Abstract: A method of generating biomarker parameters includes acquiring imaging data depicting a patient using a MRI system. The imaging data is acquired for a plurality of contrasts resulting from application of a pulse on the patient's anatomy. A process is executed to generate a MoCoAve image for each contrast. This process includes dividing the imaging data for the contrast into bins corresponding to one of a plurality of respiratory motion phases, and reconstructing the imaging data in each bin to yield bin images. The process further includes selecting a reference bin image from the bin images, and warping the bin images based on the reference bin image. The warped bin images and the reference bin image are averaged to generate the MoCoAve image for the contrast. One or more biomarker parameter maps are calculated based on the MoCoAve images generated for the contrasts.

Abstract: A method for acquiring magnetic resonance imaging data with respiratory motion compensation using one or more motion signals includes acquiring a plurality of gradient-delay-corrected radial readout views of a subject using a free-breathing multi-echo pulse sequence, and sampling a plurality of data points of the gradient-delay-corrected radial readout views to yield a self-gating signal. The self-gating signal is used to determine a plurality of respiratory motion states corresponding to the plurality of gradient-delay-corrected radial readout views. The respiratory motion states are used to correct respiratory motion bias in the gradient-delay-corrected radial readout views, thereby yielding gradient-delay-corrected and motion-compensated multi-echo data. One or more images are reconstructed using the gradient-delay-corrected and motion-compensated multi-echo data.

Abstract: The disclosure relates to a field camera and a method for measuring a magnetic field distribution using a magnetic resonance tomograph and the field camera. The field camera has a number of samples, which are distributed over a spatial volume to be measured, and a number of receive antennas. In an act of the method, a sensitivity matrix for the receive antennas, for each sample at each receive antenna, is captured using the magnetic resonance tomograph. In another act, antenna signals of the samples in a magnetic field to be measured are captured by the receive antennas, using the magnetic resonance tomograph. Finally, magnetic resonance signals of the individual samples are determined from the antenna signals as a function of the sensitivity matrix, using a controller. In a further act, the magnetic field strength at the location of the samples may be determined from the magnetic resonance signals.

Abstract: A method for acquiring magnetic resonance imaging data with respiratory motion compensation using one or more motion signals includes acquiring a plurality of gradient-delay-corrected radial readout views of a subject using a free-breathing multi-echo pulse sequence, and sampling a plurality of data points of the gradient-delay-corrected radial readout views to yield a self-gating signal. The self-gating signal is used to determine a plurality of respiratory motion states corresponding to the plurality of gradient-delay-corrected radial readout views. The respiratory motion states are used to correct respiratory motion bias in the gradient-delay-corrected radial readout views, thereby yielding gradient-delay-corrected and motion-compensated multi-echo data. One or more images are reconstructed using the gradient-delay-corrected and motion-compensated multi-echo data.

Abstract: A method of generating biomarker parameters includes acquiring imaging data depicting a patient using a MRI system. The imaging data is acquired for a plurality of contrasts resulting from application of a pulse on the patient's anatomy. A process is executed to generate a MoCoAve image for each contrast. This process includes dividing the imaging data for the contrast into bins corresponding to one of a plurality of respiratory motion phases, and reconstructing the imaging data in each bin to yield bin images. The process further includes selecting a reference bin image from the bin images, and warping the bin images based on the reference bin image. The warped bin images and the reference bin image are averaged to generate the MoCoAve image for the contrast. One or more biomarker parameter maps are calculated based on the MoCoAve images generated for the contrasts.

Abstract: The disclosure relates to techniques for automatically characterizing liver tissue of a patient, comprising receiving morphological magnetic resonance image data set and at least one magnetic resonance parameter map of an imaging region comprising at least partially the liver of the patient, each acquired by a magnetic resonance imaging device, via a first interface. The techniques further include applying a trained function comprising a neural network to input data comprising at least the image data set and the parameter map. At least one tissue score describing the liver tissue is generated as output data, which is provided using a second interface.

Abstract: The disclosure relates to a field camera and a method for measuring a magnetic field distribution using a magnetic resonance tomograph and the field camera. The field camera has a number of samples, which are distributed over a spatial volume to be measured, and a number of receive antennas. In an act of the method, a sensitivity matrix for the receive antennas, for each sample at each receive antenna, is captured using the magnetic resonance tomograph. In another act, antenna signals of the samples in a magnetic field to be measured are captured by the receive antennas, using the magnetic resonance tomograph. Finally, magnetic resonance signals of the individual samples are determined from the antenna signals as a function of the sensitivity matrix, using a controller. In a further act, the magnetic field strength at the location of the samples may be determined from the magnetic resonance signals.

Abstract: A method for acquiring magnetic resonance imaging data with respiratory motion compensation using one or more motion signals includes acquiring a plurality of gradient-delay-corrected radial readout views of a subject using a free-breathing multi-echo pulse sequence, and sampling a plurality of data points of the gradient-delay-corrected radial readout views to yield a self-gating signal. The self-gating signal is used to determine a plurality of respiratory motion states corresponding to the plurality of gradient-delay-corrected radial readout views. The respiratory motion states are used to correct respiratory motion bias in the gradient-delay-corrected radial readout views, thereby yielding gradient-delay-corrected and motion-compensated multi-echo data. One or more images are reconstructed using the gradient-delay-corrected and motion-compensated multi-echo data.

Abstract: A magnetic resonance imaging system and method are provided for improved determination of noise bias effects in calculating fitted parameters for quantitative MRI procedures. The system and method includes selecting a range for the SNR and fitted parameter values, and for each of a plurality of base pairs of these values and for a plurality of b values, adding a random noise term to the real and imaginary components of a plurality of corresponding signal terms, fitting magnitudes of the resulting “noisy” signals to determine a “noisy” fitted parameter value, and compare the “noisy” and base fitted parameter values to determine a noise-based error for each pair of base values. The noise-based errors can be used to generate an error map, modify imaging parameters to reduce such errors, or correct fitted parameters directly.

Abstract: A magnetic resonance imaging system and method are provided for improved determination of noise bias effects in calculating fitted parameters for quantitative MRI procedures. The system and method includes selecting a range for the SNR and fitted parameter values, and for each of a plurality of base pairs of these values and for a plurality of b values, adding a random noise term to the real and imaginary components of a plurality of corresponding signal terms, fitting magnitudes of the resulting “noisy” signals to determine a “noisy” fitted parameter value, and compare the “noisy” and base fitted parameter values to determine a noise-based error for each pair of base values. The noise-based errors can be used to generate an error map, modify imaging parameters to reduce such errors, or correct fitted parameters directly.

Abstract: In a method and apparatus for noise decorrelation of magnetic resonance (MR) measurement signals acquired by multiple detectors of an MR apparatus, which are disturbed by additive noise, noise signals and reference signals of the multiple detectors are used to determine an improved noise decorrelation matrix, which removes a noise correlation in the MR measurement signals of the multiple detectors.

Abstract: In a magnetic resonance (MR) apparatus and a method for operating such an apparatus, a T1 parameter map is generated with fat fraction correction, by using a model in which the fat fraction of acquired MR data is used as a known parameter. The T1 values from the acquired MR data are fat fraction-corrected in such a manner, so as to generate fat fraction-corrected entries for the T1 parameter map according to the model.

Abstract: In a method and magnetic resonance apparatus for determining a scanning region that is relevant to a magnetic resonance examination, overview data are acquired from an object positioned on a patient accommodation device during total imaging, the overview data are evaluated so as to generate position information therefrom relating to the object positioned on the patient accommodation device on the basis of the overview data, and the scanning region that is relevant to the magnetic resonance examination is determined on the basis of the position information.

Abstract: In a computer and a magnetic resonance method and apparatus for automatic characterization (classification) of liver tissue in a region of interest of a liver, at least one value tuple of the region of interest of the liver is acquired, the value tuple including at least one T1 value determined from magnetic resonance images of the region of interest, or a reciprocal value thereof, and a T2 or T2* value or a reciprocal value thereof. The value tuple is transferred into a multidimensional parameter space and the characterization of the liver tissue is then performed on the basis of the position of the value tuple in the parameter space.

Abstract: In a method and apparatus for the automatic assignment of at least one combination image of an examination object to a spin species represented in a combination magnetic resonance (MR) image, an information MR dataset is obtained and evaluated in a computer to determine information about the examination object from the captured information MR dataset. At least two MR datasets are acquired at one of at least two echo times in each case following an excitation by a multi-contrast measurement. At least one combination image is determined from the at least two MR datasets, and spin species represented in the at least one combination image are assigned on the basis of the information determined from the information MR dataset. By using additional information about the examination object determined by MR technology an automatic unambiguous global assignment of the correct spin species is enabled.

Abstract: Disclosed herein is a framework for identifying signal components in image data. In accordance with one aspect, the framework receives multiple measured signal values corresponding to respective quantified signal components in image data. The framework determines at least one first measure of fit map of a signal model based on the measured signal values. The measured signal values may be swapped to generate swapped signal values. At least one second measure of fit map of the signal model may be determined based on the swapped signal values. The multiple signal components may then be identified by comparing the first and second measure of fit maps.

Abstract: Noise information describing general noise properties of a magnetic resonance imaging apparatus (MRIA) in an imaging volume is measured using a predetermined first magnetic resonance sequence. Patient image data is acquired in at least one imaging region of a patient using a predetermined second magnetic resonance sequence. An anatomical landmark of a group of predetermined anatomical landmarks is localized in the patient image data by a landmark detection algorithm. For landmarks, reference information regarding imaging quality information is provided in a database. A landmark-specific signal-to-noise ratio is determined for each localized landmark from the patient image data at the landmark and the noise information, and the imaging quality information is determined from the landmark-specific signal-to-noise ratio.

Abstract: In a method and apparatus for determining time-dependent dephasing factors of at least one spectral component of at least two spectral components in a region of interest in an object under examination, measured data of the region of interest and acquired over time by a test measurement in a magnetic resonance scanner. The contribution of at least one of the at least two spectral components in the recorded measured data is determined. Dephasing factors of the at least one spectral component are determined on the basis of the contribution determined therefor in the recorded measured data over time. Dephasing factors determined in this way can be determined individually with relatively little effort and used in Dixon techniques.

Abstract: In a method and apparatus for noise decorrelation of magnetic resonance (MR) measurement signals acquired by multiple detectors of an MR apparatus, which are disturbed by additive noise, noise signals and reference signals of the multiple detectors are used to determine an improved noise decorrelation matrix, which removes a noise correlation in the MR measurement signals of the multiple detectors.

Abstract: In a method to correct noise effects in magnetic resonance (MR) images, which is executed in a processor (computer), the processor executes a fitting algorithm in order to calculate initial values for each of selected variables in signal model that models noise effects in a modeled, noise-containing MR image. The processor then iteratively executes the same or a different fitting algorithm, in order to generate final values for each of the selected variables. The processor is provided with an actual, acquired MR image that contains noise, and the processor uses the final values of the selected variables to calculate synthetic signal intensities in the MR image, thereby producing a synthetic MR image with no noise bias effects of errors. This synthetic image is made available in electronic form at an output of the processor, as a data file.