A method for detecting tissue abnormality in a tissue sample, comprising: illuminating a tissue sample in vivo with a first light beam having a wavelength selected from the range 390-430 nanometer (nm); applying contrast agent to the tissue sample; capturing a one or more images of the tissue sample; and detecting tissue abnormality based on at least one of color changes in the tissue sample and blood vessel features in the tissue sample appearing in said one or more images.

A system and method for generating simulated post-contrast T1-weighted magnetic resonance (MR) images without the use of exogenous contrast material based upon patient-specific non-contrast MR images using machine learning/artificial intelligence techniques to train the system to generate post-contrast T1-weighted magnetic resonance images based upon retrospectively collected non-contrast MR images of various sequence types including T1-weighted, T2-weighted, FLAIR (Fluid-Attenuated Inversion Recovery), and/or DWI (Diffusion-Weighted Imaging).

A machine learning device includes: a generation unit generating a first shape model representing a shape of an object before deformation and a second shape model representing a shape of the object after the deformation based on measurement data before and after the deformation; and a learning unit learning a feature amount including a difference value between each micro region and another micro region that constitute the first shape model, and a relation providing a displacement from the each micro region of the first shape model to each corresponding micro region of the second shape model.

A system (10) that provides diagnosis support information (110) relating to a disease of a target subject (5) includes: an acquisition unit (11) that acquires subject information (105) including actual image data (15) of an MR image including at least a reference region including part of an evaluation target region of the subject; and an information providing unit (12) that provides diagnosis support information (110) based on pseudo PET image data (115) of the evaluation target region generated by an image processing model (60) machine learned with training data (70) including actual image data (71) of a MR image of a reference region and actual image data (72) of a PET image including the evaluation target region of a plurality of test subjects so as to generate pseudo PET image data (75) of the evaluation target region from actual image data (71) of an MR image of the reference region, from the actual image data (15) of an individual MR image of the target subject.

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images, and/or to fusing data from high-elevation digital images having different spatial, temporal, and/or spectral resolutions. In various implementations, first and second temporal sequences of high-elevation digital images capturing a geographic area may be obtained. These temporal sequences may have different spatial, temporal, and/or spectral resolutions (or frequencies). A mapping may be generated of the pixels of the high-elevation digital images of the second temporal sequence to respective sub-pixels of the first temporal sequence. A point in time at which a synthetic high-elevation digital image of the geographic area may be selected. The synthetic high-elevation digital image may be generated for the point in time based on the mapping and other data described herein.

A non-transitory computer-readable recording medium storing therein an anomalousness determination program that causes a computer to execute a process includes: sensing a region in an object in each of a plurality of ultrasound examination images using an object sensing technique; based on a result of the sensing and a structure of the object, acquiring a result of sensing each of a plurality of regions in the object in each of the ultrasound examination images; and determining anomalousness in the object based on the result of sensing each of the regions in the ultrasound examination images.

In an embodiment, a method includes acquiring a first image data set of the patient, via an X-ray apparatus, at a first time point during the operative intervention, the first image data set including the reference structure, the anatomical structure and the reference system between the reference structure and the anatomical structure; acquiring a second image data set of the patient at a second time point, the second image data set including at least the reference structure; registering the second image data set to the first image data set. As a result of the registering of the second image data set to the first image data set, a registered second image data set is determined. Finally, an embodiment of the method includes determining the validity of the reference system by a comparison of the registered second image data set with the first image data set.

Systems and methods to identify and/or reduce or eliminate sample motion artifacts are disclosed. Sample motion artifacts may be reduced or eliminated using scan patterns where an acquisition time difference between when perimeter pixels in adjacent tiles are acquired is reduced, as compared to a conventional raster scan to reduce or eliminate discontinuities that would otherwise appear at tile boundaries in an image. In some embodiments, test images acquired using relatively small test scan patterns or intensities of test points acquired at different times may be compared to determine whether sample motion has occurred. In some embodiments, intensity of adjacent pixels at a tile boundary are compared. In some embodiments, intensity of one or more single pixels is monitored over time to determine whether sample motion has occurred over a period of time. In some embodiments, a flattening or reshaping tool may be used to suppress sample motion during imaging.

Provided are an ultrasound imaging apparatus and an operation method thereof for obtaining spectral Doppler pulse wave data at a plurality of points in a region of interest by using a multiline receiving technique, identifying a location and a direction of blood flow by using the obtained spectral Doppler pulse wave data, and automatically correcting an angle of a sample volume by using information about the identified location and direction of the blood flow.

A method and a system are provided for automatically delineating a striatum in a nuclear medicine brain image and calculating a striatum specific uptake ratio. In the method, initially, a target image is obtained. Then, the target image is projected to a space coordinate to generate a projection amount; an upper end and a lower end of a brain are obtained; and a preset range from the upper to the lower ends is set as a striatum slice area in the target image. A brain area is determined from the target image by a line detection method. Then, a brain volume template is deformed according to the brain area and the striatum slice area, so that a striatum in the brain volume template corresponds to the target image to delineate a striatum region of the target image. Finally, a specific uptake ratio of the striatum region can be calculated.