A modular X-ray source and method for replacement of such an X-ray source are disclosed. The source is inside a consumable modular enclosure where the entire assembly is swapped out during maintenance. The enclosure covers an X-ray tube, high voltage circuit boards 6 and cooling insulating oil are arranged inside the module enclosure. The enclosure structure includes an X-ray window, connector engagement alignment guide and electrical connectors. The modular X-ray source is used in a multiple source tomosynthesis imaging system where multiple pulsed X-ray sources are utilized. The easy replacement of X-ray tube assembly inside the consumable modular enclosure results in lower maintenance cost and overall reliable X-ray imaging machine. The modular source has potential to increase the machine volume in the field and create new standards for replaceable modular X-ray source.

A combination unit generates a plurality of composite two-dimensional images from a plurality of tomographic images acquired by performing tomosynthesis imaging on an object using different generation methods. In this case, the combination unit generates a first composite two-dimensional image having a quality corresponding to a two-dimensional image acquired by simple imaging or a second composite two-dimensional image in which a structure included in the object has been highlighted as at least one of the plurality of composite two-dimensional images.

Systems and methods for tomosynthesis with an x-ray filter are disclosed. The present technology provides performing breast tomosynthesis in the presence of an x-ray filter. For example, an x-ray filter may be placed between an x-ray source and breast tissue. The filter may proportionally filter out a subset of the energies emitted by the x-ray source. A filter may include characteristics to filter x-ray energies based on a k-edge of a contrast agent introduced into the breast, such that the breast tissue has relatively greater exposure to x-ray energies above the k-edge of the contrast agent to illuminate the contrast agent without substantial illumination of other breast tissue. Thus, tomosynthesis images similar to those obtained using subtraction may be acquired without software-based contrast enhancing techniques.

A processor is configured to generate a structure-highlighted synthesized two-dimensional image from a plurality of tomographic images, to detect a structure of interest from the plurality of tomographic images or the structure-highlighted synthesized two-dimensional image, and to set at least some of the plurality of tomographic images as either storage-required images or non-storage-required images based on a detection result of the structure of interest.

A C-arm X-ray apparatus includes an x-ray emitter (5) and an X-ray detector (4) which are maintained on a C-arm (2) mounted on a reference plane. The x-ray emitter (5) has nanorods as electron emitters and has an elongated structure which is at least partially aligned along a surface normal of the reference plane.

Systems and methods for mapping a region of interest within breast tissue utilize multiple layers of information to produce a unique digital fingerprint of breast tissue. X-ray and ultrasound imaging is combined with elastography and Doppler to create an architectural map of a breast including coordinates to mark one or more regions of interest. The architectural map can be utilized during future imaging procedures and surgeries to automatically and virtually indicate the location of previously biopsied lesions. The architectural map can be displayed on a user interface of a computing device to guide a user to the region of interest during imaging.

An image processing device includes at least one processor. The processor detects a specific structural pattern indicating a lesion candidate structure for a breast in a series of a plurality of projection images obtained by performing tomosynthesis imaging on the breast or in a plurality of tomographic images obtained from the plurality of projection images, synthesizes the plurality of tomographic images to generate a synthesized two-dimensional image, specifies a priority target region, in which the specific structural pattern is present, in the synthesized two-dimensional image, and performs determination regarding a diagnosis of a lesion on the basis of the synthesized two-dimensional image and the priority target region.

A learning device detects a first region of interest including a calcification and a second region of interest including an other lesion on the basis of any one of a composite two-dimensional image obtained from a plurality of projection images captured by tomosynthesis imaging or a plurality of tomographic images, the tomographic image, or a normal two-dimensional image, and trains an image generation model, in which a weight for the first region of interest is largest and a weight for the second region of interest is set to be equal to or larger than a weight for a region other than the first region of interest and the second region of interest, by updating a weight for a network of the image generation model on the basis of a loss between a pseudo two-dimensional image output by the image generation model and the normal two-dimensional image and/or the composite two-dimensional image to reduce the loss.

One or more example embodiments relates to a system for calibration and/or for quality control of an FFS X-ray system, a corresponding FFS X-ray system, a control facility suitable for it and to a method for calibration and/or for quality control of the FFS X-ray system.

A method and system for creating a dynamic self-learning medical image network system, wherein the method includes receiving, from a first node initial user interaction data pertaining to one or more user interactions with the one or more initially obtained medical images; training a deep learning algorithm based at least in part on the initial user interaction data received from the node; and transmitting an instance of the trained deep learning algorithm to the first node and/or to one or more additional nodes, wherein at each respective node to which the instance of the trained deep learning algorithm is transmitted, the trained deep learning algorithm is applied to respective one or more subsequently obtained medical images in order to obtain a result.