The invention generally relates to systems and methods for therapeutically modulating nerves in or associated with a nasal region of a patient for the treatment of a rhinosinusitis condition.

A first image acquisition unit acquires a first image that is captured by a sensor having a first pixel arrangement pattern and includes a first reproduction band in a frequency domain. A second image acquisition unit acquires a second image that is captured by a sensor having a second pixel arrangement pattern and includes a second reproduction band different from the first reproduction band in the frequency domain. A correction processing unit generates a first correction image by correction processing of at least reducing or deleting high-frequency components that are not included in the second reproduction band within the first reproduction band.

Methods and apparatuses for asynchronously identifying and modeling a gingiva strip from the three-dimensional (3D) dental model of the patient's dentition. These methods may reduce the time required to generate accurate 3D dental models and therefore may reduce and streamline the process of generating dental treatment plans.

There are provided techniques for analyzing an atom probe tomography data set obtained from a tip-shaped sample. The techniques include defining analysis sub-volumes in the atom probe tomography data set; performing a fast Fourier transform (FFT) on each of the analysis sub-volumes to obtain a signal in a Fourier domain; identifying at least one FFT peak in the signal in the Fourier domain, each FFT peak being indicative of an expected crystal feature in the corresponding analysis sub-volume; continuously and automatically calculating an image compression factor and a radius of the tip-shaped sample, based on identified crystal features, the identified crystal features being obtained from a collection of expected crystal features; and reconstructing a three-dimensional model of the tip-shaped sample. Said reconstructing includes comparing the identified crystal features with calibration data; and dynamically adjusting the image compression factor and the radius of the tip-shaped sample.

An example method includes obtaining, a virtual model of a portion of an anatomy of a patient obtained from a virtual surgical plan for an orthopedic joint repair surgical procedure to attach a prosthetic to the anatomy; identifying, based on data obtained by one or more sensors, positions of one or more physical markers positioned relative to the anatomy of the patient; and registering, based on the identified positions, the virtual model of the portion of the anatomy with a corresponding observed portion of the anatomy.

Methods and systems are described for image segmentation. A machine learning model is applied to a set of images to generate results. The results may be obtained as a probability map for each image in the set of images. The model may be trained by accessing a set of labeled images, each image associated with a label indicating a location of a feature within a respective image. An initial set of parameters is accessed. An encoder is initialized with the initial set of parameters. The encoder is applied to the set of labeled images to generate a prediction of a feature location within each image. The initial set of parameters are updated based on the predictions and the label associated with the labeled images. The updated set of parameters and an additional set of parameters generated using a set of unlabeled images are aggregated.

This invention provides a method to optimize an x-ray beam for more than one structure within the field of view. The preferred embodiment comprises a modular construction of a collimator comprising multiple materials of varying thickness. A first attenuation is performed by the first portion of the collimator to optimize a first anatomic feature and a second attenuation is performed by the second portion of the collimator to optimize a second anatomic feature.

Disclosed is a care device recommendation server providing a service for recommending a skin care device for a user, the care device recommendation server including: a data management unit configured to obtain face images of the user from a shin measurement device including a camera and a display; a skin condition determination unit configured to determine a first skin condition of the user corresponding to a first time point at which the face images are obtained, based on the obtained the face images; a care device recommendation unit configured to determine the skin care device for the user among a plurality of care devices included in a care device DB based on the determined first skin condition; a care device control value-providing unit configured to provide a first control value of the skin care device corresponding to the first skin condition to each of a user terminal of the user and the skin measurement device; a care device control unit configured to remotely control the care device such that the skin care device is driven by the control value; and a history provision unit configured to provide information about a previous skin condition of the user, which corresponds to each of the face images of the user obtained from the data management unit, to the user terminal.

A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes generating, by a projection completion circuitry, an intermediate CT image data based, at least in part, on input CT projection data. The intermediate CT image data is configured to include relatively fewer artifacts than an uncorrected CT image reconstructed from the input CT projection data. The method further includes generating, by an artificial neural network (ANN), CT output image data based, at least in part, on the intermediate CT image data. The CT output image data is configured to include relatively fewer artifacts compared to the intermediate CT image data. The method may further include generating, by detail image circuitry, detail CT image data based, at least in part, on input CT image data. The CT output image data is generated based, at least in part, on the detail CT image data.

A mobile device includes an application processor and an image sensor. The application processor includes an imaging subsystem configured to process high resolution image data through a first interface and a sensor hub configured to process sensor data through a second interface. The image sensor operates in one of first and second modes. The image sensor is configured to capture the high resolution image data in response to a request from the imaging subsystem and the imaging subsystem is configured to access the high resolution image data using the first interface for performing a first operation, during the first mode. The image sensor is configured to capture low resolution image data and the sensor hub is configured to access the low resolution image data using the second bus for performing a second operation, during the second mode.