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 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.

Various systems are provided for non-uniform thickness and/or sampling of slabs of the breast to present DBT acquisitions. A method for generating a patient image as a set of slabs representing an imaged object, the method comprising acquiring a tomosynthesis projection, reconstructing a series of slab images, each slab representing a portion of a breast, and a plurality of slabs of non-uniform thickness and/or non-uniform sampling in a 3D reconstructed domain defined by x-, y-, and z-axes.

X-ray devices and systems are described in this application. In particular, this application describes x-ray devices and systems that are used for three-dimensional (3D) image reconstruction with uncertain geometry. The x-ray imaging system contains an arm configured to be moved around an object to be imaged, a light weight, low power x-ray source attached to the arm, an x-ray detector configured to move complimentary to the x-ray source to capture multiple two-dimensional (2D) images in a solid angle path outside of a planar arc, 3D position and orientation tracking devices configured to capture the geometric position and orientation of the x-ray source and detector when each 2D projection image is captured, and a processor configured to construct a three dimensional (3D) image from the multiple 2D images using a reconstruction algorithm. These x-ray systems are lighter, more maneuverable, and less expensive than convectional CT x-ray systems because the geometry tracking devices combined with the processor and algorithm enable e generation of 3D images without the complex, precise, heavy, and expensive mechanical system that fixes the precise geometry of each 2D projection image to a high degree of accuracy. Other embodiments are described.

Exemplary method and apparatus embodiments according to the applications can provide calibration of a dental scanning device. An exemplary dental apparatus can include a sensing apparatus including at least one lens and a sensor that is configured to obtain one or more images of at least one surface position, and a calibration target including a spatial light modulator configured to form a prescribed set of calibration patterns, and whose display plane corresponds to the at least one surface position.

A medical image processing apparatus of an embodiment includes processing circuitry. The processing circuitry is configured to acquire medical image data on the basis of tomosynthesis imaging of a test object, and input the acquired medical image data of the test object to a trained model to acquire a two-dimensional image data, the trained model being generated by learning of two-dimensional image data on the basis of X-ray imaging of a person and image data on the basis of tomosynthesis imaging of the person who is subjected to the X-ray imaging.

A method for processing breast tomosynthesis images includes: obtaining multiple breast images, performing binarization operation and pooling operation to at least one marked breast image to obtain a lesion mask; training a convolutional neural network (CNN) according to the breast images and the lesion mask; obtaining multiple cropped images from the breast images of different layers according to a particular two-dimensional position, and inputting each cropped image into the CNN to obtain cropped heat maps; inputting the cropped heat maps into a recurrent neural network (RNN) in an order of the levels to obtain output heat maps, and combining the output heat maps to obtain a integrated heat map, and training the RNN according to the integrated heat map and the lesion mask.