A medical image processing apparatus according to an embodiment includes processing circuitry configured to generate an output data set apparently expressing a second data set obtained by transmitting and receiving an ultrasound wave, for each scanning line, as many times as a second number that is larger than a first number, by inputting a first data set to a trained model that generates the output data set on a basis of the first data set obtained by transmitting and receiving an ultrasound wave as many times as the first number for each scanning line.

Provided is an ultrasound diagnostic apparatus including an ultrasound probe, an imaging section that images the subject on the basis of a reception signal output from the ultrasound probe to generate an ultrasound image, an image analysis section that performs image analysis using the ultrasound image, a movement detection sensor that detects and outputs a movement of the ultrasound probe as a detection signal, a movement amount calculation section that calculates a movement amount of the ultrasound probe in a case where an imaging inspection portion that is currently being imaged among a plurality of inspection portions of the subject is inspected, using the detection signal output from the movement detection sensor, and a portion discrimination section that discriminates the imaging inspection portion on the basis of an image analysis result in the image analysis section and the movement amount calculated by the movement amount calculation section.

An ultrasound imaging device and a clutter filtering method using the same are disclosed. The clutter filtering method using the ultrasound imaging device according to one embodiment includes obtaining ultrasound data from a field-of-view (FOV) of an object, generating decomposition data including common scale information by performing rank matrix decomposition once on all of the obtained ultrasound data, estimating local characteristic information by reflecting spatial information on each pixel to the common scale information, and extracting a blood flow signal by performing filtering on each pixel based on the estimated local characteristic information.

A fully automated ultrasound apparatus includes a sensor or probe which can be initially manually attached to a side of the neck of a patient, an ultrasound interface to control the sensor and periodically acquire raw ultrasound data, a signal and image processing system to autonomously convert the raw ultrasound data into a measurement that is useful to physicians, and a display to relay the current measurements and measurement history to provide data trends. The sensor can include one or more ultrasound transducers built into a housing. A disposable component can serve to secure the sensor to the neck of the patient and to provide a coupling medium between the sensor and the skin of the patient.

A system and method is provided for obtaining ultrasound images of an interior of an object that includes an image processing unit that receives and processes acquired ultrasound scan data to create ultrasound images derived from ultrasound image data, a motion detection system configured to detect a pattern of inactivity time frames within movement cycles of the object and an ultrasound imaging probe operably connected to the image processing unit to acquire the ultrasound scan data for use by the image processing unit to form the ultrasound images. The motion detection system detects a pattern of one or more inactivity time frames within a first cycle of movement of the object, obtains ultrasound volumetric scan data of the object during the inactivity time frame within a second cycle of movement of the object, and calibrates a location of a scan plane of the ultrasound image within the volumetric ultrasound image.

The present disclosure describes ultrasound imaging systems and methods, which may enable the automatic identification of an image plane in a pre-operative volume corresponding to a real-time image of a moving region of interest. An example method includes receiving real-time ultrasound image data from a probe associated with a position-tracking sensor, generating real-time images based on the real-time ultrasound data and deriving a motion model from the real-time ultrasound image data. The method may further include automatically identifying an image plane in a pre-operative data set to correspond to the real-time ultrasound image by correcting for motion-induced misalignment between the real-time data and the pre-operative data.

Systems and methods for generating adaptive contrast accumulation imaging images are disclosed. A point spread function thinning/skeletonization technique may be performed on contrast enhanced image frames. An aggressiveness parameter of the technique may be adapted temporally and/or spatially. The aggressiveness parameter may be adapted based on various factors, including, but not limited to, time since injection of the contrast agent, signal intensity, and/or vessel size. The images may be temporally accumulated to generate a final sequence of adaptive contrast accumulation imaging images.

A computer system is configured to remove motion artifacts in medical images using a generative adversarial network (GAN). The computer system instantiates the GAN having one or more generative network(s) and one or more discriminative network(s) that are pitted against each other to train a generative model and a discriminative model. The training uses a training dataset including a plurality of medical images that are previously classified as without significant motion artifacts for diagnostic purposes. The discriminative model is trained to classify medical images as real or artificial. The generative model is trained to enhance the quality of a medical image and remove motion artifacts by producing a medical image directly from a post-contrast image, without using a pre-contrast mask.

Systems and methods are provided for the denoising of images in the presence of broadband noise based on the detection and/or estimation of in-band noise. According to various example embodiments, an estimate of broadband noise that lies within the imaging band is made by detecting or characterizing the out-of-band noise that lies outside of the imaging band. This estimated in-band noise may be employed for denoise the detected imaging waveform. According to other example embodiments, a reference receive circuit that is sensitive to noise within the imaging band, but is isolated from the imaging energy, may be employed to detect and/or characterize the noise within the imaging band. The estimated reference noise may be employed to denoise the detected in-band imaging waveform.

The invention provides methods and systems for generating an ultrasound image. In a method, the generation of an ultrasound image comprises: obtaining channel data, the channel data defining a set of imaged points; for each imaged point: isolating the channel data; performing a spatial spectral estimation on the isolated channel data; and selectively attenuating the spatial spectral estimation channel data, thereby generating filtered channel data; and summing the filtered channel data, thereby forming a filtered ultrasound image. In some examples, the method comprises aperture extrapolation. The aperture extrapolation improves the lateral resolution of the ultrasound image. In other examples, the method comprises transmit extrapolation. The transmit extrapolation improves the contrast of the image. In addition, the transmit extrapolation improves the frame rate and reduces the motion artifacts in the ultrasound image. In further examples, the aperture and transmit extrapolations may be combined.