Abstract: The present invention describes a method for simulation of a magnetic resonance (MR) scanner in an MRI simulator, said method comprising—input of data parameters into a web interface of the MRI simulator, wherein the input of data parameters is at least a pulse sequence and an anatomical model;—connection of the web interface with a cloud-based simulator engine of the MRI simulator for transfer of data parameters to the cloud-based simulator engine, said method involving—importing a pulse sequence calculation model;—setting input data; and—performing a slice selection in an obtained image in the web interface; said method also involving—recalculation of the data parameters for the provision of one or more simulated MR signals, said recalculation being performed in the loud, and wherein the method also comprises—reconstruction of an MR image based on said one or more simulated MR signals, said econstruction of an MR image being performed in the cloud; and—sending the MR image to the web interface.

Abstract: The present invention describes a method in the field of a magnetic resonance imaging (MRI), said method comprising creating an object MR image in an MRI simulator unit: performing an image/parameter comparison of the object MR image and parameter(s) thereof with a second MR image and/or a corresponding set of parameters functioning as a reference: wherein the comparison is based on at least comparing the anatomical position of the object MR image and/or parameter(s) thereof and the second MR image and/or the corresponding set of parameters, said method also comprising providing automatic feedback on the image/parameter comparison and providing the same in a user interface of the MRI simulator unit.

Abstract: A method for generating training datasets for AI applications includes providing an MRI simulator and input thereto. The input is a pulse sequence and an anatomical model. The method includes selecting the position and/or orientation of a plane/volume of interest of the anatomical model, and executing the MRI simulator, producing a simulated MR image. The method includes obtaining produced synthesized MR images and/or label maps for each synthesized MR image, obtaining a training dataset based on all obtained produced MR images and/or label maps possible to produce for each MR image, and building a computer model for synthesized MR images and/or label maps using an SBR framework. The procedure may be repeated with different pulse sequences and/or the same pulse sequence, by amending the position and/or orientation of a plane/volume of interest of the anatomical model, and/or by amending properties of the anatomical model, and/or with another anatomical model.

Abstract: The present invention describes method directed to magnetic resonance (MR) imaging simulation, said method comprising—partitioning a pulse sequence in parts that have RF excitation and parts that do not have RF excitation;—for each of the parts that do not have the RF active, called gradient parts, performing compression of the gradient parts and then representing signals driving the gradients as an accumulation of area until a readout time point; and then performing the simulation.

Abstract: The present invention describes a method for simulation of a magnetic resonance (MR) scanner in an MRI simulator, said method comprising—input of data parameters into a web interface of the MRI simulator, wherein the input of data parameters is at least a pulse sequence and an anatomical model;—connection of the web interface with a cloud-based simulator engine of the MRI simulator for transfer of data parameters to the cloud-based simulator engine, said method involving—importing a pulse sequence calculation model;—setting input data; and—performing a slice selection in an obtained image in the web interface; said method also involving—recalculation of the data parameters for the provision of one or more simulated MR signals, said recalculation being performed in the loud, and wherein the method also comprises—reconstruction of an MR image based on said one or more simulated MR signals, said econstruction of an MR image being performed in the cloud; and—sending the MR image to the web interface.

Abstract: The present invention describes method directed to magnetic resonance (MR) imaging simulation, said method comprising—creating an empty 3D slice corresponding to a prescribed slice;—placing the empty 3D slice on the transversal XY plane of an image of an anatomical model;—calculating steps of rotation and translation that if applied would bring a volume-of-interest on the transversal XY plane;—calculating steps for undoing the rotation and translation performed;—applying the steps for undoing the rotation and translation performed on the empty 3D slice so as to bring it at the position of the prescribed slice in 3D space; and—calculating interpolated values of properties of the anatomical model at points of the empty 3D slice which have been placed at the position of the prescribed slice in 3D space, preferably wherein the method also involves a subsequent step of one-to-one matching of the calculated interpolated values with the corresponding points of the empty 3D slice on the transversal XY plane before ca

Abstract: The present invention describes a method for generation of training datasets for artificial intelligence (AI) applications in MRI (Magnetic Resonance Imaging), said method comprising providing an MRI simulator; providing input to the MRI simulator, said input being in the form of a pulse sequence and a computer-based anatomical model; executing the MRI simulator and thus producing a simulated artificial MR image; repeating the same procedure with different pulse sequences and/or the same pulse sequence, wherein when using the same pulse sequence then amending the characteristics of the same pulse sequence and/or amending the characteristics of the MR simulation when executing the MRI simulator; optionally also by amending the characteristics of the anatomical model; optionally also by amending the position and/or orientation of a plane/volume of interest of the anatomical model; optionally also by amending the characteristics of the MR simulation when executing the MRI simulator; obtaining produced MR i