Apparatus for identifying a patient, said apparatus comprising: a medical device for implantation into the patient; an optical identifier affixed to the device; wherein at least a portion of the optical identifier is radiopaque, whereby to generate a scannable X-ray image of the optical identifier when the medical device is imaged using X-ray.

A frequency resolution unit performs frequency resolution of a radiographic image to generate band images representing frequency components in a plurality of frequency bands. A reference image generation unit generates a reference image representing information associated with scattered radiation included in the radiographic image, and generates a plurality of band reference images corresponding to a plurality of frequency bands from the reference image. A band image conversion unit performs conversion between the corresponding pixels of the band reference images and the band images in the corresponding frequency bands to generate converted band images. A synthesis unit synthesizes the converted band images to generate a processed radiographic image with converted contrast.

According to one embodiment, an image processing device includes processing circuitry. The processing circuitry acquires a plurality of first image data indicating a plurality of time-sequential bloodstream images obtained after administering contrast agent to an object, and sets a target region on second image data. The second image data are generated based on the plurality of the first image data so as to indicate information on temporal change of pixel values of each pixel. Further, the processing circuitry selects a reproduction section by selecting at least reproduction-start image data and reproduction-end image data from the plurality of first image data based on bloodstream information of the target region.

Methods and systems are provided for boosting the contrast levels in an image reconstructed from projection data acquired at a single energy. In one embodiment, a method comprises modifying projection data corresponding to a material based on an absorption behavior of the material at a selected energy, wherein the projection data is acquired at an energy higher than the selected energy. In this way, contrast levels may be enhanced in an image reconstructed from projection data acquired at a typical single energy as though the image were reconstructed from projection data acquired at a lower energy.

Apparatus and methods are described including, using a computer processor, automatically identifying whether a given pixel within an image corresponds to a portion of an object. A set of concentric circles that are disposed around the pixel are sampled, and a first function is applied to each of the circles such that the circles are defined by a first set of rotationally invariant descriptors. A second function is applied to the set of circles to generate a second set of descriptors, each of which represents a difference between respective pairs of the circles. A third function is applied such that the second set of descriptors becomes rotationally invariant. The processor identifies whether the given pixel corresponds to the portion of the object, based upon the first and second sets of rotationally invariant descriptors. Other applications are also described.

A method for display of a fluoroscopic image sequence during ongoing image acquisition acquires and renders a first image of a subject on a display according to a first parameter setting, modifies the first parameter setting according to an operator instruction entered following acquisition of the first image, and applies the modified first parameter setting for acquiring and rendering one or more subsequent fluoroscopic images of the subject in the fluoroscopic image sequence.

An information processing method of an embodiment is a processing method of information acquired by imaging performed by a medical image diagnostic apparatus, the information processing method includes the steps of: on the basis of first subject data acquired by the imaging performed by the medical image diagnostic apparatus, acquiring noise data in the first subject data; on the basis of second subject data acquired by the imaging performed by a medical image diagnostic modality same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noises based on the noise data are added to the second subject data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and third subject data acquired by the imaging performed by the medical image diagnostic modality.

Disclosed is a medical imaging conversion method, automatically converting: at least one or more real x-ray images of a patient, including at least a first anatomical structure of the patient and a second anatomical structure of the patient, into at least one digitally reconstructed radiograph (DRR) of the patient representing the first anatomical structure without representing the second anatomical structure, by a single operation using either one convolutional neural network (CNN) or a group of convolutional neural networks (CNN) which is preliminarily trained to, both or simultaneously: differentiate the first anatomical structure from the second anatomical structure, and convert a real x-ray image into at least one digitally reconstructed radiograph (DRR).

An imaging control apparatus obtains a plurality of images at different radiation energies, the images having been obtained as a result of irradiating a subject with radiation whose energy changes during one shot, and detecting, a plurality of times, the radiation that has passed through the subject during the one shot; generates an energy subtraction image by performing energy subtraction processing using a plurality of images; and generates a difference image using a plurality of generated energy subtraction images.

The present disclosure provides a medical imaging method and system and a non-transitory computer-readable storage medium. The medical imaging method comprises obtaining an original image acquired by an X-ray imaging system, and post-processing the original image based on a trained network to obtain an optimized image after processing. The medical imaging system comprises a control module, configured to obtain an original image of an object; a learning network module, configured to post-process the original image to obtain a post-processed image based on user preferences selected by one or more users; and an optimized module, configured to optimize the learning network based on generated images sent to the learning network module, wherein the generated images comprise the post-processed image previously obtained by the learning network module.