Abstract: It is provided a method, configured to modify medical image data acquired by different vendor-specific or type-specific medical imaging devices of a same medical imaging technology to look more like a common reference.

Abstract: Concepts for multi-energy dark-field (DAX) and phase-contrast (PC) X-ray imaging are proposed. One such concept comprise acquiring a set of low energy images with phase stepping and a set of high energy images (with or without phase stepping). Transmission, DAX, and PC images are obtained from the phase-stepped low energy images using phase retrieval. Also, a high energy transmission image is obtained from the high energy image(s). Based on the transmission image from the low energy images and the transmission image from the high energy image(s), a modified transmission image is generated.

Abstract: GC A phase contrast X-ray imaging system with field of view (FOV) visualization and a holder arranged to hold a grating and a light field projector to enable the FOV illumination, for an operator to visualize the region on a subject that will be irradiated with X-rays. By taking advantage of the holder of the necessary object in the X-ray beam path (i.e. the grating) to hold a light field projector to illuminate the FOV, the solution is naturally more compact to implement and the mechanical complexity is kept to a minimum. The light field projector provides a sharp, well-defined area with a distinct outline of the FOV.

Abstract: The present invention relates to image processing. In order to facilitate a sustainable infrastructure for training of AI algorithms, an imaging system is proposed with a mobile annotation device to receive an image acquired by the medical imaging apparatus (e.g. x-ray, CT or MRI scanner) in real-time, that is, during the imaging session. The image acquired by the medical imaging apparatus is then displayed, thereby allowing the user to annotate the acquired image. The user annotation may comprise one or more of a recommended workflow in relation to the patient, an indication of an image quality in relation to the first image, an indication on a medical finding, a priority information representing the urgency of the medical finding. The acquired image and the user annotation are then stored in a database, thereby creating a training database for training of the AI algorithm.