An X-ray diagnostic apparatus according to an embodiment includes processing circuitry configured to improve quality of fourth data corresponding to a fourth number of views that is smaller than a first number of views by inputting the fourth data to a learned model generated by performing machine learning with second data corresponding to a second number of views as input learning data, and third data corresponding to a third number of views that is larger than the second number of views as output learning data, the second data and the third data being acquired based on first data corresponding to the first number of views. The fourth data is data acquired by tomosynthesis imaging.

A radiological imaging apparatus is provided that includes a radiation source for emitting radiation, a radiation detector positioned to receive radiation emitted by the radiation source and generate radiation data, wherein the radiation data comprises a primary component and a secondary component, and a data processing system. The data processing system is configured to apply image transforms to the primary component using generating functions, build a scatter model basis using the transforms, adjust parameters in the scatter model to fit scatter using the scatter model basis, generate an estimated scatter image by using the fitted scatter model, and modify the radiation data using the scatter image to decrease the scatter in the radiation data thereby generating a scatter corrected image.

A trained model generation program causes a computer to implement a learning execution function of inputting first input image data representing a first input image generated by a first reconstruction method using compressed sensing and second input image data representing a second input image generated by a second reconstruction method different from the first reconstruction method to a machine learning device to execute machine learning, the second reconstruction method being an analytical reconstruction method, and causing the machine learning device to generate a trained model, and a trained model acquisition function of acquiring trained model data indicating the trained model. Then, input image data representing an input image is input to the trained model to generate a reconstructed image with improved image quality.

Method of calculating a scaled virtual grid for x-ray projection images, comprising providing at least a first and a second co-registered x-ray projection image Ij—j=1, 2, . . . of a desired anatomy of a patient (step S1), defining two points P.sub.1 and P.sub.2 of the desired anatomy in 3-D space and determining 3-D coordinates of the two points P.sub.1 and P.sub.2 thereby using the x-ray projection images (step S2), calculating the scaled virtual grid based on the determined 3-D coordinates of the two points P.sub.1 and P.sub.2 (step S3), and projecting and displaying the calculated grid to a user on at least one of the first and the second co-registered x-ray projection image (step S4).

A radiation imaging apparatus including: a first scintillator layer configured to convert a radiation (R) which has entered the first scintillator layer into light; a second scintillator layer configured to convert a radiation transmitted through the first scintillator layer into light; a fiber optic plate (FOP) provided between the first scintillator layer and the second scintillator layer; and an imaging portion configured to convert the light generated in the first scintillator layer and the light generated in the second scintillator layer into an electric signal.

An X-ray detector unit is disclosed. In an embodiment, the X-ray detector unit includes: at least one analysis unit to process electrical signals delivered from a coupled converter unit and operatable by an operating voltage; an adjustable voltage supply, coupled to the at least one analysis unit, to provide an adjustable supply voltage; an identification unit, assigned to the at least one analysis unit, to provide identification information about the at least one analysis unit in a readable manner; and a communication unit, coupled to the adjustable voltage supply, to read the identification information provided from the identification unit, and based upon the identification information provided, to adjust the adjustable voltage supply to equate the provided supply voltage to the operating voltage of the at least one analysis unit.

Method and/or apparatus embodiments for 3-D cephalometric analysis of a patient according to the application can display reconstructed volume image data from a computed tomographic scan of a patient's head including segmented dentition elements having an initial arrangement from one or more 2D/3D views; can compute and display a plurality of cephalometric parameters for the patient according to the reconstructed volume image data; then use the patient specific cephalometric parameters and population biometry data, to identify one or more maxillofacial/dental abnormalities; and compose patient specific treatment plans to correct selected dentition abnormalities using maxillofacial/dental structure, which can be composed in a final tooth arrangement in a final virtual CT volume. One or more aligners can be generated to incrementally move dentition from the initial arrangement to the final tooth arrangement.

A nuclear medicine (NM) multi-head imaging system is provided that includes a gantry, plural detector units mounted to the gantry, and at least one processor operably coupled to at least one of the detector units. The detector units are mounted to the gantry. Each detector unit defines a detector unit position and corresponding view oriented toward a center of the bore. Each detector unit is configured to acquire imaging information over a sweep range corresponding to the corresponding view. The at least one processor is configured to, for each detector unit, determine plural angular positions along the sweep range corresponding to boundaries of the object to be imaged, generate a representation of each angular position for each detector unit position, generate a model based on the angular positions using the representation, and determine scan parameters to be used to image the object using the model.

In valve modeling from medical scan data, chordae are modeled as a dense structure. Rather than attempting to provide the same number of chordae (e.g., 25) as found in a human valve, hundreds or thousands of chordae connectors are used. Since solving for lengths of so many chordae may be computationally intensive, the lengths of only a few are solved, and the lengths of the rest of the chordae are derived from the lengths of the few.

A radiographic imaging system includes a portable information terminal 16 and a console 18 which are plural control devices of which each one performs a control relating to imaging of a radiographic image and of which at least one is selectively used; and a terminal control unit 30 of the portable information terminal 16 and a control unit 50 of the console 18 that respectively function as a setting unit that sets, with respect to at least one of usage control devices which is control device to be selectively used, control content based on one usage control device in a case where the number of usage control devices is one, and sets control content based on a combination of plural usage control devices in a case where the number of usage control devices is plural.