Abstract: Proposed concepts thus aim to provide schemes, solutions, concept, designs, methods and systems pertaining to updating a magnetic field (B0) map of a subject during a magnetic resonance imaging (MRI) examination or during image reconstruction. In particular, when a subject moves, the magnetic field inside the magnetic bore changes. As a result, any B0 map obtained prior to the movement of the subject may be inaccurate. Accordingly, an initial B0 map is updated to reflect changes in the B0 map caused by the movement of the subject. This can be achieved by determining a B0 map of the subject based on a B0 prediction model, instead of spending additional scanning time to acquire another B0 map.

Abstract: Proposed concepts thus aim to provide schemes, solutions, concepts, designs, methods and systems pertaining to predicting a field perturbation map for magnetic resonance imaging (MRI) of a subject. In particular, the invention aims to provide a field perturbation map of the subject without the need for additional time-consuming scans of the subject. An accurate field perturbation map is necessary in order to obtain an MRI scan of the subject of high quality. Accordingly, a synthetic computed tomography (CT) image is generated by inputting an initial MRI magnitude image of the subject to an image conversion machine learning algorithm. Subsequently, a weighted susceptibility map of the subject is determined based on the synthetic CT image and the initial MRI magnitude image, which is in turn used to determine the field perturbation map of the subject.