123 results about "Coil sensitivity" patented technology

A method of correcting for motion in magnetic resonance images of an object detected by a plurality of signal receiver coils comprising the steps of acquiring a plurality of image signals with the plurality of receiver coils, determining motion between sequential image signals relative to a reference, applying rotation and translation to image signals to align image signals with the reference, determining altered coil sensitivities due to object movement during image signal acquisition, and employing parallel imaging reconstruction of the rotated and translated image signals using the altered coil sensitivities in order to compensate for undersampling in k-space.

The present invention includes a method for parallel magnetic resonance imaging termed Superresolution Sensitivity Encoding (SURE-SENSE) and its application to functional and spectroscopic magnetic resonance imaging. SURE-SENSE acceleration is performed by acquiring only the central region of k-space instead of increasing the sampling distance over the complete k-space matrix and reconstruction is explicitly based on intra-voxel coil sensitivity variation. SURE-SENSE image reconstruction is formulated as a superresolution imaging problem where a collection of low resolution images acquired with multiple receiver coils are combined into a single image with higher spatial resolution using coil sensitivity maps acquired with high spatial resolution. The effective acceleration of conventional gradient encoding is given by the gain in spatial resolution Since SURE-SENSE is an ill-posed inverse problem, Tikhonov regularization is employed to control noise amplification. Unlike standard SENSE, SURE-SENSE allows acceleration along all encoding directions.

A method for accelerating data acquisition in MRI with N-dimensional spatial encoding has a first method step in which a transverse magnetization within an imaged object volume is prepared having a non-linear phase distribution. Primary spatial encoding is thereby effected through application of switched magnetic fields. Two or more RF receivers are used to simultaneously record MR signals originating from the imaged object volume, wherein, for each RF receiver, an N-dimensional data matrix is recorded which is undersampled by a factor R_i per selected k-space direction. Data points belonging to a k-space matrix which were not recoded by a selected acquisition schema are reconstructed using a parallel imaging method, wherein reference information concerning receiver coil sensitivities is extracted from a phase-scrambled reconstruction of the undersampled data matrix. The method generates a high-resolution image free of artifacts in a time-efficient manner by improving data sampling efficiency and thereby reducing overall data acquisition time.

Methods and systems in a parallel magnetic resonance imaging (MRI) system utilize sensitivity-encoded MRI data acquired from multiple receiver coils together with spatially dependent receiver coil sensitivities to generate MRI images. The acquired MRI data forms a reduced MRI data set that is undersampled in at least a phase-encoding direction in a frequency domain. The acquired MRI data and auto-calibration signal data are used to determine reconstruction coefficients for each receiver coil using a weighted or a robust least squares method. The reconstruction coefficients vary spatially with respect to at least the spatial coordinate that is orthogonal to the undersampled, phase-encoding direction(s) (e.g., a frequency encoding direction). Values for unacquired MRI data are determined by linearly combining the reconstruction coefficients with the acquired MRI data within neighborhoods in the frequency domain that depend on imaging geometry, coil sensitivity characteristics, and the undersampling factor of the acquired MRI data. An MRI image is determined from the reconstructed unacquired data and the acquired MRI data.

A system and method for mapping the sensitivity of MR coils includes a neural network or other computer intelligence trained from sample MR data to determine coil sensitivity profiles or sensitivity normalizations. Once the network is trained, subsequent coil mapping determinations may include fewer mapping acquisitions per coil. The resulting sensitivity map can be used in compensating for B1 inhomogeneities, parallel imaging reconstruction, generating tailored excitation currents for each individual coil, RF shimming, or other processes.

The subject invention pertains to a method and apparatus for producing sensitivity maps with respect to medical imaging. The subject invention relates to a method for applying an inpainting model to correct images in parallel imaging. Some images, such as coil sensitivity maps and intensity correction maps, have no signal at some places and may have noise. Advantageously, the subject invention allows an accurate method to fill in holes in sensitivity maps, where holes can arise when, for example, the pixel intensity magnitudes for two images being used to create the sensitivity map are zero. A specific embodiment of the subject invention can accomplish de-noise, interpolation, and extrapolation simultaneously for these types of maps such that the local texture can be carefully protected.

A system and method for MR imaging with coil sensitivity or calibration data acquisition for reducing wrapping or aliasing artifacts is disclosed. Low resolution MR data representative of coil sensitivity of a coil arrangement within an FOV is acquired prior to application of an imaging data acquisition scan and is used to reduce wrapping or aliasing artifacts in a reconstructed image.

A sensitivity distribution is estimated for a multicoil used in multicoil fast imaging. Initial sensitivity maps M1 to M3 are produced respectively from images C1 to C3 acquired from the plurality of RF coils in the multicoil. By fitting TPS (thin-plate splines) to the initial sensitivity maps M1 to M3 used as target data, sensitivity maps M1′ to M3′ for unfolding are estimated. In fitting the TPS, functions are activated, such as automatic arrangement of control points, addition of target points to the outside of an image, use of a known model, and fitting to at least either absolute value components of MR data or phase components of the MR data. Thus, even if only coarse echo data is acquired from the region to be imaged, a sensitivity map of each element coil of the multicoil is estimated with high precision.

In a method for magnetic resonance imaging on the basis of a partially-parallel acquisition (PPA) reconstruction method and a magnetic resonance tomography apparatus, data for at least two two-dimensional slices of a patient are acquired (which two-dimensional slices are displaced in the direction of a slice-selection gradient (z-gradients) defining the slice-normal direction) with at least one data acquisition in k-space (which acquisition forms a partial data set) per slice with a number of component coils, with the sum of all partial data sets forming a complete data set in k-space. The coil sensitivities of each component coil are determined on the basis of the complete data set. At least one partial data set of each slice is completed with a PPA reconstruction method on the basis of the determined coil sensitivities. The completed slices in k-space are transformed into whole images in the spatial domain.

The invention discloses a magnetic resonance reconstruction method based on deep learning and convex set projection, and relates to the technical field of magnetic resonance. The method comprises thesteps that S1, a network is constructed according to overlapping structures of multiple convolutional neural network modules and multiple convex set projection layers and shared data, wherein the shared data includes acquired K-space data and coil sensitivity information, and the convex set projection layers are obtained on the basis of the shared data; S2, after the network is constructed, all network parameters are trained through a reverse propagation process and verified; S3, structure and operation characteristics of the network are determined according to the verified network parameters,known test set data is input, the forward propagation of the network is conducted, unknown mapping data is obtained, and magnetic resonance reconstruction is completed. The method solves the problemthat by means of a current magnetic resonance reconstruction technology based on deep learning, only single-channel magnetic resonance data can be supported, and multi-channel magnetic resonance datacannot be processed.

The invention discloses a diffusion magnetic resonance imaging and reconstruction method. The method includes the steps of S1, using multiple channel coils and adopting a multi-excitation diffusion imaging mode to carry out signal acquisition on tested targets to obtain k spatial data; S2, calculating a coil sensitivity figure and carrying out iterative initialization; S3, conducting iterative reconstruction on needed diffusion images on the basis of the POCS algorithm according to the collected k spatial data, the coil sensitivity figure obtained through calculation and initialization parameters. According to the method, not only is acquisition efficiency of signals improved, but also fuzzy artifacts and motion artifacts of the images are reduced, and image resolution is improved.

A method for calibrating coil sensitivity profiles is described. The method includes generating reference sensitivity maps for each coil, imaging a subject, interleaving, with the imaging of the subject, imaging of at least one fiducial mark provided with each coil, and deriving, based on the coil positioning and coil loading, actual sensitivity maps from the reference sensitivity maps.

Various methods and systems are provided for reconstructing magnetic resonance images from accelerated magnetic resonance imaging (MRI) data. In one embodiment, a method for reconstructing a magnetic resonance (MR) image includes: estimating multiple sets of coil sensitivity maps from undersampled k-space data, the undersampled k-space data acquired by a multi-coil radio frequency (RF) receiver array; reconstructing multiple initial images using the undersampled k-space data and the estimated multiple sets of coil sensitivity maps; iteratively reconstructing, with a trained deep neural network, multiple images by using the initial images and the multiple sets of coil sensitivity maps to generate multiple final images, each of the multiple images corresponding to a different set of the multiple sets of sensitivity maps; and combining the multiple final images output from the trained deep neural network to generate the MR image.

SENSitivity Encoding (SENSE) has demonstrated potential for significant scan time reduction using multiple receiver channels. SENSE reconstruction algorithms for non-uniformly sampled data proposed to date require relatively high computational demands. A Projection Onto Convex Sets (POCS)-based SENSE reconstruction method (POCSENSE) has been recently proposed as an efficient reconstruction technique in rectilinear sampling schemes. POCSENSE is an iterative algorithm with a few constraints imposed on the acquired data sets at each iteration. Although POCSENSE can be readily performed on rectilinearly acquired k-space data, it is difficult to apply to non-uniformly acquired k-space data. Iterative Next Neighbor re-Gridding (INNG) algorithm is a recently proposed new reconstruction method for non-uniformly sampled k-space data. The POCSENSE algorithm can be extended to non-rectilinear sampling schemes by using the INNG algorithm. The resulting algorithm (POCSENSINNG) is an efficient SENSE reconstruction algorithm for non-uniformly sampled k-space data, taking into account coil sensitivities.

The invention discloses a synthetic method of a magnetic resonance multi-channel image. The method includes the following steps: (1) obtaining multi-channel K space data; (2) comparing the maximum value of a module of K space data of each channel to obtain CHmin which is the number of a channel where the minimum maximum value is located; (3) conducting inverse Fourier transform on the K space data of each channel to obtain an original image of the channel; (4) enabling the image of each channel to an index phase of the image of the CHmin channel to obtain images with a phase portion only comprising coil sensitivity information; (5) conducting lowpass filtering processing on all images; (6) conducting principal component analysis on smoothened image data to obtain optimal matching filter vector; (7) conducting weighting and summation of the original image of each channel by using a matching filter to obtain a final synthetic image. The synthetic method can guarantee that the synthetic image is close to optimal signal-to-noise ratio, enables signal strength of the image to have unbiasedness, and can eliminate signal loss caused by phase oscillation.

Coil sensitivity of a receive coil to a gradient null location is measured and, from the measurements, a coil calibration value is determined and used to modify the MR data acquired with that receive coil to reduce the adverse effects of gradient nulling on MR images. Coil sensitivity values are determined for each coil of a coil array and the data for each coil is respectively weighted. An image that is substantially free of gradient null artifacts or ghosting is then reconstructed from the weighted data.

In a method and magnetic resonance imaging apparatus wherein magnetic resonance signals are simultaneously received from an examination subject by multiple reception coils, a single, uninterrupted pulse sequence is executed which includes reference scans of the subject with a first sequence kernel that is optimized for coil sensitivity calibration, immediately followed by a series of accelerated image scans with a second sequence kernel, different from the first sequence kernel, that is optimized for imaging. Coil sensitivity maps for the respective coils are calculated from the data acquired in the reference scans, and an image of the subject is reconstructed by operating on the image data with a parallel reconstruction algorithm employing the calculated coil sensitivity maps.

Based on a theory that a reconstructed image in magnetic resonance is modulated by a phased array coil unit sensitivity, the invention provides a regularization correction method of a magnetic resonance phase array coil image uniformity. According to the method of the invention, a general coil with the uniform sensitivity in the superconductive magnetic resonance and each phased array imaging coil channel are used to carry out pre-scan so as to obtain the coil sensitivity. Through introducing a regular function, noise of the reconstructed image can be inhibited. Simultaneously, in the superconductive resonance, the general-coil pre-scan image is taken as a reference image in a regularization function so that the good uniformity can be obtained. A calculating speed is very fast. The method has little influence on image reconstruction time.