Abstract: A computer-implemented method for reconstructing MRI images of a body includes the steps of: a) acquiring a set MRI signal samples (sq) at different k-space location along a k-space trajectory; b) acquiring or estimating a B0 inhomogeneity map ?B0(r), wherein B0 is a static magnetic field for polarizing nuclear spins of the body and r represents position in image space; c) initializing an image (f); d) updating the image by a performing a data-consistency operation in k-space, wherein conversion between image space and k-space is performed through a non-uniform discrete pseudo-Fourier transform operator taking into account the B0 inhomogeneity map obtained from step b) to correct for off-resonance effects; and e) further updating the image by using an image-correction neural network. A computer program product and a magnetic resonance imaging apparatus for carrying out such a method is also provided.

Abstract: A method of performing magnetic resonance imaging of a body using a magnetic resonance imaging scanner the method includes applying to the body a time-varying magnetic field gradient (Gx, Gy, Gz) defining a continuous trajectory (ST) in k-space complying with a set of constraints including constraints on maximum amplitude and maximum slew rate of the time-varying magnetic field gradient, such that sampling points (KS) belonging to the trajectory define a pseudo-random sampling of the k-space, approximating a predetermined target sampling density, the trajectory in k-space minimizing, subject to the set of constraints, a cost function defined by the difference between a first term, called attraction term, promoting consistency of the distribution of sampling points in k-space with the predetermined target sampling density, and a second term, called repulsion term, promoting separation in k-space between sampling points, the repulsion term being expressed as a sum of contributions corresponding to respective pa

Abstract: A magnetic resonance (MR) image may be created from MR data by receiving the MR data, applying a transform to the MR data, where a result of the applying is an image space representation of the MR data, determining a wrapped phase map of the image space representation of the MR data, obtaining an unwrapped phase map based on the wrapped phase map, scaling the unwrapped phase map into a B0 field map, reconstructing the MR image based on the MR data, correcting the MR image based on the B0 field map, and outputting the MR image. The scaling may be free of accounting for effects on the MR data by artifact sources secondary to B0 field inhomogeneities.

Abstract: A method of performing magnetic resonance imaging of a body includes a) immerging the body in a static and substantially uniform magnetic field; b) exciting nuclear spins inside the body using at least one radio-frequency pulse; c) applying to the body a time-varying magnetic field gradient defining at least one trajectory (ST) in k-space and simultaneously acquiring samples of a magnetic resonance signal so as to perform a pseudo-random sampling (KS) of the k-space; and d) applying a sparsity-promoting nonlinear reconstruction algorithm for reconstructing a magnetic resonance image of the body; wherein, at least in a low-spatial frequency region of the k-space, the distance between any two adjacent points belonging to a same trajectory is lower than 1/FOV, FOV being the size of a field of view of the reconstructed image. A magnetic resonance imaging apparatus for carrying out such a method is also provided.

Abstract: A method of performing magnetic resonance imaging of a body using a magnetic resonance imaging scanner the method includes applying to the body a time-varying magnetic field gradient (Gx, Gy, Gz) defining a continuous trajectory (ST) in k-space complying with a set of constraints including constraints on maximum amplitude and maximum slew rate of the time-varying magnetic field gradient, such that sampling points (KS) belonging to the trajectory define a pseudo-random sampling of the k-space, approximating a predetermined target sampling density, the trajectory in k-space minimizing, subject to the set of constraints, a cost function defined by the difference between a first term, called attraction term, promoting consistency of the distribution of sampling points in k-space with the predetermined target sampling density, and a second term, called repulsion term, promoting separation in k-space between sampling points, the repulsion term being expressed as a sum of contributions corresponding to respective pa

Abstract: A magnetic resonance (MR) image may be created from MR data by receiving the MR data, applying a transform to the MR data, where a result of the applying is an image space representation of the MR data, determining a wrapped phase map of the image space representation of the MR data, obtaining an unwrapped phase map based on the wrapped phase map, scaling the unwrapped phase map into a B0 field map, reconstructing the MR image based on the MR data, correcting the MR image based on the B0 field map, and outputting the MR image. The scaling may be free of accounting for effects on the MR data by artifact sources secondary to B0 field inhomogeneities.

Abstract: A method of performing magnetic resonance imaging of a body includes a) immerging the body in a static and substantially uniform magnetic field; b) exciting nuclear spins inside the body using at least one radio-frequency pulse; c) applying to the body a time-varying magnetic field gradient defining at least one trajectory (ST) in k-space and simultaneously acquiring samples of a magnetic resonance signal so as to perform a pseudo-random sampling (KS) of the k-space; and d) applying a sparsity-promoting nonlinear reconstruction algorithm for reconstructing a magnetic resonance image of the body; wherein, at least in a low-spatial frequency region of the k-space, the distance between any two adjacent points belonging to a same trajectory is lower than 1/FOV, FOV being the size of a field of view of the reconstructed image. A magnetic resonance imaging apparatus for carrying out such a method is also provided.

Abstract: A method of parallel magnetic resonance imaging of a body, comprising:—acquiring a set of elementary magnetic resonance images of said body from respective receiving antennas having known or estimated sensibility maps and noise covariance matrices, said elementary images being under-sampled in k-space; and performing regularized reconstruction of a magnetic resonance image of said body; wherein said step of performing regularized reconstruction of a magnetic resonance image is unsupervised and carried out in a discrete frame space. A method of performing dynamical and parallel magnetic resonance imaging of a body, comprising:—acquiring a set of time series of elementary magnetic resonance images of said body from respective receiving antennas having known or estimated sensibility maps and noise covariance matrices, said elementary images being under-sampled in k-space; and performing regularized reconstruction of a time series of magnetic resonance images of said body.

Abstract: A method of parallel magnetic resonance imaging of a body, comprising:—acquiring a set of elementary magnetic resonance images of said body from respective receiving antennas having known or estimated sensibility maps and noise covariance matrices, said elementary images being under-sampled in k-space; and performing regularized reconstruction of a magnetic resonance image of said body; wherein said step of performing regularized reconstruction of a magnetic resonance image is unsupervised and carried out in a discrete frame space. A method of performing dynamical and parallel magnetic resonance imaging of a body, comprising:—acquiring a set of time series of elementary magnetic resonance images of said body from respective receiving antennas having known or estimated sensibility maps and noise covariance matrices, said elementary images being under-sampled in k-space; and performing regularized reconstruction of a time series of magnetic resonance images of said body.