An ultrasound diagnostic apparatus includes: an image generator that generates ultrasound image data based on a reception signal obtained from a moving ultrasound probe; an evaluator that evaluates an index regarding suitability of combining a plurality of pieces of ultrasound image data generated by the image generator and generates an evaluation result; and a combiner that selects ultrasound image data according to the generated evaluation result and combines the ultrasound image data to generate panoramic image data.

Apparatus for imaging cavitation in a subject volume of a subject comprises a plurality of transducer elements, and control means. The control means is arranged to: control at least a first one of the transducer elements to generate ultrasound having a focal region; receive passive detection signals from a first group of the transducer elements and generate from the passive detection signals a cavitation image of cavitation in the focal region; control at least a second one of the transducer elements to generate reflective imaging ultrasound; receive reflective imaging detection signals from a second group of the transducer elements and generate from the reflective imaging detection signals a reflective image; register the reflective image with a 3D image of the subject volume to obtain a transformation between a coordinate system of the first group of transducer elements and a coordinate system of the 3D image; and apply the transformation to the cavitation image to align the cavitation image with the 3D image.

A medical image processing apparatus according to the present embodiment includes processing circuitry. The processing circuitry extracts a registration target area corresponding to a registration target organ from an ultrasonic image by acquiring edges of the registration target organ from the ultrasonic image among a plurality of types of medical images including the registration target organ. The processing circuitry calculates the degree of deformation of a plurality of positions in the registration target area. The processing circuitry performs a rigid registration on the types of medical images on the basis of the degree of deformation for each position calculated at the positions and the similarity between the types of medical images at the positions.

Systems and methods for calibrating ultrasound imaging directed towards a bone region. An ultrasound imaging device generates a first steered frame and a second steered frame, wherein the first steered frame and the second steered frame are directed towards the bone region at different angles from one another and are superimposed with one another. Parameters of each of the first steered frame and the second steered frame are applied to a cost function that outputs an estimated propagation speed of ultrasound waves to the bone region for optimizing an appearance of the first steered frame and the second steered frame. The ultrasound imaging device is calibrated based on the estimated propagation speed.

Embodiments of the disclosure provide an ultrasound beamforming method and device. The method includes: obtaining channel data of a target tissue; and processing the channel data using at least two different ultrasound beamforming methods to obtain image data of the target tissue corresponding to the different ultrasound beamforming methods, where the at least two different ultrasound beamforming methods are different in at least one of principle, step, and parameter.

A multimodal medical image fusion method based on a DARTS network is provided. Feature extraction is performed on a multimodal medical image by using a differentiable architecture search (DARTS) network. The network performs learning by using the gradient of network weight as a loss function in a search phase. A network architecture most suitable for a current dataset is selected from different convolution operations and connections between different nodes, so that features extracted by the network have richer details. In addition, a plurality of indicators that can represent image grayscale information, correlation, detail information, structural features, and image contrast are used as a network loss function, so that the effective fusion of medical images can be implemented in an unsupervised learning way without a gold standard.

An ultrasonic diagnostic apparatus includes a probe configured to irradiate an ultrasonic signal to an object and receive the ultrasonic signal reflected from the object; a pressure device configured to contract or expand a blood vessel of the object and is divided into a plurality of regions; and a controller configured to operate in one region of the plurality of regions to generate an image related to the blood vessel of the object and sequentially operate in other regions of the plurality of regions.

This disclosure provides methods for vascular imaging co-registration and using the co-registered imaging data in guiding live fluoroscopy. Extravascular imaging data includes an extravascular contrast image showing the portion of the blood vessel with contrast showing a visualized anatomical landmark while intravascular imaging data is obtained during a translation procedure that includes one or more intravascular images showing a detected anatomical landmark. The starting location and the ending location of the imaging element on the extravascular imaging data is marked, and the predicted location of the detected anatomical landmark on the extravascular imaging data is marked. The predicted location of the detected anatomical landmark is then aligned with the visualized anatomical landmark.

A method for diagnosing a joint condition includes in one embodiment: creating a 3d model of the patient specific bone; registering the patient's bone with the bone model; tracking the motion of the patient specific bone through a range of motion; selecting a database including empirical mathematical descriptions of the motion of a plurality actual bones through ranges of motion; and comparing the motion of the patient specific bone to the database.

An ultrasound diagnosis apparatus includes: a scan controlling unit that exercises control to perform a first scanning process by transmitting an ultrasound wave in a first direction and a second scanning process by transmitting an ultrasound wave in each of a plurality of directions; an image generating unit that generates a first ultrasound image and second ultrasound images from the first and the second scanning processes, respectively; an image generation controlling unit that has a needle image generated, based on an analysis result on the brightness distribution of each member of a group of images based on the first ultrasound image and the second ultrasound images or an analysis result on the brightness distribution of each of the second ultrasound images; an image synthesizing unit that generates a synthesized image from the first ultrasound image and the needle image; and a display controlling unit that displays the synthesized image.