Various methods and systems are provided for mammogram imaging. In one example, a method for an x-ray mammography system includes determining, based on a pre-exposure image of a subject acquired with the x-ray mammography system before acquisition of one or more main images, that the subject has an implant; automatically adjusting one or more imaging parameters in response to determining that the subject has the implant; and acquiring the one or more main images with the adjusted one or more imaging parameters.

A processor functions as a trained neural network that derives an estimation result relating to a three-dimensional bone density of a bone part from a simple radiation image acquired by simply imaging a subject including the bone part, or a DXA scanning image acquired by imaging the subject by a DXA method. The trained neural network learns using, as teacher data, (i) two radiation images or the like acquired by imaging the subject including the bone part with radiation having different energy distributions, and a two-dimensional bone density of the bone part included in the two radiation images or the like, or (ii) the radiation image or the like of the subject or a bone part image representing the bone part of the subject, the two-dimensional bone density of the bone part included in the radiation image or the like, or the bone part image, and the three-dimensional bone density of the bone part of the subject.

A control apparatus including at least one processor that is configured to: capture a low-energy image by a radiography apparatus by emitting radiation having first energy to a subject into which a contrast medium has been injected, and then sequentially acquires each of a plurality of high-energy images captured by the radiography apparatus at different timings by emitting radiation having second energy higher than the first energy to the subject into which the contrast medium has been injected, sequentially derive a body movement amount of the subject from each of the plurality of high-energy images, and perform, in a case in which the derived body movement amount exceeds a threshold value, control of causing the radiography apparatus to re-capture the low-energy image before a next high-energy image is captured.

One embodiment of the present invention includes a computer-implemented method that includes receiving spectral computed tomography (CT) volumetric image data. The spectral CT volumetric image data include data for at least two different energies and/or energy ranges. The spectral CT volumetric image data is processed with a machine learning engine configured to map spectrally enhanced features extracted from the spectral CT volumetric image data onto fractional flow reserve (FFR) values to determine a FFR value. The FFR value is then visually presented.

Disclosed are an optimization method and system for a personalized contrast test based on deep learning, in which a contrast medium optimized for each individual patient is injected to implement optimum pharmacokinetic characteristics in a process of acquiring a medical image, the method including: obtaining drug information of a contrast medium and body information of a patient, in a contrast enhanced computed tomography (CT) scan; generating injection information of the drug to be injected into the patient by a predefined algorithm based on the drug information and the body information; injecting the drug into the patient based on the injection information, and acquiring a medical image by scanning the patient; and amplifying a contrast component in the medical image by inputting the medical image to a deep learning model trained in advance.

One or more example embodiments of the present invention relates to a method for providing vessel wall-related data. The method includes receiving spectral computed tomography data of an examination region, the examination region having a vessel; calculating a representation of a vessel wall of the vessel and at least one parameter map of the examination region based on the spectral computed tomography data; calculating the vessel wall-related data based on the representation of the vessel wall and the at least one parameter map of the examination region; and providing the vessel wall-related data.

A modular x-ray imaging system includes an application specific module, a base unit in communication with the application specific module, and a mechanical support configured to support the x-ray application specific module. The base unit and application specific module are configured to communicate by wired and/or wireless communication.

The present invention relates to an apparatus (10) for generating photon counting spectral image data, comprising: an input unit (20); a processing unit (30); and an output unit (40). The input unit is configured to receive non-photon counting X-ray spectral energy data. The processing unit is configured to implement a deep learning regression algorithm to generate photon counting X-ray spectral data, and the generation comprises utilization of the non-photon counting X-ray spectral energy data. The output unit is configured to output the photon counting X-ray spectral data.

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. The at least one processor is operably coupled to at least one of the detector units, and configured to acquire, via the detector units, imaging information. The imaging information includes edge information and interior information. The edge information corresponds to a contour boundary of tissue and the interior information corresponds to an intermediate portion of the tissue. The least one processor is configured to control the detector units to acquire a proportionally larger amount of imaging information for the contour boundary than for the intermediate portion.

An ensemble of at least two X-ray contrast agents includes X-ray contrast agent and a second X-ray contrast agent. The second X-ray contrast agent has an X-ray absorption whose change between at least two different X-ray photon energies differs significantly from the change of the X-ray absorption of the first X-ray contrast agent between the at least two different X-ray photon energies. An X-ray imaging method, an image reconstruction device, an X-ray imaging system are also disclosed.