Abstract: For material characterization, first and second measured MRI data representing an object and corresponding first and second sequence descriptions may be received. For each iteration, first and second simulated MRI data may be generated according to the sequence descriptions based on a model including model values for at least one material parameter. An error may be determined, which depends on a deviation of the simulated MRI data from the measured MRI data. The model may be adapted based on the error. For an initial iteration, the model corresponds to an initial model and otherwise it corresponds to the adapted model of the preceding iteration. The quantitative material characterization may be determined based on the adapted model of a final iteration of the two or more iterations.

Abstract: Techniques are described for acquiring measurement data of an object under examination positioned in a magnetic resonance system using magnetic resonance with magnetization preparation. The techniques include performing a magnetization preparation block, applying at least two further preparation RF pulses, after the end of the magnetization preparation block, performing an acquisition block which comprises at least one further RF pulse and during which at least two echo signals are each acquired as measurement data, wherein at least one of the at least two echo signals is generated by the at least two further preparation RF pulses and the further RF pulse, and storing and/or further processing the acquired measurement data.

Abstract: A method of processing magnetic resonance data of a sample under investigation includes the steps of provision of the MR data being collected with an MRI scanner apparatus, and machine learning based data analysis of the MR data by supplying the MR data to an artificial neural network being trained with predetermined training data, wherein at least one image parameter of the sample and additionally at least one uncertainty quantification measure representing a prediction error of the at least one image parameter are provided by output elements of the neural network. Furthermore, a magnetic resonance imaging (MRI) scanner apparatus being adapted for employing the method of processing MR data is described.

Abstract: A method of processing magnetic resonance data of a sample under investigation includes the steps of provision of the MR data being collected with an MRI scanner apparatus, and machine learning based data analysis of the MR data by supplying the MR data to an artificial neural network being trained with predetermined training data, wherein at least one image parameter of the sample and additionally at least one uncertainty quantification measure representing a prediction error of the at least one image parameter are provided by output elements of the neural network. Furthermore, a magnetic resonance imaging (MRI) scanner apparatus being adapted for employing the method of processing MR data is described.

Abstract: A method of processing magnetic resonance (MR) data of a sample under investigation, includes the steps of providing the MR data being collected with an MRI scanner apparatus, and subjecting the MR data to a multi-parameter nonlinear regression procedure being based on a non-linear MR model and employing a set of input parameters, wherein the regression procedure results in creating a parameter map of model parameters of the sample, wherein the input parameters (initial values and possibly boundaries) of the regression procedure are estimated by a machine learning based estimation procedure applied to the MR data. The machine learning based estimation procedure preferably includes at least one of at least one neural network and a support vector machine. Furthermore, an MRI scanner apparatus is described.

Abstract: A method of processing magnetic resonance (MR) data of a sample under investigation, includes the steps of providing the MR data being collected with an MRI scanner apparatus, and subjecting the MR data to a multi-parameter nonlinear regression procedure being based on a non-linear MR model and employing a set of input parameters, wherein the regression procedure results in creating a parameter map of model parameters of the sample, wherein the input parameters (initial values and possibly boundaries) of the regression procedure are estimated by a machine learning based estimation procedure applied to the MR data. The machine learning based estimation procedure preferably includes at least one of at least one neural network and a support vector machine. Furthermore, an MRI scanner apparatus is described.