The present disclosure is generally directed to systems and methods for generating de-noised MR images that are reconstructed from a hybridization of two separate image reconstruction pipelines, at least one of which includes the use of a neural network. Further, the amount of influence that the neural network reconstruction has on the hybrid reconstructed image is controlled via a regularization parameter that is selected based on an estimated noise level associated with the initial image acquisition, which can be calculated from pre-scan data.

Various methods and systems for designing a restored smile are provided. One method includes receiving scan data of a patient's teeth, developing a digital model of the patient's teeth via a computing device, where the model represents the patient's teeth based upon the scan data, creating a dental treatment plan to restore one or more teeth from an initial condition to a successive condition, and wherein a final condition of the one or more is based on the one or more teeth having at least one planned additional restorative tooth structure provided therewith.

A system for polarimetric characterization of a target that includes a liquid light guide (LLG) for propagating light from a light source to the target (S) at least one of a Polarization State Analyzer (PSA) serving to analyze polarization of light having propagated into the LLG and that has been reflected by the target, and a Polarized State Generator (PSG) for modulating the polarization of light injected into the LLG, an optical detector for detecting light backscattered by the target (S) that has been illuminated by the LLG.

A method for controlling a scanner comprises: sensing an outer surface of a body of a subject to collect body surface data, using machine learning to predict a surface of an internal organ of the subject based on the body surface data, and controlling the scanner based on the predicted surface of the internal organ.

A computer-implemented method for determining a subset of coil elements for capturing a magnetic resonance tomography recording, comprises: providing a target volume in a scout view, and determining a plurality of subsets of coil elements from among the plurality of coil elements, wherein individual subsets are configured different from one another. The method further comprises: determining at least one quality criterion for each subset of coil elements, wherein the at least one quality criterion of a corresponding subset of coil elements relates to an image quality in the target volume, dependent upon the corresponding subset of coil elements; determining the subset of coil elements from the plurality of subsets, based on the corresponding at least one quality criterion; and providing an information item regarding which of the plurality of coil elements are included by the subset of coil elements.

An information processing apparatus includes a memory and at least one processor. In the memory, a program is stored. The processor is configured to execute the program stored in the memory, and generate prescription data for skincare of a skin based on history data of an index value representing change in blood flow before and after treatment. The index value is calculated based on a first skin image and a second skin image. The first skin image is obtained by imaging the skin before a blood flow enhancement treatment, and the second skin image is obtained by imaging the skin after the blood flow enhancement treatment.

A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

A method for assessing movement of a body portion includes, via one or more machine learning models, analyzing a sensor signal indicative of movement of the body portion to determine a movement of the body portion; determining a sensor confidence level based, at least in part, on a characteristic of the sensor signal; receiving a series of images indicative of movement of the body portion; measuring an angle of movement of the body portion; determining a vision confidence level based, at least in part, on a quality of an identification the body portion; selecting the sensor signal, the measured angle of movement, or a combination thereof as an input into a machine learning model based on the sensor confidence level and the vision confidence level, respectively; analyzing the input to determine a movement pattern of the body portion; and outputting the movement pattern to a user.

A system includes an electrical tomography system and a volume estimation system. The volume estimation system is configured to reconstruct an initial impedance image based at least partially on received electrical tomography data of a domain, receive prior information associated with the domain, enhance the initial impedance image based at least partially on the received prior information to generate an enhanced impedance image, and based at least partially on the enhanced initial impedance image, generate a volumetric image of a region of interest of the enhanced impedance image, wherein the volumetric image represents a plurality of values indicating a volume of a fluid.

A height estimation method performed by a height estimation apparatus includes a first feature point extraction step of extracting a feature point coordinate, a first coordinate estimation step of estimating a coordinate of a first subject frame, a pre-generation step of deriving a height of the first subject frame and generating a distance addition pattern and a correction coefficient for an individual missing pattern, a second feature point extraction step of extracting a feature point coordinate from a second input image, a second coordinate estimation step of estimating a coordinate of a second subject frame and estimating a coordinate of an object frame, a subject data selection step of selecting the individual missing pattern and the correction coefficient in accordance with the feature point coordinate, an object data selection step of selecting an object height, and a height estimation step of adding up a distance between a feature point coordinate and another feature point coordinate extracted in accordance with the missing pattern and deriving an estimated value of a height of the subject in accordance with a result of adding up the distance, the correction coefficient, the object height, and the coordinates of the object frame.