An ultrasound diagnosis apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured: to generate a piece of reflected-wave data by performing a phased addition process while using reflected-wave signals generated by transmitting an ultrasound wave with respect to mutually the same scanning line; to speculate a degree of saturation of the reflected-wave signals observed before the phased addition process on the basis of a relationship between signals and noise in a data sequence represented by a set made up of pieces of the reflected-wave data; and to output a result of the speculation. The processing circuitry is configured to cause a display to display data based on the result of the speculation.

A method and system enable to-be-processed medical image data and its corresponding noise characteristic information to be normalized to resemble noise characteristic information of training data used to train at least one neural network for at least one ultrasound data acquisition mode. After normalizing, this processed medical image data is input into the trained neural network for producing output data used for generating cleaner images. Noise characteristic information can be used directly in training a neural network, generating a trained neural network that can handle medical image data with various noise characteristics.

An ultrasound diagnostic apparatus including a hardware processor that: calculates a plurality of eigenvectors by performing principal component analysis on a time-series reflection wave data group; calculates a principal frequency component of a time direction represented by the plurality of eigenvectors; determines a reduction rate of each of the plurality of eigenvectors on a basis of the principal frequency component of each of the plurality of eigenvectors and a clutter component reduction condition that defines a reference frequency for reduction of a clutter component; calculates a filter coefficient for reduction of the clutter component on a basis of the plurality of eigenvectors, and the reduction rate corresponding to each of the plurality of eigenvectors; and generates blood flow image data by applying the filter coefficient to the time-series reflection wave data group.

Ultrasound imaging systems and methods for generated clutter-reduced images are provided. For example, an ultrasound imaging system can include an array of acoustic elements in communication with a processor. The processor is configured to activate the array to perform a scan sequence to obtain a plurality of signals, identify off-axis signals from the plurality of signals by comparing the right subaperture and the left subaperture, and create a clutter-reduced image based on the comparison. Because off-axis signals are more likely to create image clutter, reducing the influence of off-axis signals on the image can therefore improve the quality of the image. Accordingly, embodiments of the present disclosure provide systems, methods, and devices for generating ultrasound images that have reduced or minimized clutter, even for images obtained using arrays that do not satisfy the Nyquist criterion.

A method of processing cardiac ultrasound data for determining information about a mechanical wave in the heart. The method comprises receiving data representative of a time series of three-dimensional data frames, generated from ultrasound signals from a human or animal heart, each frame comprising a set of voxels, each voxel value representing an acceleration component of a respective location in the heart at a common time. The method also comprises identifying, for each voxel, a frame of the series in which the voxel value is at a maximum. A three-dimensional time-propagation data set is generated by assigning each voxel a value representative of the time of the respective frame in the time series for which the corresponding voxel is at a maximum. The method then comprises generating data representative of a three-dimensional velocity vector field by calculating time derivatives from the three-dimensional time-propagation data set.

Aspects of the technology described herein relate to configuring an ultrasound system with imaging parameter values. In particular, certain aspects relate to configuring an ultrasound system to produce a plurality of sets of ultrasound images, each respective set of the plurality of sets of ultrasound images being produced with a different respective set of a plurality of sets of imaging parameter values; obtaining, from the ultrasound system, the plurality of sets of ultrasound images; determining a set of ultrasound images from among the plurality of sets of ultrasound images that has a highest quality; and based on determining the set of ultrasound images from among the plurality of sets of ultrasound images that has the highest quality, automatically configuring the ultrasound system to produce ultrasound images using a set of imaging parameter values with which the set of ultrasound images that has the highest quality was produced.

According to one embodiment, a medical image processing apparatus includes processing circuitry. The processing circuitry estimates a contour of a desired structure based on a medical image, receives a desired correction mode among multiple correction modes for correcting the estimated contour, and corrects the estimated contour according to the desired correction mode.

A non-transitory computer-readable recording medium stores therein a learning program that causes a computer to execute a process including: generating a shadow image including a shadow according to a state of ultrasound reflection in an ultrasound image; generating a combined image by combining the ultrasound image and the shadow image; inputting, into a first decoder and a second decoder, an output acquired from an encoder in response to inputting the combined image into the encoder; and executing training of the encoder, the first decoder, and the second decoder, based on: reconfigured error between an output image of a coupling function and the combined image, the coupling function being configured to combine a first image output from the first decoder with a second image output from the second decoder, and an error function between an area in the first image and the shadow in the shadow image.

A medical system includes a physiological monitoring system configured to sense a physiological signal and record physiological signal data indicative of the patient's physiological state. The physiological monitoring system including a controller, a storage device, at least one sensor operatively coupled to the controller, and a first communication component. The system includes a mobile device configured to facilitate sensor placement, the mobile device comprising a controller, a display device, and a second communication component configured to facilitate communication between the physiological monitoring system and the mobile device. The controller of the mobile device is configured to provide a graphical user interface (GUI) on the display device, the GUI including information about a proper placement of the at least one sensor, wherein the proper placement is determined based on the physiological signal data.

An ultrasonic processing apparatus is provided. The ultrasonic processing apparatus comprises an ultrasonic therapy transducer (ATA) adapted to generate focused ultrasonic waves; an ultrasonic imaging transducer (UID) connected to the ultrasonic therapy transducer; and an electronic system configured to control the ultrasonic therapy transducer so as to emit a pulse train of ultrasonic waves generating a cloud of cavitation bubbles (BC); control the ultrasonic imaging transducer so as to acquire at least one image of the region to be processed; acquire a plurality of echo signals of ultrasonic wave pulses emitted by the ultrasonic therapy transducer captured by the ultrasonic imaging transducer; process the plurality of echo signals so as to reconstruct an image of the cloud of cavitation bubbles; and display said image of the cloud of cavitation bubbles superposed on said image of the region to be processed. The processing includes spatio-temporal filtering.