Abstract: A computer-implemented method is configured for scheduling multiple pathways in a schedule, each pathway including a collection of related events. The computer-implemented method includes training, with processor, a neural network with a repository of historical rescheduling data to create a trained data set. The computer-implemented method further includes assigning, with the processor, resources from a resource pool to each of the events of each of the pathways to create the schedule. The computer-implemented method further includes detecting, with the processor, a change in a resource assigned to an event. The computer-implemented method further includes automatically adjusting, with the processor, at least one other event in the schedule in response to the detected change based on the trained data set to predict an optimal adjustment to the schedule with a prediction model.

Abstract: A method for acquiring MR image data pertaining an organ, moving between a first extreme and a second extreme position such as in motion due to breathing of the patient to be examined. A scout film is registered of the organ concurrently with the registration of a signal that is indicative for the position of such organ. Real-time MR image data are acquired in at least one scan-plane of said organ, and the scan-plane is adjusted depending on the concurrently measured signal estimating the position of said organ. Several scan-planes are employed. For each individual scan-plane of said plurality of scan-planes, its position as a function of time is defined in relation to pre-selected organ-positions is measured with the scout-film. During the acquisition of the real-time MR image data the respective scan-positions are each individually adjusted, each scan-plane position thereby depending on the signal indicative for the position of the organ. In particular the invention is used in cardiac MR-imaging.

Abstract: The invention relates to the analysis of successive data sets. A local intensity variation is formed from such successive data sets, that is, from data values in successive data sets at corresponding positions in each of the data sets. A region of interest is localized in the individual data sets on the basis of the local intensity variation. In particular the time derivative of the local intensity variation is used to localize the region of interest. The invention can be used notably for cardiological applications so as to separate the image of the myocardium from a sequence of 3D magnetic resonance reconstruction images.

Abstract: The invention relates to the analysis of successive data sets. A local intensity variation is formed from such successive data sets, that is, from data values in successive data sets at corresponding positions in each of the data sets. A region of interest is localized in the individual data sets on the basis of the local intensity variation. In particular the time derivative of the local intensity variation is used to localize the region of interest. The invention can be used notably for cardiological applications so as to separate the image of the myocardium from a sequence of 3D magnetic resonance reconstruction images.