An ultrasonic device and a method and system for transforming the display of a three-dimensional ultrasonic image thereof. The method comprises: first acquiring original ultrasonic three-dimensional body data including a tested object (S10); detecting the orientation of the tested object from the original ultrasonic three-dimensional body data according to image features of the tested object (S20); then comparing the orientation of the tested object with a desired orientation, so as to obtain a rotation transformation parameter in a three-dimensional coordinate system (S30); next, rotating and transforming the original ultrasonic three-dimensional body data according to the rotation transformation parameter, so as to obtain the transformed three-dimensional body data (S40); and finally, outputting the transformed three-dimensional body data (S50). Therefore, the ultrasonic three-dimensional body data of the tested object can automatically rotate to the desired orientation without requiring manual adjustment, thereby improving the efficiency thereof.

A system and method for presenting rotation previews of an object depicted in a volume rendering of 3D and/or 4D image data is provided. The method includes presenting a volume rendering of an object having an initial view at a display system. The method includes presenting volume rendering previews at the display system. Each of the volume rendering previews provides a different rotational view of the object and is located on the display system in relation to the volume rendering at a position associated with a directional directive of a user input device. The method includes receiving a directional directive from the user input device. The method includes presenting an updated volume rendering of the object at the display system. The updated volume rendering includes an updated view of the object rotated from the initial view based on the directional directive from the user input device.

A method and system for detecting the presence or absence of a target or desired object within a three-dimensional (3D) image. The 3D image is processed to extract one or more 3D feature representations, each of which is then dimensionally reduced into one or more two-dimensional (2D) feature representations. An object detection process is then performed on the 2D features map(s) to generate information about at least the presence or absence of an object within the 2D feature representations, and thereby the overall 3D image.

Embodiments include a patch-type, ultrasound sensor system and method to monitor the function and motion of a patients anatomical structure, comprising processing at least one received ultrasound image using one or more analytical tools, including radon transformation, higher-order spectra techniques, and/or active contour models, to generate at least one processed ultrasound image; inputting the at least one processed ultrasound image into a deep learning Convolutional Neural Network to obtain an automatic classification result selected from two or more classes indicating the functional state of the anatomical structure. The patch-type, ultrasound sensor system can communicate via a wireless or wired connection. The monitoring can be at rest or during surgery or other procedure or whilst the subject is exposed to any physiological stressors as part of medical examinations, and can be adapted for use in monitoring the function of body structures including the heart, blood vessels, lungs or joints.

The invention relates to a method for obtaining a numerical model, the numerical model associating at least one objective measurement to a subjective sensation, the method comprising the steps of: a) imaging the at least one area of the brain by using unfocused waves produced by a transcranial ultrasound probe (20), to obtain at least one acquired image of the activity of the area, b) evaluating a physical quantity representative of the activity of the at least one area based on the acquired images, to obtain at least one objective measurement, c) obtaining from the subject at least one numerical value representative of a subjective sensation, and d) determining the numerical model by using the obtained objective measurement and the obtained numerical value.

The invention provides an ultrasound processing unit. A controller (18) of the unit is adapted to receive ultrasound data of an anatomical region, for example of the heart. The controller processes the ultrasound data over a period of time to monitor and detect whether alignment of a particular anatomical feature (34) represented in the data relative to a field of view (36) of the transducer unit is changing over time. In the event that the alignment is changing, the controller generates an output signal for communicating this to a user, allowing a user to be alerted at an early stage to likelihood of misalignment and loss of imaging or measurement capability.

A detection unit detects a lesion candidate contained in display frame data (a tomographic image), using a machine learned detection model. An exclusion processing unit collates a feature vector of the detected lesion candidate with feature vectors registered in an exclusion database to determine whether the detected lesion candidate is an exclusion target. When the detected lesion candidate is an exclusion target, a mark display control unit restricts display of a mark for indicating a lesion candidate.

A system is described for generating diagnostic information from a video sequence of ultrasound images acquired in “blind sweeps”, i.e., without operator seeing ultrasound images as they are acquired. We disclose two different types of machine learning systems for predicting diagnostic information: a “Temporal Accumulation” system and a “3-D Modeling Component” system. These machine learning systems could be implemented in several possible ways: using just one or the other of them in any given implementation, or using both of them in combination. We also disclose a computing system which implements (a) an image selection system including at least one machine learning model trained to identify clinically suitable images from the sequence of ultrasound images and (b) an image diagnosis/measurement system including of one or more machine learning models, configured to obtain the clinically suitable images identified by the image selection system and further process such images to predict health states.

An image processing apparatus selects one or a plurality of examinations to which a medical image belongs, determines image processing candidate examinations based on the selected one or plurality of examinations, displays medical images belonging to the determined image processing candidate examinations on a display unit, and executes image processing using, of the displayed medical images, a plurality of medical images selected by a user, wherein, when the one examination is selected, the selected one examination and one or a plurality of examinations obtained by a search based on the selected one examination are determined as the image processing candidate examinations, and when the plurality of examinations are selected, in the determining, the selected plurality of examinations are determined as the image processing candidate examinations.

A method includes emitting an ultrasound beam, having a predefined field of view (FOV), from an array of ultrasound transducers in a catheter in an organ of a patient. Echo signals are received in the array, in response to the ultrasound beam. A position of a target object is estimated within the FOV. When the estimated position of the target object violates a centering condition, the FOV of the ultrasound beam is automatically modified to re-meet the centering condition.