Abstract: A training method for training neural networks to determine a magnetic susceptibility distribution of a sample may include: storing a simulated magnetic susceptibility map of the sample, generating a modified magnetic susceptibility map by combining an influence of one or more external magnetic susceptibility sources with the simulated magnetic susceptibility map and storing the modified magnetic susceptibility maps. The method may include generating a first training image by applying a quantitative susceptibility mapping model the modified magnetic susceptibility map and storing the first training image, applying the first neural network to the first image and a second neural network to an output of the first neural network and changing network parameters of the first and the second neural network depending on a deviation of an output of the second artificial neural network from the simulated magnetic susceptibility map.

Abstract: A computer-implemented method for augmenting diffusion-weighted magnetic resonance imaging data, which contains, for each of a plurality of Q-space points, respective magnetic resonance datasets (MR-datasets) for a target voxel and for a plurality of auxiliary voxels in a predefined neighborhood of the target voxel. The method includes applying a trained artificial neural network to input data, which contains the respective MR-datasets of the target voxel and the plurality of auxiliary voxels for all of the plurality of Q-space points; computing an interpolated MR-dataset for the target voxel and for a target Q-space point, which is not contained in the plurality of Q-space points, by means of the artificial neural network depending on the input data.

Abstract: A training method for training neural networks to determine a magnetic susceptibility distribution of a sample may include: storing a simulated magnetic susceptibility map of the sample, generating a modified magnetic susceptibility map by combining an influence of one or more external magnetic susceptibility sources with the simulated magnetic susceptibility map and storing the modified magnetic susceptibility maps. The method may include generating a first training image by applying a quantitative susceptibility mapping model the modified magnetic susceptibility map and storing the first training image, applying the first neural network to the first image and a second neural network to an output of the first neural network and changing network parameters of the first and the second neural network depending on a deviation of an output of the second artificial neural network from the simulated magnetic susceptibility map.

Abstract: Techniques are disclosed to leverage the use of convolutional neural networks or similar machine learning algorithms to predict an underlying susceptibility distribution from MRI phase data, thereby solving the ill-posed inverse problem. These techniques include the use of Deep Quantitative Susceptibility “DeepQSM” mapping, which uses a large amount of simulated susceptibility distributions and computes phase distribution using a unique forward solution. These examples are then used to train a deep convolutional neuronal network to invert the ill-posed problem.

Abstract: In a magnetic resonance method and apparatus, deficiencies in conventional masking in quantitative susceptibility mapping (QSM) are addressed by the inclusion of an additional step in the conventional QSM post-processing pipeline. In this additional step, atlas-based segmentation techniques, which have been developed for morphological applications such as T1w MPRAGE are used in order to provide the mask. This mask is then fed to the remainder of the QSM post-processing pipeline.

Abstract: In a magnetic resonance method and apparatus, deficiencies in conventional masking in quantitative susceptibility mapping (QSM) are addressed by the inclusion of an additional step in the conventional QSM post-processing pipeline. In this additional step, atlas-based segmentation techniques, which have been developed for morphological applications such as T1w MPRAGE are used in order to provide the mask. This mask is then fed to the remainder of the QSM post-processing pipeline.

Abstract: An imaging system includes determination of a first range of values of an imaging parameter, determination of a cost function expressing a difference between a first pulse profile and a second pulse profile, the second pulse profile generated based on respective values of each of a set of pulse parameters, identification of first coefficient values of each function of a set of functions which substantially minimize the cost function over the first range of values of the imaging parameter, where each of the set of functions determines a value of a respective one of the set of pulse parameters based on a value of the imaging parameter, and storage of the first coefficient values of each function of the set of functions in association with the first range of values.

Abstract: Techniques are disclosed to leverage the use of convolutional neural networks or similar machine learning algorithms to predict an underlying susceptibility distribution from MRI phase data, thereby solving the ill-posed inverse problem. These techniques include the use of Deep Quantitative Susceptibility “DeepQSM” mapping, which uses a large amount of simulated susceptibility distributions and computes phase distribution using a unique forward solution. These examples are then used to train a deep convolutional neuronal network to invert the ill-posed problem.

Abstract: In a method and magnetic resonance (MR) apparatus for producing an MR image of a subject, MR signals are acquired respectively with multiple MR signal reception channels, and with an ultra-high basic magnetic field in the MR data acquisition scanner. Each of the acquired MR signals has a phase and exhibiting phase noise, and the acquired MR signals from the multiple MR signal reception channels are entered into a computer, as raw data in which said phase noise is preserved. The computer calculates the respective phase noise of each MR signal reception channel relative to the phase noise in each other MR signal reception channel, and calculates a phase noise map from the relative phase noise calculation for each of said reception channels, the phase noise map representing a spatial distribution of phase noise over the multiple MR signal reception channels.

Abstract: In a method and magnetic resonance (MR) apparatus for producing an MR image of a subject, MR signals are acquired respectively with multiple MR signal reception channels, and with an ultra-high basic magnetic field in the MR data acquisition scanner. Each of the acquired MR signals has a phase and exhibiting phase noise, and the acquired MR signals from the multiple MR signal reception channels are entered into a computer, as raw data in which said phase noise is preserved. The computer calculates the respective phase noise of each MR signal reception channel relative to the phase noise in each other MR signal reception channel, and calculates a phase noise map from the relative phase noise calculation for each of said reception channels, the phase noise map representing a spatial distribution of phase noise over the multiple MR signal reception channels.

Abstract: An imaging system includes determination of a first range of values of an imaging parameter, determination of a cost function expressing a difference between a first pulse profile and a second pulse profile, the second pulse profile generated based on respective values of each of a set of pulse parameters, identification of first coefficient values of each function of a set of functions which substantially minimize the cost function over the first range of values of the imaging parameter, where each of the set of functions determines a value of a respective one of the set of pulse parameters based on a value of the imaging parameter, and storage of the first coefficient values of each function of the set of functions in association with the first range of values.

Abstract: A simple method to denoise ratio images in magnetic resonance imaging, includes generating a MRI sequence provided for acquiring data from an object to be imaged, wherein the MRI sequence is configured for generating at least two different standard images, respectively a first standard image and a second standard image, acquiring the two different standard images, and combining the two different standard images in a ratio image. The ratio image is obtained by calculating a ratio of the first standard image and the second standard image that is tunable by a parameter ? wherein the parameter ? is automatically chosen for maximizing the negentropy of the ratio image.

Abstract: A simple method to denoise ratio images in magnetic resonance imaging, includes generating a MRI sequence provided for acquiring data from an object to be imaged, wherein the MRI sequence is configured for generating at least two different standard images, respectively a first standard image and a second standard image, acquiring the two different standard images, and combining the two different standard images in a ratio image. The ratio image is obtained by calculating a ratio of the first standard image and the second standard image that is tunable by a parameter ? wherein the parameter ? is automatically chosen for maximizing the negentropy of the ratio image.