To provide a technique in which, in imaging using an EPI method, an occurrence of an artifact when phase correction is performed for each channel is avoided and the phase correction is accurately performed. A common phase correction value to be applied to data of all channels is calculated using pre-scan data of each channel. The common phase correction value is obtained by combining a difference phase obtained for each of the channels. The difference phase is obtained by complex integration, while an absolute value of each channel is maintained as it is. The combination is performed by complex average, and averaging processing according to a weight of the absolute value is performed. The occurrence of an artifact can be prevented by using the common phase correction value, and robust phase correction can be performed by including the weight of the absolute value.

The invention provides for a magnetic resonance imaging system (100) for acquiring magnetic resonance data (142) from a subject (118) within a measurement zone (108). The magnetic resonance imaging system (100) comprises: a processor (130) for controlling the magnetic resonance imaging system (100) and a memory (136) storing machine executable instructions (150, 152, 154), pulse sequence commands (140) and a dictionary (144). The pulse sequence commands (140) are configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of multiple steady state free precession (SSFP) states per repetition time. The pulse sequence commands (140) are further configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of the multiple steady state free precession (SSFP) states according to a magnetic resonance fingerprinting protocol. The dictionary (144) comprises a plurality of tissue parameter sets. Each tissue parameter set is assigned with signal evolution data pre-calculated for multiple SSFP states.

There are provided a parallel rapid imaging method and device based on a complex number conjugate symmetry of multi-channel coil data and nonlinear GRAPPA image reconstruction, and a medium. The imaging method includes: obtaining virtual conjugate coil data by expanding the actual multi-channel coil data; combining actual multi-channel coil data and virtual multi-channel coil data to obtain a linear data term and a nonlinear data term; calibrating weighting factors of the linear data term and the nonlinear data term by using combined low-frequency full-sampling data (margins of the low-frequency full-sampling data includes parts of high-frequency data); reconstructing data which is under-sampled in a high-frequency region according to the calibrated weighting factors; fusing the low-frequency full-sampling data and the reconstructed data for the high-frequency region.

A proposal is provided for setting scan parameters comprising at least one value range scan parameter and at least two state scan parameters of a scan sequence of a magnetic resonance protocol for a magnetic resonance examination. A user is supported in the selection of the state scan parameters to be set by a computing unit that checks whether the selection of state scan parameters to be set made by the user comprises a permissible combination of settings and/or states. If an impermissible combination of settings and/or states is present, the computing unit ascertains at least one proposal with a permissible combination of settings and/or states for the state scan parameters to be set.

The present patent disclosure relates to a method and a device 700 for determining a spatial distribution of at least one tissue parameter within a sample on a time domain magnetic resonance, TDMR, signal emitted from the sample after excitation of the sample according to an applied pulse sequence, a method of obtaining at least one time dependent parameter relating to a magnetic resonance, MR, signal emitted from a sample after excitation of the sample according to an applied spin echo pulse sequence, and a computer program product for performing the methods. A TDMR signal model is used to approximate the emitted time domain magnetic resonance signal. The model is factorized into one or more first matrix operators that have a non-linear dependence on the at least one tissue parameter and a remainder of the TDMR signal model.

Magnetic resonance imaging according to the present invention includes T1, T2, or diffusion mapping with improved image resolution. The improved image resolution is achieved by leveraging the delay in the image acquisition to remove the partial volume effect of fluid in and around the tissue being imaged.

In a method for determining a fat-reduced MR image, a first MR image is provided having, apart from the other tissue constituents, MR signals from only one of the two fat constituents, the first MR image is applied to a trained ANN, which was trained by first MR training data as the input data, the training data including, apart from the other tissue constituents, MR signals from only the one of the two fat constituents, and using second MR training data as a base knowledge, the second MR training data including, apart from the other tissue constituents, no MR signals from the two fat constituents; and an MR output image is determined from the trained ANN, to which the first MR image was applied, as a fat-reduced MR image, wherein the fat-reduced MR image includes, apart from the other tissue constituents, no MR signals from the two fat constituents.

Disclosed are a system, method, and computer program product for generating pruned tractograms of neural fiber bundles. The method includes receiving scan data produced by diffusion imaging of at least a portion of a brain from a magnetic-resonance imaging (MRI) device. The method also includes generating an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data. The method further includes generating a density map using a set of tracts from the initial tractogram, identifying each tract that passes through a segment of the density map more than once, and setting a contribution of said tract to a unique tract count of the segment equal to a threshold pruning value. The method further includes generating a pruned tractogram by identifying a segment having a unique tract count less than or equal to the threshold pruning value and excluding the segment from the pruned tractogram.

Techniques for compensating magnetic resonance imaging (MRI) data for artefacts caused by motion of a subject being imaged. The techniques include obtaining spatial frequency data obtained by using a magnetic resonance imaging (MRI) system to perform MRI on a patient, the spatial frequency data including first spatial frequency data and second spatial frequency data; determining a transformation using a first image obtained using the first spatial frequency data and a second image obtained using the second spatial frequency data; determining a residual spatial phase; correcting, using the transformation, second spatial frequency data and the residual spatial phase, to obtain corrected second spatial frequency data and a corrected residual spatial phase; and generating a magnetic resonance (MR) image using the corrected second spatial frequency data and the corrected residual spatial phase.

Systems and methods are provided for performing automated reconstruction of a dynamic MRI dataset that is acquired without a fixed temporal resolution. On one or more image quality metrics (IQMs) are obtained by processing a subset of the acquired dataset. In one example implementation, at each stage of an iterative process, one or more IQMs of the image subset is computed, and the parameters controlling the reconstruction and/or the strategy for data combination are adjusted to provide an improved or optimal image reconstruction. Once the IQM of the image subset satisfies acceptance criteria based on an estimate of the overall temporal fidelity of the reconstruction, the full reconstruction can be performed, and the estimate of the overall temporal fidelity can be reported based on the IQM at the final iteration.