The present disclosure provides an X-ray exposure area regulation method, a storage medium, and an X-ray system. The X-ray exposure area regulation method may include: monitoring the state of an object under test (OUT) in real time and acquiring an initial image of the OUT when the state of the OUT satisfies the preset condition, determining an area of interest (AOI) in said initial image, and setting said X-ray exposure area based on the information of said AOI. Automatic regulation of an exposure area in the X-ray system can be realized according to the OUT. This not only facilitates operations and improves test efficiency, but also frees patients from exposure to unnecessary radiations.

Systems and methods for rapid, accurate, fully-automated, brain hemorrhage deep learning (DL) based assessment tools are provided, to assist clinicians in the detection & characterization of hemorrhages or bleeds. Images may be acquired from a subject using an imaging source, and preprocessed to cleanup, reformat, and perform any needed interpolation prior to being analyzed by an artificial intelligence network, such as a convolutional neural network (CNN). The artificial intelligence network identifies and labels regions of interest in the image, such as identifying any hemorrhages or bleeds. An output for a user may also include a confidence value associated with the identification.

An abnormal site detection unit detects an abnormal site included in a medical image acquired based on radiation transmitted through a subject. A position specifying unit generates positional information for specifying a position of the abnormal site based on a feature point of the subject included in the medical image in a case where the abnormal site is detected in the medical image. An information generation unit generates additional imaging instruction information, including information that indicates a type of additional imaging based on the abnormal site and information that indicates the position of the abnormal site based on the positional information, for instructing at least one additional imaging.

A brain-function image data augmentation method includes: a step of providing a target database including a plurality of image-data information; a step of, based on a plurality of image-data expected information in an expectation-value database, calculating a ratio of the plurality of image-data expected information with respect to different ages; a step of, based on the plurality of image-data information and the ratio, obtaining image-data ratio information with respect to an estimated age; a step of establishing a relationship for each pair of the image-data information and the image-data expected information; a step of, based on the relationship, the ratio and the image-data information, calculating an estimated image-data information with respect to the estimated age; and, a step of combining linearly the estimated image-data information and the image-data ratio information so as to generate an augmented image-data information with respect to the estimated age.

An image inspection device includes a radiation image acquisition unit that acquires a radiation image obtained by imaging a subject using radiation, an imaging condition recognition unit that recognizes an imaging condition relating to an imaging direction and/or laterality of the subject reflected in the radiation image, and a marker superimposition unit that superimposes, on the radiation image, a marker indicating the imaging direction and/or laterality of the subject reflected in the radiation image by using a result of the recognition of the imaging condition recognition unit.

This X-ray fluoroscopic imaging apparatus is provided with: an imaging unit; an X-ray image acquisition unit for acquiring an X-ray image; a blood vessel extracted image acquisition unit for acquiring a blood vessel extracted image; a composite image generation unit for generating a second composite image in which the X-ray image and the blood vessel extracted image are composed; and a region of interest setting unit for setting a region of interest in which a device is reflected. The composite image generation unit is configured to generate the second composite image by aligning the positions of the X-ray image and the blood vessel extracted image, based on the device and the blood vessel image reflected in the region of interest.

A computer-implemented method for determining and evaluating an objective tumor response to an anti-cancer therapy using cross-sectional images can include receiving cross-sectional images of digital medical image data and identifying target lesions within the cross-sectional images. For each of the target lesions, a target lesion type and anatomical location is identified, a segmenting tool is activated for segmenting the target lesions into regions of interest, lesion metrics are automatically extracted from the regions of interest according to tumor response criteria, and conformity of target lesion identification is monitored using rules associated with the tumor response criteria, prompting a user to address any nonconforming target lesion. The method also includes receiving a presence/absence of metastases, determining changes in lesions metrics, and deriving an objective tumor response based on the tumor response criteria.

The present invention is disclosing several methods related to intra-body navigation of radiopaque instrument through natural body cavities. One of the methods is disclosing the pose estimation of the imaging device using multiple images of radiopaque instrument acquired in the different poses of imaging device and previously acquired imaging. The other method allows to resolve the radiopaque instrument localization ambiguity using several approaches, such as radiopaque markers and instrument trajectory tracking.

Systems and methods for image reconstruction are provided. The methods may include obtaining a first image sequence of a subject and obtaining an initial input function that relates to a concentration of an agent in blood vessels of the subject with respect to time. The first image sequence may include one or more first images generated based on a first portion of scan data of the subject. The methods may further include, for each of a plurality of pixels in the one or more first images, determining at least one correction parameter associated with the pixel and determining, based on the initial input function and the at least one correction parameter, a target input function The methods may further include generating one or more target image sequences related to one or more dynamic parameters based at least in part on a plurality of target input functions.

An image acquisition unit acquires two radiographic images based on radiation which has different energy distributions and has been transmitted through a subject including a soft part and a bone part. A subtraction unit performs weighting subtraction using a predetermined initial weighting coefficient between corresponding pixels of the two radiographic images to derive a soft part image obtained by extracting the soft part of the subject and a bone part image obtained by extracting the bone part of the subject. A weighting coefficient derivation unit derives a new weighting coefficient on the basis of a pixel value of the bone part included in the bone part image. The subtraction unit performs the weighting subtraction on the two radiographic images using the new weighting coefficient to derive a new soft part image and a new bone part image.