Abstract: Systems and methods for reconstruction for a medical imaging system. An adapter is used to adapt scan data so that different quantities of repetitions or directions may be used to train and implement a single multichannel backbone network.

Abstract: For reconstruction in medical imaging, such as reconstruction in MR imaging, scanning is accelerated by under-sampling. In iterative reconstruction, the input to the regularizer is altered provide for correlation of non-local aliasing artifacts. Duplicates of the input image are shifted by different amounts based on the level of acceleration. The resulting shifted images are used to form the input to the regularizer. Providing an input based on shifts allows the regularization to suppress non-local as well as local aliasing artifacts.

Abstract: For MR image reconstruction, MR measurement data representing an imaged object is obtained and, for each iteration of at least two iterations, a prior MR image for the respective iteration is received, an optimized MR image is generated by optimizing a predefined first loss function, which depends on the MR measurement data and on the prior MR image, and an enhanced MR image is generated by applying a trained machine learning model, MLM, for image enhancement to the optimized MR image. The prior MR image of the respective iteration corresponds to the enhanced MR image of a preceding iteration, unless the respective iteration corresponds to an initial iteration of the at least two iterations, and the prior MR image of the initial iteration corresponds to a predefined initial image.

Abstract: Techniques are provided for image reconstruction in parallel MR imaging, in which a respective set of regularly undersampled MR measurement data in k-space representing an imaged object is received for each of a plurality of coil channels. For each pair of coil channels of the plurality of coil channels, a respective set of reconstruction weights for reconstructing MR data at k-space points, which are not measured according to the undersampling, from the MR measurement data, is received. For each of the plurality of coil channels, a respective coil sensitivity map is determined depending on the respective sets of reconstruction weights for the respective coil channel. A reconstructed MR image is generated based on the coil sensitivity maps.

Abstract: Techniques are provided for training a machine learning model (MLM) for image enhancement for use in a magnetic resonance (MR) image, in which a point spread function for undersampled MR data acquisition is received. A cropped point spread function is determined, which is given by the point spread function within a predefined spatial region. At least one training MR dataset corresponding to at least one coil channel is received, and a ground truth reconstructed MR image corresponding to the at least one training MR dataset is received. The MLM is trained in a supervised manner depending on the at least one training MR dataset, on the ground truth reconstructed MR image, and on a Fourier transform of the cropped point spread function.

Abstract: Techniques are provided for determining magnetic resonance images showing different contrasts in an examination. Magnetic resonance data for all magnetic resonance images are acquired using the same acquisition technique and the magnetic resonance images are reconstructed from their magnetic resonance data sets using at least one reconstruction algorithm. The reconstruction comprises at least one de-noising step. After acquisition of the magnetic resonance data, at least one noise strength measure is determined for the magnetic resonance data sets for each contrast, and de-noising strengths for the de-noising step are chosen individually for each contrast depending on the respective at least one noise strength measure.

Abstract: For reconstruction, a machine-learned model is adapted to allow for reconstruction based on the repetitions available in some scanning. The reconstruction for one or more subsets is performed during the scanning. The machine-learned model is trained to reconstruction separately or independently for each repetition or to use information from previous repetitions without requiring waiting for completion of scanning. The reconstructed image may be displayed much more rapidly after completion of the acquisition since the reconstruction begins during the reconstruction.

Abstract: Techniques are provided for determining a magnetic resonance imaging (MRI) image using multiple measurement data sets that form a propeller pattern. Partial MRI images are reconstructed for each measurement data set. The partial MRI images are then combined.

Abstract: In a method for ascertaining correction information for correcting a magnetic resonance imaging scan, respective first and second magnetic resonance data for at least one gradient direction are acquired, where the first magnetic resonance data is acquired while the magnetic field gradient is applied in the respective gradient direction, and the second magnetic resonance data is acquired while the magnetic field gradient is applied counter to the respective gradient direction. The method may further include determining a respective phase difference for reference points along a respective position space line in the position space that extends in the respective gradient direction based on the first and second magnetic resonance data, and providing the phase differences of at least one subgroup of the reference points as correction information or ascertaining the provided correction information based on the phase differences of at least the subgroup of the reference points.

Abstract: Systems and methods for a deep learning reconstruction network with computationally light and efficient CNN architecture and a training strategy tailored to image reconstruction of dynamic multi-coil GRASP MRI. The configuration of the size of the network used in training time may be adjusted, which allows for higher accelerations and different hardware constraints.

Abstract: In a method for generating combined image data based on first magnetic resonance (MR) data and second MR data, the first MR data and the second MR data are provided, the first MR data having been generated by a first actuation of a magnetic resonance device from an examination area of an examination object using a first sequence module, and the second MR data having been generated by a second actuation of the magnetic resonance device from the examination area of the examination object using the first sequence module, the first MR data and the second MR data are registered to one another to generate first registered MR data and second registered MR data; the first registered MR data and the second registered MR data are statistically combined to generate combined image data, and the combined image data is provided as an output in electronic form as a data file.

Abstract: A computer-implemented method for gradient delay time correction of MR data using an MR device, wherein the magnetic resonance data is recorded using a three-dimensional recording technique with linear recording trajectories which are oriented in different readout directions of a readout plane that is perpendicular to a partition direction. The method includes: in a calibration measurement using the magnetic resonance device, recording calibration data which describes readout-direction-dependent shifts, caused by delay effects, of measurement points in the k-space; determining correction data by evaluating the calibration data; correcting the MR data based on the correction data in order to compensate for the delay effects, wherein the calibration data, which covers a coverage region in partition direction is recorded in a resolved manner, and the correction data is determined and applied in a manner that is dependent on partition direction.

Abstract: In reconstruction, such as reconstruction in MR imaging, sub-sampled measurements from the scan are used in each iteration. By masking parts of the sub-sampled measurements (i.e., sub-sampling the acquired sub-sampled data) used in one or more iterations of reconstruction, banding is reduced or eliminated.

Abstract: For reconstruction in medical imaging, user control of a characteristic (e.g., noise level) of the reconstructed image is provided. A machine-learned model alters the reconstructed image to enhance or reduce the characteristic. The user selected level of characteristic is then provided by combining the reconstructed image with the altered image based on the input level of the characteristic. Personalized or more controllable impression for medical imaging reconstruction is provided without requiring different reconstructions.

Abstract: For reconstruction in medical imaging using phase correction, a machine learning model is trained for reconstruction of an image. The reconstruction may be for a sequence without repetitions or may be for a sequence with repetitions. Where repetitions are used, rather than using just a loss for that repetition in training, the loss based on an aggregation of images reconstructed from multiple repetitions may used to train the machine learning model. In either approach, a phase correction is applied in machine training. A phase map is extracted from output of the model in training or extracted from the ground truth of the training data. The phase correction, based on the phase map, is applied to the ground truth and/or the output of the model in training. The resulting machine-learned model may better reconstruct an image as a result of having been trained using phase correction.

Abstract: For reconstruction in medical imaging using phase correction, a machine learning model is trained for reconstruction of an image. The reconstruction may be for a sequence without repetitions or may be for a sequence with repetitions. Where repetitions are used, rather than using just a loss for that repetition in training, the loss based on an aggregation of images reconstructed from multiple repetitions may used to train the machine learning model. In either approach, a phase correction is applied in machine training. A phase map is extracted from output of the model in training or extracted from the ground truth of the training data. The phase correction, based on the phase map, is applied to the ground truth and/or the output of the model in training. The resulting machine-learned model may better reconstruct an image as a result of having been trained using phase correction.

Abstract: A method and device for generating MRI data with increased resolution is described. In the method, k-space data is sampled with a non-rectangular sampling pattern. A non-rectangular sampling region of a Cartesian k-space is sampled and a complementary region (KB) of the Cartesian k-space is not sampled. First MR image data is reconstructed based on the sampled k-space data. Second MR image data with an increased resolution compared to a resolution of the reconstructed first MR image data is generated by applying a supplementing method adapted to supplement the reconstructed first MR image data with image information which, transformed into the Fourier domain of the reconstructed first MR image data, is associated with the complementary region determined from the k-space-sampling.

Abstract: Systems and methods for reconstruction for a medical imaging system. Non-Cartesian k-space data is acquired using a dynamic MR sequence. A time compression network compresses the non-Cartesian data. The compressed data is used for reconstruction of an image. The time compression network is configured to reduce the (time and memory) complexity of the reconstruction process.

Abstract: For reconstruction in medical imaging, user control of a characteristic (e.g., noise level) of the reconstructed image is provided. A machine-learned model alters the reconstructed image to enhance or reduce the characteristic. The user selected level of characteristic is then provided by combining the reconstructed image with the altered image based on the input level of the characteristic. Personalized or more controllable impression for medical imaging reconstruction is provided without requiring different reconstructions.

Abstract: Techniques are provided for determining a magnetic resonance image data set of an acquisition region comprising two proton species having a different spin characteristic. In the magnetic resonance image data set, contributions of one of the proton species are emphasized, and the technique includes acquiring magnetic resonance signals from the acquisition region using an opposed-phase condition of the spins of the two proton species in a magnetic resonance imaging device, the magnetic resonance signals forming a raw data set, applying phase unwrapping of the background phase to the raw data set to determine an intermediate data set such that the intermediate data represents a difference between the contributions of one proton species and the contributions of the other proton species for each voxel, and determining the magnetic resonance image data set from the intermediate data set.