhanced images of a volume of tissue.

Abstract: A method and system for generating an enhanced image of a volume of tissue, the method comprising: emitting acoustic waveforms toward the volume of tissue; detecting a set of acoustic signals derived from acoustic waveforms interacting with the volume of tissue; generating, from the set of acoustic signals, an initial model representing a distribution of an acoustomechanical parameter across a region of the volume of tissue; extracting a set of frequency components, from the set of acoustic signals; generating a first simulated wavefield with a first frequency component of the set of frequency components; generating an updated model of the initial model with the first simulated wavefield; iteratively refining the updated model with a set of simulated wavefields associated with the set of frequency components until a threshold condition is satisfied, thereby producing a final model; and generating the enhanced image from the final model of the volume of tissue.