A system is provided for MRI coil sensitivity estimation and reconstruction At least two cascades of regularization networks are serially connected such that the output of a cascade is used as input of a following cascade, at least two deepsets coil sensitivity map networks are serially connected such that the output of a deepsets coil sensitivity map network is used as input of a following deepsets coil sensitivity map network (CR), and wherein the outputs of the deepsets coil sensitivity map networks are also used as inputs for the cascades.

According to one embodiment, a medical information processing apparatus has processing circuitry. The processing circuitry acquires medical data on a subject, acquires numerical data obtained by digitizing an acquisition condition of the medical data, and applies a machine learning model to input data including the numerical data and the medical data, thereby generating output data based on the medical data.

In a method for diffusion-weighted MR-imaging of an object, which undergoes a cyclic motion, a first sub-period type of the cyclic motion is predicted for a first acquisition timeframe, where the first sub-period type corresponds to one of two or more predefined characteristic types of sub-periods of the cyclic motion. A first amount of diffusion-weighting may be selected based on the first sub-period type. A first MR-acquisition may be carried out during the first acquisition timeframe, where a diffusion-weighting according to the first amount of diffusion-weighting is applied. An MR-image of the object is generated based on MR-data including a first MR-dataset obtained as a result of the first MR-acquisition.

In a method for determining at least one test position for a test measurement to be recorded by means of a magnetic resonance system, a test image is recorded, and at least one test position is selected based on the test image. With methods for the compensation of effects of deviations of gradients actually generated during a readout duration from gradients planned for this readout time duration, the selection of test positions according to the disclosure based on a test image advantageously ensures that the test positions lie in a recording region favorable for the test measurement, e.g. also within an examination object to be examined in the test image. A higher image quality in MR images, which were generated using test measurements carried out at test positions positioned according to the disclosure, can therefore be achieved.

Calibration data is generated for completing undersampled measurement data acquired via a magnetic resonance system. This includes recording N measurement data sets using an acquisition scheme, and undersampling the k-space with an acceleration factor R, with N being greater than or equal to R, and the N measurement data sets together scanning the k-space completely. Phase images are generated from the N recorded measurement data sets, at least one homogeneity value of the created phase images is determined, and a complete calibration data set is generated based upon the recorded measurement data sets, taking into account the at least one homogeneity value. Thus, it is possible to determine which measurement data sets are subject to undesired phase errors, the measurement data sets used for the creation of the calibration data sets can be selected optimally, and input of the detected phase errors into the calibration data sets can be avoided.

Systems and methods for performing ungated magnetic resonance imaging are disclosed herein. A method includes producing magnetic resonance image MRI data by scanning a target in a low magnetic field with a pulse sequence having a spiral trajectory; sampling k-space data from respective scans in the low magnetic field and receiving at least one field map data acquisition and a series of MRI data acquisitions from the respective scans; forming a field map and multiple sensitivity maps in image space from the field map data acquisition; forming target k-space data with the series of MRI data acquisitions; forming initial magnetic resonance images in the image domain by applying a Non-Uniform Fast Fourier Transform to the target k-space data; and forming reconstructed images with a low rank plus sparse (L+S) reconstruction algorithm applied to the initial magnetic resonance images.

A magnetic resonance imaging apparatus includes processing circuitry. The processing circuitry is configured to acquire a first coil sensitivity map and a second coil sensitivity map for a plurality of coils, the second coil sensitivity map being different in phase from the first coil sensitivity map; generate a first image based on the first coil sensitivity map and magnetic resonance data related to the plurality of coils; generate a second image based on the first coil sensitivity map, the second coil sensitivity map, and the first image; and generate a final image from the first image and the second image.

During operation, a computer system may acquire magnetic resonance (MR) signals associated with a sample from a measurement device or memory. Then, the computer system may access a predetermined set of coil magnetic field basis vectors associated with a surface surrounding the sample, where coil sensitivities of coils in the measurement device are represented by weighted superpositions of the predetermined set of coil magnetic field basis vectors using coefficients, and where the predetermined coil magnetic field basis vectors are solutions to Maxwell's equations. Next, the computer system may solve, on a voxel-by-voxel basis for voxels associated with the sample, a nonlinear optimization problem for MR information associated with the sample and the coefficients using: a forward model that uses the MR information as inputs and simulates response physics of the sample, the MR signals and the predetermined set of coil magnetic field basis vectors.

A computer system that performs a sparsity technique is described. During operation, the computer system accesses or obtains information associated with non-invasive measurements performed on at least an individual, historical non-invasive measurements, and a dictionary of predetermined features or basis functions associated with the historical non-invasive measurements. Note that the non-invasive measurements and the historical non-invasive measurements may include or correspond to magnetic resonance (MR) measurements. For example, the MR measurements may include magnetic resonance imaging (MRI) scans. Then, the computer system updates the dictionary of predetermined features based at least in part on the non-invasive measurements and the historical non-invasive measurements, where the updating includes performing a minimization technique with a cost function having an L2-norm term and an L0-norm term. Next, the computer system determines weights associated with features in the updated dictionary of predetermined features based at least in part on the non-invasive measurements.

In MRI, upon simultaneously generating computed images of multiple parameters, imaging time is efficiently reduced while preventing decrease in spatial resolution and SN ratio as much as possible. A plurality of original images is reconstructed from nuclear magnetic resonance signals acquired under various imaging conditions, and a computed image is obtained by calculation performed among the plurality of original images. The various imaging conditions include an imaging condition that a repetition time of an imaging sequence is different from one another, and upon imaging, the number of phase encoding steps is made smaller when the repetition time is long. An image is reconstructed in such a manner that a matrix size of the image obtained when the number of phase encoding steps is small is made equal to the matrix size of the image obtained when the number of phase encoding steps is large.