A method may include obtaining an original image. The method may also include determining a plurality of decomposition coefficients of the original image by decomposing the original image. The method may also include determining at least one enhancement coefficient by performing enhancement to at least one of the plurality of decomposition coefficients using a coefficient enhancement model. The method may also include generating an enhanced image corresponding to the original image based on the at least one enhancement coefficient.

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.

An image processing method performed by a terminal is provided. A molybdenum target image is obtained, and a plurality of candidate regions are extracted from the molybdenum target image. In the molybdenum target image, a target region is marked in the plurality of candidate regions by using a neural network model obtained by deep learning training, a probability that a lump comprised in the target region is a target lump being greater than a first threshold, a probability that the target lump is a malignant tumor being greater than a second threshold, and the neural network model being used for indicating a mapping relationship between a candidate region and a probability that a lump comprised in the candidate region is the target lump.

Disclosed herein is a system, comprising: a radiation source configured to cause emission of characteristic X-rays of a chemical element in a portion of a human body by generating and directing radiation to the portion; a first image sensor configured to capture a set of images of the portion using the characteristic X-rays; and a second image sensor configured to capture a set of tomograms using the radiation that has transmitted through the portion.

An imaging member including: a pressing member that presses a breast of a subject; and an ultrasonography member that has a first surface on which an acoustic matching member having fluidity is provided, and is provided such that a second surface on a side opposite to the first surface is provided on a surface of the pressing member, which is on a side opposite to a surface that comes into contact with the breast, via a coupling material having lower fluidity than the acoustic matching member.

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.

A multimodal system for breast imaging includes an x-ray source, and an x-ray detector configured to detect x-rays from the x-ray source after passing through a breast. The system includes an x-ray detector translation system operatively connected to the x-ray detector so as to be able to translate the x-ray detector from a first displacement from the breast to a second displacement at least one of immediately adjacent to or in contact with the breast. The system includes an x-ray image processor configured to: receive a CT data set from the x-ray detector, the CT data set being detected by the x-ray detector at the first displacement; compute a CT image of the breast; receive a mammography data set from the x-ray detector, the mammography data set being detected by the x-ray detector at the second displacement; and compute a mammography image of the breast.

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.