The present disclosure relates to a method for scanning a patient in an imaging system. The imaging system may include one or more cameras directed at the patient. The method may include obtaining a position of each of the camera(s) relative to the imaging system. The method may also include obtain image data of the patient captured by the camera(s), wherein the image data may correspond to a first view with respect to the patient. The method may further include generating projection image data of the patient based on the image data and the position of each of the camera(s) relative to the imaging system, wherein the projection image data may correspond to a second view with respect to the patient different from the first view. The method may further include generating control information for scanning the patient based on the projection image data of the patient.

The invention relates to a computer-implemented method for automatically detecting anatomical structures (3) in a medical image (1) of a subject, the method comprising applying an object detector function (4) to the medical image, wherein the object detector function performs the steps of: (A) applying a first neural network (40) to the medical image, wherein the first neural network is trained to detect a first plurality of classes of larger-sized anatomical structures (3a), thereby generating as output the coordinates of at least one first bounding box (51) and the confidence score of it containing a larger-sized anatomical structure; (B) cropping (42) the medical image to the first bounding box, thereby generating a cropped image (11) containing the image content within the first bounding box (51); and (C) applying a second neural network (44) to the cropped medical image, wherein the second neural network is trained to detect at least one second class of smaller-sized anatomical structures (3b), thereby generating as output the coordinates of at least one second bounding box (54) and the confidence score of it containing a smaller-sized anatomical structure.

Disclosed are a system and a method for ultrasound elastography and a method for dynamically processing frames in real time. The system includes an elasticity processing apparatus having an elasticity information detecting module for extracting elasticity information representing the elasticity of a target to be detected; a quality parameter calculating module for calculating at least a quality parameter reflecting quality of each elasticity image corresponding to the elasticity information; and a frame processing module for determining whether to output corresponding elasticity image based on the quality parameter of each elasticity image. When calculating a strain of consecutive images, the parameter reflecting the quality of each image is also computed, through which, the current elasticity image is determined whether to be displayed, thus avoiding the situation that colors of acquired successive elasticity images may vary greatly due to large difference existing in stress.

Disclosed is an intraluminal imaging system, including an intraluminal imaging catheter or guidewire configured to be positioned within an anatomy of a patient, and a processor circuit in communication with the imaging catheter or guidewire, wherein the processor circuit is configured to receive a plurality of cross-sectional images of the anatomy from the imaging catheter or guidewire. The processor is further configured to compute, using image processing of at least one of the cross-sectional images, a value of the anatomy, estimate a cross-sectional shape of the anatomy to be circular, calculate a diameter of the anatomy based on the computed value and the estimated circular shape, and output the diameter of the anatomy to a display.

This method, for determining the local intensity (I.sub.0) of an acoustic field propagating in a target region of a soft solid, at a position located within said target region, includes at least the following steps: determining (102) a value of an ultrasound attenuation coefficient (α) of the soft body in the target region; determining (104) a value of the shear modulus (μ) of the soft body in the target region; determining (106) a value of the speed of sound (c) in the target region of the soft body; and building (110), with the values determined in steps a), b) and c), a viscoelastic model (M) of a steady-state displacement induced by an acoustic field having a time invariant shape or a viscoelastic model of a difference between two steady-state displacements induced by an acoustic field having a time invariant shape. Moreover, this method also includes the following steps: applying (112) to the target region the acoustic field emitted by an ultrasound source, for a duration such that the acoustic field induces a steady-state localized deformation (Formula (I)) of the soft body in the target region; measuring (114) at least one steady state displacement induced by the acoustic field at a given position in the target region; and computing (116) the amplitude of the intensity of the acoustic field at said given position by inverting the viscoelastic model (M) at said given position, for the displacement(s) measured at step f).

Systems and methods for improving soft tissue contrast, characterizing tissue, classifying phenotype, stratifying risk, and performing multi-scale modeling aided by multiple energy or contrast excitation and evaluation are provided. The systems and methods can include single and multi-phase acquisitions and broad and local spectrum imaging to assess atherosclerotic plaque tissues in the vessel wall and perivascular space.

In some embodiments, noise data may be used to train a neural network (or other prediction model). In some embodiments, input noise data may be obtained and provided to a prediction model to obtain an output related to the input noise data (e.g., the output being a prediction related to the input noise data). One or more target output indications may be provided as reference feedback to the prediction model to update one or more portions of the prediction model, wherein the one or more portions of the prediction model are updated based on the related output and the target indications. Subsequent to the portions of the prediction model being updated, a data item may be provided to the prediction model to obtain a prediction related to the data item (e.g., a different version of the data item, a location of an aspect in the data item, etc.).

A downloadable navigator for a mobile ultrasound unit having an ultrasound probe, implemented on a portable computing device. The navigator includes a trained orientation neural network to receive a non-canonical image of a body part from the mobile ultrasound unit and to generate a transformation associated with the non-canonical image, the transformation transforming from a position and rotation associated with a canonical image to a position and rotation associated with the non-canonical image; and a result converter to convert the transformation into orientation instructions for a user of the probe and to provide and display the orientation instructions to the user to change the position and rotation of the probe.

A method for controlling a medical device may be provided. The method may include obtaining, via one or more cameras, first data regarding a first motion of a subject in an examination space of the medical device. The method may include obtaining, via one or more radars, second data regarding a second motion of the subject. The method may further include generating, based on the first data and the second data, a control signal for controlling the medical device to scan at least a part of the subject.

A photoacoustic image evaluation apparatus includes a processor configured to acquire a first photoacoustic image generated at a first point in time and a second photoacoustic image generated at a second point in time before the first point in time, the first and second photoacoustic images being photoacoustic images generated by detecting photoacoustic waves generated inside a subject, who has been subjected to blood vessel regeneration treatment, by emission of light into the subject; acquire a blood vessel regeneration index, which indicates a state of a blood vessel by the regeneration treatment, based on a difference between a blood vessel included in the first photoacoustic image and a blood vessel included in the second photoacoustic image; and display the blood vessel regeneration index on a display.