X-ray imaging systems and methods comprising, at least, an x-ray source, an x-ray detector, and a collimator assembly. The collimator assembly comprising a computer, a display, a camera, an x-ray source to object (patient) measuring device to measure source to object distance (SOD), and a plurality of metallic barriers used to manipulate a size and shape of X-ray beams, thereby also reducing the volume of irradiated tissue in the patient. The collimator may comprise computer-controlled motorized shutters to admit radiation into the region defined by the adjustable beam-defining components of the collimator of an X-ray apparatus. In some embodiments, the plurality of metallic barriers may be a fixed cone, or a cone comprised of movable plates.

An X-ray tube billing system for a fluoroscopic apparatus for non-destructive inspection configured to perform X-ray fluoroscopy on a subject using an X-ray tube includes a fluoroscopy time detector configured to detect a length of time during which the X-ray fluoroscopy has been performed using the X-ray tube, and a billing amount calculator configured to calculate a billing amount related to use of the X-ray tube based on the length of time during which the X-ray fluoroscopy has been performed, which is detected by the fluoroscopy time detector.

An X-ray single-pixel camera based on X-ray computational correlated imaging, which belongs to the technical research fields of X-ray computational correlated imaging and X-ray single-pixel imaging. The X-ray single-pixel camera includes: an X-ray modulation system (3), an X-ray modulation control system (4), an X-ray single-pixel detector (5), a main control system unit (6), a time synchronization system (7) and a computational imaging system (8). The main control system unit (6) controls each module through software; the time synchronization system (7) controls synchronization of each module for automatic collection; and the computational imaging system (8) is configured to perform a second-order correlated computation or a compressed sensing computation or a deep learning computation on the signals collected by the X-ray single-pixel detector (5) and a preset modulation matrix, so as to obtain an image of an object under test. The X-ray single-pixel camera based on X-ray computational correlated imaging, provided by the present invention, realizes single-pixel imaging, greatly reduces the sampling number while ensuring the imaging quality, and reduces the X-ray radiation dose in an imaging process.

An X-ray fluoroscopic imaging apparatus includes a fluoroscopic recording button that an operator operates to record a fluoroscopic image as a still image or a moving image in a storage during fluoroscopic imaging and record, as the still image or the moving image in the storage, the fluoroscopic image recorded in a temporary storage after completion of the fluoroscopic imaging.

A correction apparatus includes an acquisition unit that acquires first image data representing a radiographic image generated by the radiation detector which is irradiated with the radiation in a state of being provided on a portion of an object to be inspected different from a portion to be inspected so as to be bent along an outer shape of the object to be inspected, and a correction unit that generates correction data on the basis of the first image data and corrects second image data representing the radiographic image generated by the radiation detector which is irradiated with the radiation in a state of being provided on the portion to be inspected so as to be bent along the outer shape of the object to be inspected.

[Problem] To make it possible to perform quantitative examination of slight weight unevenness of a material, and to provide a method for manufacturing a carbon fiber composite material in which weight unevenness is suppressed through the examination step. [Solution] The above problem can be solved by a method for manufacturing a deposit containing a thermoplastic resin and carbon fibers and having a side of 300 mm or more, wherein the composite is manufactured through the following steps. Step 101: an examination step of examining the weight of an aggregate of carbon fibers or the deposit non-destructively. Step 201: a step of ascertaining weight unevenness on the basis of the result of examination in step S101. Step 301: a step of adding an aggregate of discontinuous carbon fibers and/or a thermoplastic resin to a weight-lacking area on the basis of the weight unevenness of step 201 so as to reduce the weight unevenness.

Disclosed herein is a method comprising: causing emission of characteristic X-rays of a chemical element introduced into a human body; capturing images of a portion of the human body with the characteristic X-rays; determining a three-dimensional distribution of the chemical element in the portion based on the images.

The moving object tracking apparatus emphasizes an image with specific size in each of fluoroscopic images derived from two or more paired fluoroscopic radiographic devices, obtains a value indicating certainty degree of detecting a candidate position of the object to be tracked on the image subjected to the emphasizing process, extracts the candidate position based on the value indicating the certainty degree of detection, calculates a value indicating a correlation between the candidate position extracted from images picked up from two or more directions, and a position of the fluoroscopic radiation generator, detects the position of the object to be tracked based on the value indicating the certainty degree of detection, and the value indicating the correlation, and controls irradiation of radiation to an irradiation target based on the detected position of the object to be tracked.

In one embodiment, a computing system may access design data of a printed circuit board to be produced by a first manufacturing process. The system may analyze the design data of the printed circuit board using a machine-learning model, wherein the machine-learning model is trained based on X-ray inspection data associated with the first manufacturing process. The system may automatically determine one or more corrections for the design data of the printed circuit board based on the analysis result by the machine-learning model.

In one embodiment, a computing system may access design data of a printed circuit board to be produced by a manufacturing process. The system may determine one or more corrections for the design data of the printed circuit board based on one or more correction rules for correcting one or more parameters associated with the printed circuit board. The system may automatically adjust one or more of the parameters associated with the design data of the printed circuit board based on the one or more corrections. The adjusted parameters may be associated with an impedance of the printed circuit board. The one or more corrections may cause the impendence of the printed circuit board to be independent from layer thickness variations of the printed circuit board to be produced by the manufacturing process.