Disclosed are a positron emission tomography system and an image reconstructing method using the same and the positron emission tomography system includes: a collection unit collecting a positron emission tomography sinogram (PET sinogram); an image generation unit applying the positron emission tomography sinogram to an MLAA with TOF and generating a first emission image and a first attenuation image; and a control unit selecting at least one of the first emission image and the first attenuation image generated by the image generation unit as an input image and generating and providing a final attenuation image by applying the input image to the learned deep learning algorithm.

Systems and methods for tracking eye movement during a diagnostic procedure include an eye tracker capturing images of an eye, and a control processor configured to detect an eye position and orientation in each of the images, determine an eye fixation position and orientation relative to an optical axis of the eye tracker, estimate eye fixation parameters based at least in part on the determined eye fixation position and orientation, and track the eye position and orientation by analyzing the images to determine the eye position and orientation relative to the eye fixation parameters. The eye fixation parameters may comprise a reference position and orientation of the eye when fixated. A histogram is constructed of detected eye positions and orientations and analyzed to determine an eye fixation position and orientation.

Systems and methods for processing electronic images from a medical device comprise receiving a first image frame and a second image frame from a medical device, and determining a region of interest by subtracting the first image frame from the second image frame, the region of interest corresponding to a visual obstruction in the first image frame and/or second image frame. Image processing may be applied to the first image frame and/or second image frame based on a comparison between a first area of the first image frame corresponding to the region of interest and a second area of the second image frame corresponding to the region of interest, and the first image frame and/or second image frame may be provided for display to a user.

A chest x-ray differential diagnosis system is operable to generate abnormality pattern data is generated for each of a received plurality of chest x-rays by identifying at least one pattern in each chest x-ray corresponding to an abnormality by utilizing a computer vision model that is trained on a plurality of training chest x-rays. Differential diagnosis data is generated for each chest x-ray based on the abnormality pattern data. Filtering parameters are received from a client device, and a filtered chest x-ray queue that includes a subset of chest x-rays is selected based on the filtering parameters and the differential diagnosis data is generated for transmission to the client device for display. Differential diagnosis data corresponding a chest x-ray indicated in chest x-ray selection data received from the client device is transmitted to the client device for display via the display device in conjunction with the chest x-ray.

An information processing apparatus includes a first selection unit that selects a second image to be compared with a first image from a plurality of candidate images, a setting unit, an overlap range calculation unit, an evaluation value calculation unit, and a second selection unit. The setting unit sets a plurality of combinations including two or more candidate images from among the plurality of candidate images. The overlap range calculation unit calculates an overlap range between the first image and each of the plurality of combinations. The evaluation value calculation unit calculates an evaluation value of each of the plurality of combinations based on the calculated overlap range. The second selection unit selects at least one combination from the plurality of combinations based on the evaluation value. The first selection unit selects the two or more candidate images included in the selected combination as the second image.

In order to improve the noise suppression in a video image stream 3 of a medical image recording system, the video image stream including a sequence of frames, it is provided that an image processing unit 5 of the image recording system analyses the video image stream 3 continuously in real time and determines at least one variability between successive image pixels of the frames, for example of spatially adjacent image pixels of frames and/or of image pixels of a plurality of the frames corresponding to one another spatially and temporally, in order, on the basis of the variability determined, to set at least one parameter of a noise suppression subsequently applied to the video image stream 3. As a result, the noise suppression can be adapted continuously to a current recording situation.

An endoscopic system includes an endoscopic imager configured to capture images of a target site within a living body and a processor configured to determine one or more image metrics for each one of a plurality of image frames captured over a time span, analyze changes in the image metrics over the time span, and determine a turbidity metric for the target site based on the analyzed changes in the image metrics.

Systems and methods for determining a heath status of a patient through an automated interview. One of the systems include one or more computers in one or more locations and one or more storage devices storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations including: providing, to a user interface of a user device, questions for a user to respond to in an interactive manner, in which each of the questions following the first questions is adaptive based on the user's response to one or more of the previous questions; capturing motion and appearance of the user in a video sequence while the user is responding to the questions; and analyzing the motion of the user in the video sequence to determine one or more indications of a disease or of a change in a disease progression.

A combination unit generates a plurality of composite two-dimensional images from a plurality of tomographic images acquired by performing tomosynthesis imaging on an object using different generation methods. In this case, the combination unit generates a first composite two-dimensional image having a quality corresponding to a two-dimensional image acquired by simple imaging or a second composite two-dimensional image in which a structure included in the object has been highlighted as at least one of the plurality of composite two-dimensional images.

A body thickness estimation unit estimates a body thickness of a subject for each pixel of at least one of a first radiographic image or a second radiographic image which includes a primary ray component and a scattered ray component, on the basis of the at least one of the first radiographic image or the second radiographic image. A bone part pixel value acquisition unit acquires a pixel value of a bone region of the subject from the first and second radiographic images. An information acquisition unit acquires bone mineral information indicating a bone mineral content of the bone region for each pixel of the bone region on the basis of imaging conditions in a case in which the at least one of the first radiographic image or the second radiographic image has been acquired, the body thickness for each pixel, and the pixel value of the bone region.