The present invention provides an image display method with which a structure of an imaging object can be easily understood on the basis of volume data. An image display method according to an aspect of the present invention includes obtaining photoacoustic image data, generating a first photoacoustic image corresponding to a first spatial region on the basis of photoacoustic image data and a second photoacoustic image corresponding to a second spatial region having a different thickness in a viewing direction of rendering from a thickness of the first spatial region and having a spatial region overlapped with the first spatial region on the basis of the photoacoustic image data, and generating and displaying a parallel image in which the first photoacoustic image and the second photoacoustic image are arranged side by side.

An ultrasound imaging method includes providing a digital representation of the shape of a surface or boundary of an anatomic region or organ; acquiring an ultrasound image by ultrasound scanning the anatomic region or organ; and combining the digital representation of the shape of the surface or boundary of the anatomic region or organ by registering the digital representation of the shape of the surface or boundary and the ultrasound image as a function of the difference in position of selected reference points on the digital representation of the surface or boundary and on the ultrasound image, the position of the reference points on the ultrasound image being determined by tracking the probe position at the reference points at the anatomic region or organ of a real body and in a spatial reference system, in which the anatomic region or the organ of the real body is placed.

A photoacoustic tomography system includes a first ring-shaped mirror having a central axis therethrough and configured to converge light inwardly towards the central axis and a subject, and an adjustment mechanism configured to move the first ring-shaped mirror along the central axis to a plurality of different positions. Each position of the plurality of different positions allows the first ring-shaped mirror to illuminate a respective ring of light around a respective portion of the subject, and an acoustic signal detector is movable along the central axis such that acoustic signals can be detected from the respective portion of the subject when illuminated by the first ring-shaped mirror while at each respective position of the plurality of different positions.

During acquisition of an ultrasound image feed, ultrasound control data frames are acquired that may be interspersed amongst the ultrasound data frames. The control data frames may use consistent reference scan parameters, irrespective of the scanner settings, and may not need to be converted to image frames. The control data frames can be passed to an artificial intelligence model, which predicts the suitable settings for scanning the anatomy that is being scanned. The artificial intelligence model can be trained with a dataset containing different classes of ultrasound control data frames for different settings, where substantially all the ultrasound control data frames in the dataset are consistently acquired using the reference scan parameters.

The wave number and phase velocity (shear wave speed) of ultrasound energy within an organ of interest are calculated using a herein-disclosed phase gradient calculation method. This calculation method is less sensitive to imperfections in the reverberant field distribution and requires a smaller support window, relative to earlier calculation methods based on autocorrelation. Applications are shown in simulations, phantoms, and in vivo liver.

The method includes excitation during which an internal mechanical stress is generated in an excitation zone, and imaging by acquiring signals during movements generated by the mechanical stress in the visco-elastic medium in response to the internal mechanical stress in an. imaging zone that includes the excitation zone. The method further includes calculating a quantitative index associated with the rheological properties of the visco-elastic medium at least at one point of the imaging zone situated at a given depth outside the excitation zone. The quantitative index is representative of a comparison between signals acquired during the movements generated in response to the mechanical stress at least at one point of the excitation zone situated at the given depth, and signals acquired during the movements generated in response to the mechanical stress at at least the point of the imaging zone situated outside the excitation zone.

An imaging member including: a pressing member that presses a breast of a subject; and an ultrasonography member that has a first surface on which an acoustic matching member having fluidity is provided, and is provided such that a second surface on a side opposite to the first surface is provided on a surface of the pressing member, which is on a side opposite to a surface that comes into contact with the breast, via a coupling material having lower fluidity than the acoustic matching member.

Systems and methods for mapping a region of interest within breast tissue utilize multiple layers of information to produce a unique digital fingerprint of breast tissue. X-ray and ultrasound imaging is combined with elastography and Doppler to create an architectural map of a breast including coordinates to mark one or more regions of interest. The architectural map can be utilized during future imaging procedures and surgeries to automatically and virtually indicate the location of previously biopsied lesions. The architectural map can be displayed on a user interface of a computing device to guide a user to the region of interest during imaging.

A method and system for creating a dynamic self-learning medical image network system, wherein the method includes receiving, from a first node initial user interaction data pertaining to one or more user interactions with the one or more initially obtained medical images; training a deep learning algorithm based at least in part on the initial user interaction data received from the node; and transmitting an instance of the trained deep learning algorithm to the first node and/or to one or more additional nodes, wherein at each respective node to which the instance of the trained deep learning algorithm is transmitted, the trained deep learning algorithm is applied to respective one or more subsequently obtained medical images in order to obtain a result.

A method for characterizing tissue of a patient, including receiving acoustic data derived from the interaction between the tissue and the acoustic waves irradiating the tissue; generating a morphology rendering of the tissue from the acoustic data, in which the rendering represents at least one biomechanical property of the tissue; determining a prognostic parameter for a region of interest in the rendering, in which the prognostic parameter incorporates the biomechanical property; and analyzing the prognostic parameter to characterize the region of interest. In some embodiment, the method further includes introducing a contrast agent into the tissue; generating a set of enhanced morphology renderings of the tissue after introducing the contrast agent; determining an enhanced prognostic parameter from the enhanced morphology renderings; and analyzing the enhanced prognostic parameter.