Abstract: A computer-implemented method for denoising image data includes a computer system receiving an input image comprising noisy image data and denoising the input image using a deep multi-scale network comprising a plurality of multi-scale networks sequentially connected. Each respective multi-scale network performs a denoising process which includes dividing the input image into a plurality of image patches and denoising those image patches over multiple levels of decomposition using a threshold-based denoising process. The threshold-based denoising process denoises each respective image patch using a threshold which is scaled according to an estimation of noise present in the respective image patch. The noising process further comprises the assembly of a denoised image by averaging over the image patches.

Abstract: A computer-implemented method of selecting a Magnetic Resonance Imaging (MRI) sampling strategy includes selecting a base variable-density sampling pattern and determining a scan time associated with the base variable-density sampling pattern. A modified variable-density sampling pattern is created by modifying one or more parameters of the base variable-density sampling pattern to maximize a sampled k-space area without increasing the scan time. Next, a scan is performed on an object of interest using the modified variable-density sampling pattern to obtain a sparse MRI dataset. Then a sparse reconstruction process is applied to the sparse MRI dataset to yield an image of the object of interest.

Abstract: A method for sparse iterative phase correction for Magnetic Resonance (MR) partial Fourier reconstruction includes acquiring a partial Fourier k-space dataset using an MR scanner and estimating, by a control computer, a coil sensitivity map associated with the MR scanner from fully sampled k-space center. The control computer extracts a symmetrically sampled k-space center dataset from the partial Fourier k-space dataset and determines a low-resolution image based on the symmetrically sampled k-space center dataset and the coil sensitivity map. The control computer also determines phase corresponding to the low-resolution image. An iterative reconstruction process may then be applied to generate an image based on the partial Fourier k-space dataset. This iterative reconstruction process applies a Fast Iterative Shrinkage Thresholding Algorithm (FISTA) with phase correction based on the phase corresponding to the low-resolution image.

Abstract: A method comprises acquiring navigation data during navigation scans at each of a plurality of points in time. A plurality of magnetic resonance imaging (MM) k-space data corresponding to an imaged object are acquired at the plurality of points in time using Cartesian sampling, the k-space data including at least two spatial dimensions, a time. The respective motion state for each of the k-space data are computed based on the navigation data. At least one image is reconstructed from the plurality MM k-space data using k-space data corresponding to at least two motion states and the same point in time to reconstruct the at least one image.

Abstract: A learning-based magnetic resonance fingerprinting (MRF) reconstruction method for reconstructing an MR image of a tissue space in an MR scan subject for a particular MR sequence is disclosed. The method involves using a machine-learning algorithm that has been trained to generate a set of tissue parameters from acquired MR signal evolution without using a dictionary or dictionary matching.

Abstract: Disclosed herein is a method obtaining a magnetic resonance image of an object, comprising obtaining a first time evolution signal from a magnetic resonance signal from the object; performing a search of a compressed dictionary of magnetic resonance fingerprints to select a magnetic resonance fingerprint representative of the first time evolution signal, wherein the selected magnetic resonance fingerprint is an exact or approximate nearest neighbor match to the first time evolution signal; obtaining a magnetic resonance parameter associated with the selected fingerprint; generating the magnetic resonance image of the object from the obtained magnetic resonance parameter; and performing a second search of the compressed dictionary using the magnetic resonance image.

Abstract: A method of generating Magnetic Resonance (MR) parameter maps includes creating one or more parameter maps, each respective parameter map comprising initial parameter values associated with one of a plurality of MR parameters. A dynamical update process is performed over a plurality of time points. The dynamical update process performed at each respective time point includes applying a randomized pulse sequence to subject using an MR scanner to acquire a k-space dataset. This randomized pulse sequence is configured to excite a distinct range of values associated with the plurality of MR parameters. The dynamical update process further includes applying a reconstruction process to the k-space dataset to generate an image and using a tracking process to update the one or more parameter maps based on the randomized pulse sequence and the image.

Abstract: A method for reconstructing magnetic resonance imaging data includes acquiring a measurement dataset using a magnetic resonance imaging device and determining an estimated image dataset based on the measurement dataset. An iterative reconstruction process is performed to refine the estimated image dataset. Each iteration of the iterative reconstruction process comprises: updating the measurement dataset and a sparse coefficient dataset based on the estimated image dataset and a plurality of belief propagation terms, incorporating a noise prior dataset into the measurement dataset, incorporating a sparsity prior dataset into the sparse coefficient dataset, updating the plurality of belief propagation terms based on the measurement dataset and the sparsity prior dataset, and updating the estimated image dataset based on the plurality of belief propagation terms. A reconstructed image and confidence map are generated using the estimated image dataset.

Abstract: A method of image reconstruction for a magnetic resonance imaging (MRI) system includes obtaining k-space scan data captured by the MRI system, the k-space scan data being representative of an undersampled region over time, iteratively reconstructing preliminary dynamic images for the undersampled region from the k-space scan data via optimization of a first instance of a minimization problem, the minimization problem including a regularization term weighted by a weighting parameter array, generating a motion determination indicative of an extent to which each location of the undersampled region exhibits motion over time based on the preliminary dynamic images, and iteratively reconstructing motion-compensated dynamic images for the region from the k-space scan data via optimization of a second instance of the minimization problem, the second instance having the weighting parameter array altered as a function of the motion determination.

Abstract: A method for performing a magnetic resonance image reconstruction with spatially varying coil compression includes using a non-Cartesian acquisition scheme to acquire a multi-coil k-space dataset fully sampled along a fully sampled direction and decoupling the multi-coil k-space dataset along the fully sampled direction to yield a plurality of uncompressed coil data matrices. The plurality of uncompressed coil data matrices are compressed to yield a plurality of virtual coil data matrices which are aligned along the fully sampled direction to yield a plurality of aligned virtual coil data matrices. The aligned virtual coil data matrices are coupled along the fully sampled direction to yield a compressed multi-coil k-space dataset. Intensity values in the plurality of aligned virtual coil data matrices are normalized based on the plurality of uncompressed coil data matrices and an image is reconstructed using the compressed multi-coil k-space dataset.

Abstract: A computer-implemented method for learning a tight frame includes acquiring undersampled k-space data over a time period using an interleaved process. An average of the undersampled k-space data is determined and a reference image is generated based on the average of the undersampled k-space data. Next, a tight frame operator is determined based on the reference image. Then, a reconstructed image data is generated from the undersampled k-space data via a sparse reconstruction which utilizes the tight frame operator.

Abstract: A method for reconstructing magnetic resonance imaging data includes acquiring a measurement dataset using a magnetic resonance imaging device and determining an estimated image dataset based on the measurement dataset. An iterative reconstruction process is performed to refine the estimated image dataset. Each iteration of the iterative reconstruction process comprises: updating the measurement dataset and a sparse coefficient dataset based on the estimated image dataset and a plurality of belief propagation terms, incorporating a noise prior dataset into the measurement dataset, incorporating a sparsity prior dataset into the sparse coefficient dataset, updating the plurality of belief propagation terms based on the measurement dataset and the sparsity prior dataset, and updating the estimated image dataset based on the plurality of belief propagation terms. A reconstructed image and confidence map are generated using the estimated image dataset.

Abstract: A computer-implemented method of detecting a foreground data in an image sequence using a dual sparse model framework includes creating an image matrix based on a continuous image sequence and initializing three matrices: a background matrix, a foreground matrix, and a coefficient matrix. Next, a subspace recovery process is performed over multiple iterations. This process includes updating the background matrix based on the image matrix and the foreground matrix; minimizing an L?1 norm of the coefficient matrix using a first linearized soft-thresholding process; and minimizing an L?1 norm of the foreground matrix using a second linearized soft-thresholding process. Then, background images and foreground images are generated based on the background and foreground matrices, respectively.

Abstract: A method of image reconstruction for a magnetic resonance imaging (MRI) system having a plurality of coils includes obtaining k-space scan data captured by the MRI system, the k-space scan data being representative of an undersampled region over time, determining a respective coil sensitivity profile for the region for each coil of the plurality of coils, and iteratively reconstructing dynamic images for the region from the k-space scan data via an optimization of a minimization problem. The minimization problem is based on the determined coil sensitivity profiles and redundant Haar wavelet transforms of the dynamic images.

Abstract: A computer-implemented method for reconstructing magnetic resonance images using a parallel computing platform comprising a host unit and a graphical processing device includes receiving a plurality of coil data sets from a magnetic resonance imaging system, each respective coil data set comprising scanner data and a coil sensitivity map associated with a distinct coil included in the magnetic resonance imaging system. An iterative compressed-sensing reconstruction process is applied to reconstruct an image based on the plurality of coil data sets.

Abstract: A method for acquiring a three-dimensional image volume using a magnetic resonance imaging device includes performing a multi-slice or multi-slab acquisition process to acquire a plurality of slices or three-dimensional slabs corresponding to an imaged object. Each respective slice or three-dimensional slab included in the plurality of slices or three-dimensional slabs comprises k-space data. An iterative compressed-sensing reconstruction process is applied to jointly reconstruct the plurality of three-dimensional slabs as a single consistent volume. The iterative compressed-sensing reconstruction process solves a cost function comprising a summation of individual data fidelity terms corresponding to the plurality of three-dimensional slabs.

Abstract: A computer-implemented method of enhancing images includes receiving one or more observed images, identifying wavelet bases, and determining a downsampling operator. A noise variance value is estimated and used to select a tuning parameter. A blurring kernel is estimated based on one or more system calibration parameter and used to determine a low-pass blurring filter operator. A cost function is created which generates one or more denoised super-resolution images based on the observed images and the plurality of wavelet bases. The cost function may include, for example, a sparsity inducing norm applied to the plurality of wavelet bases (with the tuning parameter applied to the sparsity inducing norm) and a constraint requiring the one or more denoised super-resolution images to be equal to a result of applying the low-pass blurring filter operator and the downsampling operator to the one or more denoised super-resolution images.

Abstract: A computer-implemented method for reconstruction of a magnetic resonance image includes acquiring a first incomplete k-space data set comprising a plurality of first k-space lines spaced according to an acceleration factor and one or more calibration lines. A parallel imaging reconstruction technique is applied to the first incomplete k-space data to determine a plurality of second k-space lines not included in the first incomplete k-space data set, thereby yielding a second incomplete k-space data set. Then, the parallel imaging reconstruction technique is applied to the second incomplete k-space data to determine a plurality of third k-space lines not included in the second incomplete k-space data, thereby yielding a complete k-space data set.

Abstract: A sensing method for detecting a sensing array is provided. The sensing array comprises at least one sensing row. The sensing method comprises the steps of: obtaining a sensing curve according to a plurality of sensing data signals extracting from the sensing row; determining whether a curve feature of the sensing curve matches one of a plurality of predetermined curve features; and obtaining a touch condition of the sensing row according to the determination result related to the sensing row. A sensing device is further provided. The present invention can reduce the impact of interference noise on detection of a sensing array, and enhance accuracy of detecting a touch condition of the sensing array.

Abstract: A computer-implemented method of selecting a Magnetic Resonance Imaging (MRI) sampling strategy includes selecting a base variable-density sampling pattern and determining a scan time associated with the base variable-density sampling pattern. A modified variable-density sampling pattern is created by modifying one or more parameters of the base variable-density sampling pattern to maximize a sampled k-space area without increasing the scan time. Next, a scan is performed on an object of interest using the modified variable-density sampling pattern to obtain a sparse MRI dataset. Then a sparse reconstruction process is applied to the sparse MRI dataset to yield an image of the object of interest.