Abstract: Systems, methods, and computer programs disclosed herein relate to training and using a machine learning model to generate contrast-enhanced magnetic resonance images.

Abstract: The present disclosure is concerned with the technical field of generation of artificial contrast-enhanced radiological images.

Abstract: Disclosed herein are systems and methods for generating artificial contrast-enhanced computed tomography (CT) images. An exemplary computer-implemented method involves receiving representations of an examination region of an examination object after administration of a contrast agent. The representations result from CT examination of the examination region at different X-ray energies. The method involves generating a representation of the contrast agent signals on the basis of the received representations (e.g., a signal intensity distribution brought about by the contrast agent in the examination region). The method involves generating a synthetic representation of the examination region comprising an ?-fold addition of the representation of the contrast agent signals to one of the received representations or to a virtual non-contrast agent representation of the examination region. ? is a negative or positive real number.