The present invention relates to a device (1) for modifying an imaging of a TEE probe in X-ray data, a medical imaging system (100) for modifying an imaging of a TEE probe in X-ray data, a method for modifying an imaging of a TEE probe in X-ray data, a computer program element for controlling such device (1) and a computer readable medium having stored such computer program element. The device (1) comprises an X-ray data provision unit (11), a model provision unit (12), a position locating unit (13), and a processing unit (14). The X-ray data provision unit (11) is configured to provide X-ray data comprising image data of a TEE probe. The model provision unit (12) is configured to provide model data of the TEE probe. The position locating unit (13) is configured to locate a position of the TEE probe in the X-ray data based on the model data of the TEE probe. The processing unit (14) is configured to define a region in a predetermined range adjacent to the TEE probe as reference area. The processing unit (14) is configured to process the X-ray data of the reference area into estimated X-ray data of a region occupied by the TEE probe. The processing unit (14) is configured to modify the X-ray data in the region occupied by the TEE probe based on the estimated X-ray data.

The present invention relates to a method (200) for iterative material decomposition of multispectral image data. It is described to: a) provide (210) a plurality of spectral images of a region of interest comprising a body part; b) provide (220) a plurality of sets of material coefficients for a plurality of materials, wherein for a set of material coefficients each material coefficient is associated with a corresponding material, and wherein each set of material coefficients is associated with a corresponding spectral image of the plurality of spectral images; c) decompose (230) each spectral image of the plurality of spectral images into a plurality of material images and an offset image, wherein different material images correspond to different materials of the plurality of materials, and wherein a material image is represented by the material coefficient for the corresponding material multiplied by material concentrations at different image locations, and wherein the material coefficient is one of the material coefficients from the set of material coefficient for the corresponding spectral image; e) manipulate (240) at least one of the material images for each spectral image on the basis of at least one topological constraint relating to the body part to determine for each spectral image an updated plurality of material images and an offset image; f) recompose (250) a plurality of spectral images, wherein each recomposed spectral image is recomposed from the corresponding updated plurality of material images and the offset image; g) compare (260) intensities at image locations in a recomposed spectral image to the intensities at the image locations in its corresponding spectral image prior to recomposition to determine a plurality of corrections, wherein a correction is associated with an image location; i) modify (270) the updated plurality of material images for each spectral image comprising utilization of the corresponding plurality of corrections; j) iterate (280) steps e) to i) until convergence; and k) output (290) at least one of the recomposed spectral images at convergence and/or output at least one of the updated material images at convergence.

A radiography apparatus includes an operation controller that invalidates an operation of a collimator using a first operating unit in a case where a radiation generation unit is present vertically downward relatively to a radiography unit, and validates the operation of the collimator using the first operating unit in a case where the radiation generation unit is present vertically upward relatively to the radiography unit.

A pipe-line method for multi-label segmentation of anatomic structures in a medical image using a convolutional neural network trained with a weighted loss function takes into account underrepresentation of at least one anatomical structure in a ground-truth mask relative to other anatomical structures. Different architectures for the convolutional neural network are described.

A pipe-line method for multi-label segmentation of anatomic structures in a medical image using a convolutional neural network trained with a weighted loss function takes into account underrepresentation of at least one anatomical structure in a ground-truth mask relative to other anatomical structures. Different architectures for the convolutional neural network are described.

A curved compression element, such as a breast compression paddle, and imaging systems and methods for use with curved compression elements. A system may include a radiation source, a detector, and a curved compression element. Operations are performed that include receiving image data from the detector; accessing a correction map for the at least one compression paddle; correcting the image data based on the correction map to generate a corrected image data; and generating an image of the breast based on the corrected image data. The breast compression element generally has no sharp edges, but rather has smooth edges and transitions between surfaces. The breast compression paddle also includes a flexible material that spans a portion of a curved bottom surface of the breast compression paddle to define a gap. The flexible material may be a thin-film material such as a shrink wrap.

Apparatus having an x-ray source and a DR detector configured to travel cooperatively around a radiographic imaging axis. An imaging volume defines a spatial region to be imaged by the x-ray source and the DR detector. Radiopaque fiducials are selectively positioned in the imaging volume.

A method for training a function of an X-ray system that has a positioning mechanism such as a C-arm, a detector, and, in a beam path in front of the detector, an anti-scatter grid. Positioning of the detector at a large number of different positions occurs. The positioning mechanism is deflected and/or distorted. Recording of at least one X-ray photograph in each of the positions then takes place, and the method further includes machine learning of artifacts generated by the anti-scatter grid from all X-ray photographs for the function.

Tissue sample management systems include a central network, a medical professional system, and a pathology lab system for processing a tissue sample in a matrix having a sectionable code. At least the pathology lab system includes at least one imaging device, and the central network is configured to process images from the at least one imaging device to identify and record at least the sectionable code of the matrix. Methods for tissue sample processing include providing a matrix having a sectionable code and measurement marks, the matrix for receiving a tissue sample, and identifying the sectionable code from an image taken of the tissue sample in the matrix. Tissue sample-receiving matrices include a sectionable alphanumeric code or bar code, a tissue sample receptacle, and measurement marks formed along a sidewall thereof. The matrices include one or more proteins and one or more lipids.

An image processor applies computation processing to a plurality of CT images formed by irradiation of radiation of a plurality of energy levels to acquire monochromatic CT images. The image processor acquires a first energy level CT image formed by irradiation of first energy level radiation and a second energy level CT image formed by irradiation of second energy level radiation, applies a plurality of weighted computations to the first and second energy level CT images to compute a plurality of monochromatic CT images as a result of the weighted computations, segments a surrounding region of a highly-absorbent material circumferentially into a plurality of regions of interest having a predetermined area and calculates a standard deviation of the surrounding region by using a mean value of image data of each region of interest, for each monochromatic CT image, and selects a monochromatic CT image with a small standard deviation.