A method for forming a virtual 3D mathematical model of a dental system, including receiving DICOM files representing the dental system; identifying number and location of voxels of tissues of the dental system; combining the voxels of the tissues into voxels of organs of the dental system; combining the organs into the virtual 3D mathematical model of the dental system, wherein the virtual 3D mathematical models supports linear, non-linear and volumetric measurements of the dental system; and presenting the virtual 3D mathematical model to a user. The DICOM files can be cone beam or multispiral computed tomography, MRT, PET and/or ultrasonography. The tissues include enamel, dentin, pulp, cartilage, periodontium, and/or jaw bone. The organs include teeth, gums, temporomandibular joint and/or jaw. A size of the voxels is typically between 40 μm and 200 μm.

The invention relates to an ultrasonic method for estimating a nonlinear shear wave elasticity of a medium, the method comprising the following steps: A1. a first collection step in which a first set comprising one shear wave elasticity data point of the medium is collected at a first level of deformation applied to the medium, A2. a second collection step in which a second set comprising one shear wave elasticity data point of the medium is collected at a second level of deformation applied to the medium different to the first level, A3. a deformation estimation step in which the difference of deformation between the first and the second level of deformation is estimated, B1. a calculation step in which a gradient between at least two data points respectively belonging to the first and the second set is calculated as a function of the difference of deformation between the first and the second level of deformation, B2. an elasticity estimation step in which the nonlinear shear wave elasticity of the medium is estimated as a function of the gradient.

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.

Examples disclosed herein relate to determining a power difference in sensor signals. Examples include a first sensor to transmit a first ultrasonic signal into a pregnant woman and to receive a second ultrasonic signal; and a second sensor to transmit a third ultrasonic signal into the pregnant woman and to receive a fourth ultrasonic signal. A processing resource determines a first power difference of the first sensor according to a difference between respective powers of the first ultrasonic signal and the second ultrasonic signal and is to determine a second power difference of the second sensor according to a difference between respective power of the third ultrasonic signal and the fourth ultrasonic signal. In examples, the processing resource is to determine a relative location of the fetal heart according to a comparison of the first power difference and the second power difference.

An ultrasound imaging apparatus and a control method thereof. The ultrasound imaging apparatus may include: a display; a communication unit; and a processor configured to be operatively connected to the display and the communication unit. The processor may obtain a first ultrasound image of a subject and a result of an analysis of the first ultrasound image. The processor may also control the display to display a user interface, which allows selection of an operating mode of the ultrasound imaging apparatus, based on the result of the analysis.

An apparatus (1) for providing image quality feedback during a medical imaging examination includes at least one electronic processor (20) programmed to: receive a live video feed (17) of a display (6) of an imaging device controller (4) of an imaging device (2) performing the medical imaging examination; extract a preview image (12) from the live video feed; perform an image analysis (38) on the extracted preview image to determine whether the extracted preview image satisfies an alert criterion; and output an alert (30) when the extracted preview image satisfies the alert criterion as determined by the image analysis.

A non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process, an information processing apparatus, and model generation method that generates an image of a lumen organ. The process includes acquiring a first image obtained by imaging a lumen organ of a patient based on an ultrasound signal of a first frequency; and generating a second image by inputting the acquired first image into a model, the model being learned to generate, when the first image is input, the second image in which the lumen organ is imaged based on an ultrasound signal of a second frequency. Preferably, the second image, in which a part of an image region of the first image is converted into the second frequency, is generated using the model, and a synthesis image is generated in which the second image is superimposed to the first image.

In a method and system, a medical imaging modality and the parameters to be deployed for the determined imaging modality are determined to produce an image of an examination object using the determined imaging modality and the determined parameters. Information from the preliminary examination(s) of the examination object can be automatically classified to generate classification results corresponding to interfering influence(s) resulting from the production of the image. The classification results can be analyzed to evaluate the classification results. The medical imaging modality and the parameter(s) is determined, based on the evaluated results, to minimize an influence of the interfering influences of the classification results in image(s) of the examination object generated using the determined medical imaging modality and the determined one or more parameters. The image(s) may then be generated using the determined medical imaging modality and the determined parameter(s).

Provided is an ultrasound system including an ultrasound probe and an image display device. The ultrasound probe includes: a transducer array, a transmitting and receiving unit that generates a sound ray signal on the basis of a reception signal from the transducer array; an image information data generation unit that generates image information data from the sound ray signal; and a probe-side wireless communication unit that transmits the image information data to the image display device. The image display device includes: an operating state acquisition unit that acquires an operating state of the image display device; and a display-device-side wireless communication unit that transmits the operating state to the ultrasound probe. The ultrasound probe includes at least one of an ultrasound transmission and reception control unit that controls transmission and reception of the ultrasonic waves on the basis of the operating state of the image display device or an output format setting unit that sets an output format of the image information data on the basis of the operating state of the image display device.

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.