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.

Improved imaging devices and methods. A portable SPECT imaging device may co-register with imaging modalities such as ultrasound. Gamma camera panels including gamma camera sensors may be connected to a mechanical arm. A coded aperture mask may be placed in front of a gamma-ray photon sensor and used to construct a high-resolution three-dimensional map of radioisotope distributions inside a patient, which can be generated by scanning the patient from a reduced range of directions around the patient and with radiation sensors placed in close proximity to this patient. Increased imaging sensitivity and resolution is provided. The SPECT imaging device can be used to guide medical interventions, such as biopsies and ablation therapies, and can also be used to guide surgeries.

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.

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.

An image processing system comprising includes: an acquisition unit that acquires a plurality of projection images obtained by tomosynthesis imaging in which radiation is emitted from a radiation source to a breast at different irradiation angles and a projection image is captured at each irradiation angle by a radiation detector; a tomographic image generation unit that generates a plurality of tomographic images in each of a plurality of tomographic planes of the breast, from the plurality of projection images; a composite two-dimensional image generation unit that generates a composite two-dimensional image from a plurality of images selected from among the plurality of projection images and the plurality of tomographic images; an information generation unit that generates correspondence relationship information representing a correspondence relationship between a position in the composite two-dimensional image and a depth of a tomographic plane corresponding to the position; a display controller that performs control of causing a display device to display the composite two-dimensional image; an acceptance unit that accepts region information representing a designated region designated with respect to the composite two-dimensional image displayed on the display device; and a designated tomographic image generation unit that generates, as a designated tomographic image, a tomographic image in a tomographic plane at a depth which corresponds to the designated region in the composite two-dimensional image and is specified on the basis of the correspondence relationship information, in a case where the acceptance unit accepts the region information, wherein in a case where the designated tomographic image is generated, the display controller further performs control of causing the display device to display the generated designated tomographic image.

A common area derivation unit derives a common area common to at least a part of a plurality of projection images corresponding to a plurality of radiation source positions which are generated by causing an imaging apparatus to perform tomosynthesis imaging in which a radiation source is moved relative to a detection unit to irradiate a subject with radiation at the plurality of radiation source positions according to movement of the radiation source. A display control unit emphasizes effective image areas, corresponding to the common area, of at least a part of a plurality of tomographic images representing a plurality of tomographic planes of the subject which are generated by reconstructing the plurality of projection images, or a composite two-dimensional image generated from the plurality of tomographic images, and displays at least the part of the tomographic images or the composite two-dimensional image on a display.

A method is for compression of breast tissue arranged between a paddle and a stage of a compression system for a mammography examination. The method includes generating a first compression of the breast tissue by building up a reference compression force by adjusting the paddle relative to the stage; comparing the first compression with a target compression; and adjusting the first compression to the target compression, the adjusting including exertion of a manual force on the paddle.

The present disclosure provides methods and systems directed to management and visualization of radiological data. A method for processing at least one medical image of a location of a body of a subject may comprise (a) retrieving, from a remote server via a network connection, the medical image; (b) identifying one or more regions of interest (ROIs) in the medical image, wherein the ROIs correspond to an anatomical structure of the location of the body of the subject; (c) annotating the ROIs with label information corresponding to the anatomical structure, thereby producing an annotated medical image; (d) generating educational information based at least in part on the annotated medical image; and (e) generating a visualization of the anatomical structure, based at least in part on the educational information.