An X-ray diagnostic apparatus according to an embodiment includes processing circuitry configured to improve quality of fourth data corresponding to a fourth number of views that is smaller than a first number of views by inputting the fourth data to a learned model generated by performing machine learning with second data corresponding to a second number of views as input learning data, and third data corresponding to a third number of views that is larger than the second number of views as output learning data, the second data and the third data being acquired based on first data corresponding to the first number of views. The fourth data is data acquired by tomosynthesis imaging.

X-ray backscatter imaging (XBI) methods and systems are provided that enable depth-sensitive information to be obtained from images acquired during a single scan from a single side of an object being imaged. The depth-sensitive information is used in combination with other image information acquired during the scan to produce high-resolution 2-D or 3-D images, where at least one of the dimensions of the 2-D or 3-D image corresponds to depth in the object.

Image processing system (IPS), comprising an input interface (IN) for receiving an input image (IM) based on projection data (π) collected in a limited angle scan along different projection directions by an imaging apparatus (IA). A directional analyzer (DA) to compute a direction component for border regions in the input image. A directional discriminator (DD) is to discriminate border regions based on whether or not their direction component is along at least one of the projection directions.

Digital Breast Tomosynthesis allows for the acquisition of volumetric mammography images. The present invention allows for novel ways of viewing such images to detect microcalcifications and obstructions. In an embodiment a method for displaying volumetric images comprises computing a projection image using a viewing direction, displaying the projection image and then varying the projection image by varying the viewing direction. The viewing direction can be varied based on a periodic continuous mathematical function. A graphics processing unit can be used to compute the projection image and bricking can be used to accelerate the computation of the projection images.

The present invention provides a ranging method based on laser-line scanning imaging to effectively suppress interference of extreme weather on imaging. The method includes the following steps: acquiring priori reference images for a fixed laser-line scanning system, including respectively placing reference whiteboards at different distances, projecting line laser beams to the whiteboards, and acquiring the reference images by using a camera; placing a laser-line scanning device in a real scene, causing the laser-line scanning device to respectively emit line lasers at different angles, and acquiring an image at each scanning angle by using a camera; and performing fusion calculation on the acquired scanning image in the real scene and the priori reference images by using a ranging algorithm based on laser-line scanning, and extracting distance information of a surrounding object, to implement environment perception.

An image processing device generates a tomographic image group from a plurality of projection images, inputs the generated tomographic image group to a tomographic image estimation model, which is a trained model generated by performing machine learning on a machine learning model using learning data composed of a set of correct answer data that is three-dimensional data indicating a three-dimensional structure and of a virtual tomographic image group generated from a plurality of virtual projection images, onto which the three-dimensional structure has been projected by performing pseudo-projection on the three-dimensional structure with radiation at a plurality of virtual irradiation positions using the three-dimensional data, and which receives the tomographic image group as an input and outputs an estimated tomographic image group, and acquires the estimated tomographic image group output from the tomographic image estimation model.

The medical image processing apparatus according to the present embodiment includes processing circuitry. The processing circuitry is configured to acquire volume data generated based on tomosynthesis imaging of a subject. The processing circuitry is configured to set a virtual focal point at a position different from a focal position in the tomosynthesis imaging. The processing circuitry is configured to generate a pseudo projection image based on the virtual focal point and the volume data.

An image generation method is described, comprising obtaining a plurality of 2D images through an object to be imaged, obtaining a 3D image data set of the object to be imaged, and registering the 2D images with the 3D image data set. The method then further includes defining an image reconstruction plane internal to the object, being the plane of an image to be reconstructed from the plurality of 2D images. Then, for a pixel in the image reconstruction plane, corresponding pixel values from the plurality of 2D images are mapped thereto, and the mapped pixel values are combined into a single value to give a value for the pixel in the image reconstruction plane. Another aspect of the method provides for chatter removed from the image. In a medical imaging context this can provide for “de-boned” images, allowing soft tissue to be more clearly seen.

A method for processing tomosynthesis images of an object of interest, using an imaging system, the imaging system comprising an X-ray source positioned facing a detector on which the object of interest is positioned. With the method of an embodiment of the invention, it is possible to display a three-dimensional (3D) reconstruction slice, as well as a two-dimensional (2D) image of the object of interest.

A method for generating a combined projection image from a medical inspection object, includes steps of capturing a set of initial projection images; reconstructing a first and a second three-dimensional volume from the set of initial projection images; generating a first re-projection image from the first three-dimensional volume and a second re-projection image from the second three-dimensional volume; weighting the first re-projection image and the second re-projection image; and combining the weighted first re-projection image and second re-projection image for generating the combined projection image.