Abstract: A method is provided that includes receiving biosignal data measured from a user, encoding the biosignal data into a vector, and generating, using a generative model, an image based on the vector. The generated image is provided for display.

Abstract: A method for magnetic resonance imaging corrects non-stationary off-resonance image artifacts. A magnetic resonance imaging (MRI) apparatus performs an imaging acquisition using non-Cartesian trajectories and processes the imaging acquisitions to produce a final image. The processing includes reconstructing a complex-valued image and using a convolutional neural network (CNN) to correct for non-stationary off-resonance artifacts in the image. The CNN is preferably a residual network with multiple residual layers.

Abstract: The present application relates to apparatus, systems, and methods to perform subject-aware self-supervised learning of a machine-learning model for classification of data, such as classification of biosignals.

Abstract: A method for providing magnetic resonance imaging with dynamic contrast and 4D flow of a volume of an object in a magnetic resonance imaging (MRI) system is provided. Contrast agent is provided to the volume of the object. Magnetic resonance excitation from the MRI system is applied to the volume of the object. The MRI system reads out a subsample of less than 10% of spatially resolved data and velocity encoded data with respect to time. The readout subsample is used to determine both dynamic contrast and 4D flow.

Abstract: A method for magnetic resonance imaging performs unsupervised training of a deep neural network of an MRI apparatus using a training set of under-sampled MRI scans, where each scan comprises slices of under-sampled, unclassified k-space MRI measurements. The MRI apparatus performs an under-sampled scan to produce under-sampled k-space data, updates the deep neural network with the under-sampled scan, and processes the under-sampled k-space data by the updated deep neural network of the MRI apparatus to reconstruct a final MRI image.

Abstract: A method for magnetic resonance imaging performs unsupervised training of a deep neural network of an MRI apparatus using a training set of under-sampled MRI scans, where each scan comprises slices of under-sampled, unclassified k-space MRI measurements. The MRI apparatus performs an under-sampled scan to produce under-sampled k-space data, updates the deep neural network with the under-sampled scan, and processes the under-sampled k-space data by the updated deep neural network of the MRI apparatus to reconstruct a final MRI image.

Abstract: An MRI apparatus performs multi-channel calibration acquisitions using a multi-channel receiver array and uses a convolutional neural network (CNN) to compute an estimated profile map that characterizes properties of the multi-channel receiver array. The profile map is composed of orthogonal vectors and transforms single-channel image space data to multi-channel image space data. The MRI apparatus performs a prospectively subsampled imaging acquisition and processes the resulting k-space data using the estimated profile map to reconstruct a final image. The CNN may be pretrained in an unsupervised manner using subsampled simulated multi-channel calibration acquisitions and using a regularization function included in a training loss function.

Abstract: A method for performing wave-encoded magnetic resonance imaging of an object is provided. The method includes applying one or more wave-encoded magnetic gradients to the object, and acquiring MR signals from the object. The method further includes calibrating a wave point-spread function, and reconstructing an image from the MR signals based at least in part on the calibrated wave point-spread function. Calibration of the wave point-spread function is based at least in part on one or more intermediate images generated from the MR signals.

Abstract: A method for magnetic resonance imaging corrects non-stationary off-resonance image artifacts. A magnetic resonance imaging (MRI) apparatus performs an imaging acquisition using non-Cartesian trajectories and processes the imaging acquisitions to produce a final image. The processing includes reconstructing a complex-valued image and using a convolutional neural network (CNN) to correct for non-stationary off-resonance artifacts in the image. The CNN is preferably a residual network with multiple residual layers.

Abstract: A method for magnetic resonance imaging (MRI) scans a field of view and acquires sub-sampled multi-channel k-space data U. An imaging model A is estimated. Sub-sampled multi-channel k-space data U is divided into sub-sampled k-space patches, each of which is processed using a deep convolutional neural network (ConvNet) to produce corresponding fully-sampled k-space patches, which are assembled to form fully-sampled k-space data V, which is transformed to image space using the imaging model adjoint Aadj to produce an image domain MRI image. The processing of each k-space patch ui preferably includes applying the k-space patch ui as input to the ConvNet to infer an image space bandpass-filtered image yi, where the ConvNet comprises repeated de-noising blocks and data-consistency blocks; and estimating the fully-sampled k-space patch vi from the image space bandpass-filtered image yi using the imaging model A and a mask matrix.

Abstract: A method for magnetic resonance imaging (MRI) scans a field of view and acquires sub-sampled multi-channel k-space data U. An imaging model A is estimated. Sub-sampled multi-channel k-space data U is divided into sub-sampled k-space patches, each of which is processed using a deep convolutional neural network (ConvNet) to produce corresponding fully-sampled k-space patches, which are assembled to form fully-sampled k-space data V, which is transformed to image space using the imaging model adjoint Aadj to produce an image domain MRI image. The processing of each k-space patch ui preferably includes applying the k-space patch ui as input to the ConvNet to infer an image space bandpass-filtered image yi, where the ConvNet comprises repeated de-noising blocks and data-consistency blocks; and estimating the fully-sampled k-space patch vi from the image space bandpass-filtered image yi using the imaging model A and a mask matrix.

Abstract: A method for an object in a magnetic resonance image (MRI) system for providing at least one velocity indicative magnetic resonance image (MRI) with motion correction of the object is provided. Velocity encoding gradients in at least one spatial direction are provided from the MRI system. Spatial frequency data resulting from the encoding gradients are acquired through the MRI system. Image signals are provided by the MRI system. Image data resulting from the image signals are acquired through the MRI system. At least one motion corrected and velocity indicative magnetic resonance image is created from the acquired spatial frequency data and image data.

Abstract: A method for performing wave-encoded magnetic resonance imaging of an object is provided. The method includes applying one or more wave-encoded magnetic gradients to the object, and acquiring MR signals from the object. The method further includes calibrating a wave point-spread function, and reconstructing an image from the MR signals based at least in part on the calibrated wave point-spread function. Calibration of the wave point-spread function is based at least in part on one or more intermediate images generated from the MR signals.

Abstract: A method for providing at least one motion corrected magnetic resonance imaging (MRI) image of an object in an MRI system with an array of a plurality of receiving coils is provided. At least one motion navigator signal of the object is provided. Individual navigator data are collected from each of the plurality of receiving coils. Motion estimates are generated for each of the plurality of receiving coils from the collected individual navigator data. A subset of the plurality of coils is found that detects a dominant motion by clustering the generated motion estimates. Only motion estimates from coils in the found subset are used to create a determined motion estimate. At least one MRI image is reconstructed using the determined motion estimate.

Abstract: A method for providing an magnetic resonance imaging (MRI) with nonrigid motion correction of an object is provided. An MRI excitation is applied to the object. A magnetic field read out from the object using a plurality of sensor coils. Spatially localized motion estimates are obtained for each sensor coil of the plurality of sensor coils. The motion estimates are used for each sensor coil to provide motion correction.

Abstract: A method for providing magnetic resonance imaging with dynamic contrast and 4D flow of a volume of an object in a magnetic resonance imaging (MRI) system is provided. Contrast agent is provided to the volume of the object. Magnetic resonance excitation from the MRI system is applied to the volume of the object. The MRI system reads out a subsample of less than 10% of spatially resolved data and velocity encoded data with respect to time. The readout subsample is used to determine both dynamic contrast and 4D flow.

Abstract: A method of providing dynamic magnetic resonance imaging (MRI) of an object in an MRI system is provided. A magnetic resonance excitation from the MRI system is applied to the object. A magnetic resonance signal is read out through k-space for a plurality of regions with two or three spatial dimensions and a temporal dimension, wherein the read out is pseudo-randomly undersampled in the spatial frequency dimensions and the temporal dimension providing k-space data that is pseudo-randomly undersampled in the spatial frequency dimensions and the temporal dimension. The readout data is used to create a sequential series of spatial frequency data sets by generating interpolated data in the spatial frequency dimensions and the temporal dimension. The sequential series of spatial frequency data sets is used to create temporally resolved spatial images.

Abstract: A method for an object in a magnetic resonance image (MRI) system for providing at least one velocity indicative magnetic resonance image (MRI) with motion correction of the object is provided. Velocity encoding gradients in at least one spatial direction are provided from the MRI system. Spatial frequency data resulting from the encoding gradients are acquired through the MRI system. Image signals are provided by the MRI system. Image data resulting from the image signals are acquired through the MRI system. At least one motion corrected and velocity indicative magnetic resonance image is created from the acquired spatial frequency data and image data.

Abstract: A method for providing at least one motion corrected magnetic resonance imaging (MRI) image of an object in an MRI system with an array of a plurality of receiving coils is provided. At least one motion navigator signal of the object is provided. Individual navigator data are collected from each of the plurality of receiving coils. Motion estimates are generated for each of the plurality of receiving coils from the collected individual navigator data. A subset of the plurality of coils is found that detects a dominant motion by clustering the generated motion estimates. Only motion estimates from coils in the found subset are used to create a determined motion estimate. At least one MRI image is reconstructed using the determined motion estimate.

Abstract: A method of providing dynamic magnetic resonance imaging (MRI) of an object in an MRI system is provided. A magnetic resonance excitation from the MRI system is applied to the object. A magnetic resonance signal is read out through k-space for a plurality of regions with two or three spatial dimensions and a temporal dimension, wherein the read out is pseudo-randomly undersampled in the spatial frequency dimensions and the temporal dimension providing k-space data that is pseudo-randomly undersampled in the spatial frequency dimensions and the temporal dimension. The readout data is used to create a sequential series of spatial frequency data sets by generating interpolated data in the spatial frequency dimensions and the temporal dimension. The sequential series of spatial frequency data sets is used to create temporally resolved spatial images.