This disclosure provides a method for imaging lymph nodes and lymphatic vessels without a contrast agent. The method includes providing, using an optical source, an infrared illumination to a region of a subject having at least one lymphatic component, detecting a reflected portion of the infrared illumination directly reflected from the region using a sensor positioned thereabout, and generating at least one image indicative of the at least one lymphatic component in the subject using the reflected portion of the infrared illumination.

An ultrasound diagnostic apparatus 1 includes an image acquisition unit 8 that transmits an ultrasound beam from an ultrasound probe 18 to a subject to acquire an ultrasound image, an optic nerve recognition unit 9 that performs image analysis on the ultrasound image acquired by the image acquisition unit 8 to recognize an optic nerve of the subject, an optic nerve evaluation unit 10 that evaluates a shape of the optic nerve of the subject recognized by the optic nerve recognition unit 9 on the basis of an anatomical structure, and an operation guide unit 12 that guides a user to operate the ultrasound probe 18 so as to acquire an ultrasound image for measurement of the optic nerve of the subject on the basis of an evaluation result obtained by the optic nerve evaluation unit 10.

The invention provides a fluid analysis apparatus, a method for operating a fluid analysis apparatus, and a fluid analysis program that perform display such that the tendency of a fluid flow in a blood vessel is easily checked. Route position information that is capable of identifying an order along a route of the anatomical structure is assigned to each position in the anatomical structure, using three-dimensional volume data in which each voxel has the information of a three-dimensional flow velocity vector indicating a flow velocity of a fluid in an anatomical structure. The three-dimensional flow velocity vector is selected such that the route position information of a position where the three-dimensional flow velocity vector is present is sequentially arranged from one point in the anatomical structure and a trajectory indicating the flow of the fluid is drawn so as to be visibly recognized.

Embodiments described herein provide a system for assessing the surface of an object for detecting contamination or other defects. During operation, the system obtains an input image indicating the contamination on the object and generates a synthetic image using an artificial intelligence (AI) model based on the input image. The synthetic image can indicate the object without the contamination. The system then determines a difference between the input image and the synthetic image to identify an image area corresponding to the contamination. Subsequently, the system generates a contamination map of the contamination by highlighting the image area based on one or more image enhancement operations.

Embodiments relate to a method and system for determining a situation of a facility by imaging a sensing data of the facility including receiving sensing data through a plurality of sensors at a query time, generating a situation image at the query time, showing the situation of the facility at the query time based on the sensing data, and determining if an abnormal situation occurred at the query time by applying the situation image to a pre-learned situation determination model.

This invention provides a signal-processing method that makes it possible to acquire, relatively easily and surely, a highly reliable normalized impulse-response signal without relying on the signal-correction processing after normalization. The signal-processing method of this invention includes a low-frequency extraction step, a high-frequency extraction step and a synthesizing step. In the low-frequency extraction step, only the low-frequency component is extracted from the spectrum of the first normalized signal NS1 obtained by normalizing the target signal S.sub.tgt in the time domain. In the high-frequency extraction step, only the high-frequency component is extracted from the spectrum of the second normalized signal NS2 obtained by normalizing the target signal S.sub.tgt in the frequency domain using the reference signal S.sub.ref. In the synthesizing step, the low-frequency component, derived from the first normalized signal NS1, and the high-frequency component, derived from the second normalized signal NS2, are synthesized to obtain a normalized impulse-response signal NS.

A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

Methods and systems for performing diagnostic functions for a deep learning model are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a deep learning model configured for determining information from an image generated for a specimen by an imaging tool. The one or more components also include a diagnostic component configured for determining one or more causal portions of the image that resulted in the information being determined and for performing one or more functions based on the determined one or more causal portions of the image.

The invention provides for a magnetic resonance imaging system (100) for acquiring magnetic resonance data (142) from a subject (118) within a measurement zone (108). The magnetic resonance imaging system (100) comprises: a processor (130) for controlling the magnetic resonance imaging system (100) and a memory (136) storing machine executable instructions (150, 152, 154), pulse sequence commands (140) and a dictionary (144). The pulse sequence commands (140) are configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of multiple steady state free precession (SSFP) states per repetition time. The pulse sequence commands (140) are further configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of the multiple steady state free precession (SSFP) states according to a magnetic resonance fingerprinting protocol. The dictionary (144) comprises a plurality of tissue parameter sets. Each tissue parameter set is assigned with signal evolution data pre-calculated for multiple SSFP states.

Dynamic thin film interferometry is a technique used to non-invasively characterize the thickness of thin liquid films that are evolving in both space and time. Recovering the underlying thickness from the captured interferograms, unconditionally and automatically is still an open problem. A compact setup is provided employing a snapshot hyperspectral camera and the related algorithms for the automated determination of thickness profiles of dynamic thin liquid films. The technique is shown to recover film thickness profiles to within 100 nm of accuracy as compared to those profiles reconstructed through the manual color matching process. Characteristics and advantages of hyperspectral interferometry are discussed including the increased robustness against imaging noise as well as the ability to perform thickness reconstruction without considering the absolute light intensity information.