Abstract: A method for separately acquiring and outputting diagnostic nuclear magnetic resonance images based on water and fat derived signals, which method provides that the separation of the signal components corresponding to the intensity of the resulting pixels or voxels derived from the water of tissues in the body under examination from those signal components derived from the fat tissues in said body under examination of the step is performed by means of a machine learning algorithm, or with automatic learning and which algorithms are configured to classify the signal component as derived from water or fat tissues in the body under examination.

Abstract: Shimming a magnetic field includes measuring the magnetic field and sampling it in a plurality of locations; defining a grid for positioning correction elements; calculating the position and magnitude parameters of one or more correction elements to obtain predetermined target values of the field characteristics, wherein an algorithm has been trained by a database of known cases in which each record links a certain initial magnetic field of a magnetic structure to the pattern of correction elements on a positioning grid for these correction elements in the magnet structure, thereby delivering as an output a pattern of correction elements on the positioning grid, which contributions to the magnetic field of the magnet structure generate a magnetic field which at least best approximates or meets the predetermined target values of the field characteristics.

Abstract: A method for separately acquiring and outputting diagnostic nuclear magnetic resonance images based on water and fat derived signals, which method provides that the separation of the signal components corresponding to the intensity of the resulting pixels or voxels derived from the water of tissues in the body under examination from those signal components derived from the fat tissues in said body under examination of the step is performed by means of a machine learning algorithm, or with automatic learning and which algorithms are configured to classify the signal component as derived from water or fat tissues in the body under examination.

Abstract: A method of reconstructing an MRI or ultrasound biomedical image, based on compressed sensing includes exciting a body under examination; acquiring image data from the generated signals, wherein the signals are acquired by pseudo-random undersampling; reconstructing the image using a nonlinear iterative algorithm for minimizing an optimization function containing one or more terms for image data sparsity in one or more predetermined domains with a data fidelity constraint term ensuring fidelity to the acquired image data; wherein two or more sets of image data are acquired from the generated signals, each data set being acquired in a different undersampling scheme and/or a different acquisition mode such as to make expected and unavoidable artifacts incoherent; each of the acquired image data set is multiplied by a correction matrix ?.