43 results about "Multiparametric Magnetic Resonance Imaging" patented technology

The invention relates to a method of selecting a set of coil elements from a multitude of physical coil elements comprised in a coil array for performing a magnetic resonance imaging scan of a region of interest.

The invention concerns a method and magnetic resonance apparatus for acquisition and generation of a time-resolved image series of an anatomical organ with a quasi-periodical movement in a subject, k-space is sampled in segments with a number of partial data sets, with the sampling points of each partial data set corresponding to grid points of a Cartesian sampling grid of a k-space segment, and the Cartesian sampling grids of the k-space segments being rotated relative to one another. A series of sub-data sets is incompletely acquired for each partial data set using alternating sampling schemes. Each incomplete sub-data set is associated with one of the individual images. For at least some of the partial data sets, complete sub-data sets are reconstructed from the incomplete sub-data sets. For the reconstruction of the individual images, in each of the individual images, at least some of the complete sub-data sets that are associated with this individual image are used.

The invention relates to a method of selecting a set of coil elements from a multitude of physical coil elements comprised in a coil array for performing a magnetic resonance imaging scan of a region of interest.

A method and apparatus for automated prostate tumor detection and classification in multi-parametric magnetic resonance imaging (MRI) is disclosed. A multi-parametric MRI image set of a patient, including a plurality of different types of MRI images, is received. Simultaneous detection and classification of prostate tumors in the multi-parametric MRI image set of the patient are performed using a trained multi-channel image-to-image convolutional encoder-decoder that inputs multiple MRI images of the multi-parametric MRI image set of the patient and includes a plurality of output channels corresponding to a plurality of different tumor classes. For each output channel, the trained image-to image convolutional encoder-decoder generates a respective response map that provides detected locations of prostate tumors of the corresponding tumor class in the multi-parametric MRI image set of the patient.

The invention discloses a method and system for dynamic magnetic resonance imaging. A norm sparsity constraint is carried out on a spatial basis on the basis of a conventional PS (Partial Separability) method, so that image artifact and reconstruction noise are more effectively reduced, the image quality is greatly improved, and the reconstruction of a high temporal-spatial resolution dynamic magnetic resonance image is effectively realized. The method comprises the following steps: acquiring K-t spatial navigation data and dynamic image data, which are collected in a sampling mode based on a partial separability method; constructing a dynamic imaging reconstruction model containing a spatial basis function and a temporal basis function based on the partial separability method; acquiring the temporal basis function according to the navigation data; based on the dynamic imaging reconstruction model, estimating the spatial basis function by a least square method according to the temporal basis function and the dynamic image data; utilizing the temporal basis function and the dynamic image data to carry out interpolation recovery on the k spatial dynamic image data, and then carrying out Fourier transformation so as to acquire a reconstructed dynamic magnetic resonance image.

Successive magnetic resonance images are reconstructed from the respective sets of magnetic resonance signals of the dynamic series on the basis of the identified distribution of likelihood of changes and optionally the static reference image. The magnetic resonance signals are acquired by way of a receiver antennae system having a spatial sensitivity profile and in an undersampled fashion and the successive magnetic resonance imagesare reconstructed optionally also on the basis of the spatial sensitivity profile.

A method for pharmacokinetic analysis, including: receiving time-series medical image data of a patient introduced with a contrast agent; identifying a reference region in the medical image data; identifying a plurality of points of interest in the medical image data; measuring an intensity of voxels in the reference region; and for each point of interest, measure an intensity of voxels therein, use the measured reference region and point of interest intensities to obtain an expression relating the point of interest's voxel concentration to that of the reference region, wherein the expression is a five-parameter nonlinear model with no reference to an arterial input function; and obtain values for each of the five-parameters by solving the expression and use the obtained values to determine whether the point of interest is malignant.

The invention relates to a non-convex low-rank reconstruction method for rapid MR imaging. An MR image data reconstruction mathematic model based on low-rank prior information of non-local similar image blocks is established, and iterative solution is carried out on the model in a direction alternative iteration method; a non-convex p norm of the low-rank matrix of the non-local image model with the low-rank prior information is solved by deposition and iteration of Taylor first-order approximation and the singular value, a similar image block is obtained, and a reconstruction image is solved via iteration by increasing the auxiliary variable and separating the variable. The image prior information is used to combine the non-local similarity with the low-rank characteristic of the image block, the Fourier transform and the characteristic of the low-rank matrix are used to simplify the calculation process, the complexity of algorithm is reduced, the performance of the reconstructed MRI images by part of K space data is improved, the image can be reconstructed more accurately with less scanning and measurement, pseudo shadows of the images are reduced, and rapid MRI is realized.