Abstract: A system is provided for MRI coil sensitivity estimation and reconstruction At least two cascades of regularization networks are serially connected such that the output of a cascade is used as input of a following cascade, at least two deepsets coil sensitivity map networks are serially connected such that the output of a deepsets coil sensitivity map network is used as input of a following deepsets coil sensitivity map network (CR), and wherein the outputs of the deepsets coil sensitivity map networks are also used as inputs for the cascades.

Abstract: DCE MR images are obtained from a MR scanner and under a free-breathing protocol is provided. A neural network assigns a perfusion metric to DCE MR images. The neural network includes an input layer configured to receive at least one DCE MR image representative of a first contrast enhancement state and of a first respiratory motion state and at least one further DCE MR image representative of a second contrast enhancement state and of a second respiratory motion state. The neural network further includes an output layer configured to output at least one perfusion metric based on the at least one DCE MR image and the at least one further DCE MR image. The neural network with interconnections between the input layer and the output layer is trained by a plurality of datasets, each of the datasets having an instance of the at least one DCE MR image and of the at least one further DCE MR image for the input layer and the at least one perfusion metric for the output layer.

Abstract: A system and method for performing accelerated k-space shift correction calibration scans for non-Cartesian trajectories is provided. The method can include applying an MRI sequence, performing a calibration scan based on the MRI sequence using the non-Cartesian trajectory to acquire k-space shift data, wherein one or more partitions are skipped during the calibration scan, interpolating the skipped one or more partitions using the k-space shift data from adjacent partitions, and calibrating the MRI system using the k-space shift data and the interpolated k-space shift data. In some embodiments, an acceleration factor Acc can be defined and the calibration scan acquires k-space shift data for only one partition in every Acc partitions.

Abstract: An adaptive reconstruction of MR data, including acquired MR data of a core region having core segments and simulated MR data of a peripheral region. The method includes ascertaining a peripheral signal based on the MR data of the peripheral region, determining a scaling factor for each core segment by taking into account the peripheral signal and a mean signal intensity of the MR data for the respective core segment, scaling the MR data of the core region by taking into account the MR data of each core segment and that of the scaling factor corresponding to the respective core segment, generating filtered MR data by combining the scaled MR data of the core region with the MR data of the peripheral region, and reconstructing image data from the filtered MR data.

Abstract: The present disclosure is generally directed to systems and methods for generating de-noised MR images that are reconstructed from a hybridization of two separate image reconstruction pipelines, at least one of which includes the use of a neural network. Further, the amount of influence that the neural network reconstruction has on the hybrid reconstructed image is controlled via a regularization parameter that is selected based on an estimated noise level associated with the initial image acquisition, which can be calculated from pre-scan data.

Abstract: A system and method for performing accelerated k-space shift correction calibration scans for non-Cartesian trajectories is provided. The method can include applying an MRI sequence, performing a calibration scan based on the MRI sequence using the non-Cartesian trajectory to acquire k-space shift data, wherein one or more partitions are skipped during the calibration scan, interpolating the skipped one or more partitions using the k-space shift data from adjacent partitions, and calibrating the MRI system using the k-space shift data and the interpolated k-space shift data. In some embodiments, an acceleration factor Acc can be defined and the calibration scan acquires k-space shift data for only one partition in every Acc partitions.

Abstract: A method for using a multi-echo magnetic resonance imaging (MRI) simultaneously quantify T1 and fat fraction in an anatomical region of interest includes performing a radial single shot multi-echo acquisition of the anatomical region of interest. The radial single shot multi-echo acquisition comprises applying a preparation pulse to invert longitudinal magnetization of the anatomical region of interest, and acquiring a plurality of radial readouts at different echo times (TE). A magnetization recovery curve is continuously sampled using the plurality of radial readouts to yield a plurality of radial spokes. The radial spokes for each TE are ground together to generate under-sampled k-space data for each TE. The under-sampled k-space data is reconstructed into a plurality of multi-echo images corresponding to the different echo times. One or more fitting algorithms are applied to the multi-echo images to generate a water-only T1 map and a proton density fat fraction (PDFF) measurement.

Abstract: In a magnetic resonance (MR) method and apparatus for determining an MR image or an MR fat image of an examination subject, first and second MR signal datasets are provided to a computer, respectively obtained at first and second echo times. The computer defines a signal model and determines possible solution candidates for values of parameters of the signal model for each pixel of the two MR signal datasets so that the MR signals thereof are matched as well as possible. A correct solution is selected from the solution candidates, using a calculated phase map, based on predetermined assumptions regarding the calculated phase map. The MR water image or the MR fat image is determined using the correct solution.

Abstract: For radial sampling in magnetic resonance imaging (MRI), a rescaling factor is determined from k-space data for each coil. The rescale factor is inversely proportional to the streak energy in the k-space data. The k-space data from the coils is rescaled for reconstruction, such as weighting the k-space data by the rescale factor in a data consistency term of iterative reconstruction. The rescale factor is additionally or alternatively used to determine a correction field for correction of intensity bias applied to intensities in the image-object space after reconstruction. These approaches may result in a diagnostically useful bias-corrected image with reduced streak artifact while benefiting from the efficient computation (i.e., computer operates to reconstruct more quickly).

Abstract: A system is provided for MRI coil sensitivity estimation and reconstruction At least two cascades of regularization networks are serially connected such that the output of a cascade is used as input of a following cascade, at least two deepsets coil sensitivity map networks are serially connected such that the output of a deepsets coil sensitivity map network is used as input of a following deepsets coil sensitivity map network (CR), and wherein the outputs of the deepsets coil sensitivity map networks are also used as inputs for the cascades.

Abstract: A computer implemented method of processing a medical image is disclosed. The method includes receiving a medical image comprising a first plurality of pixels each having an initial pixel value. For each of the first plurality of pixels, a filtering operation is applied to the pixel to generate a filtered pixel value for the pixel based on the initial pixel values of pixels that surround the pixel in the medical image. For each of the first plurality of pixels, a comparison of the initial pixel value with the filtered pixel value is performed. The method comprises, for each of the first plurality of pixels, determining, based on the comparison, whether or not to categorize the pixel as an erroneous pixel; and for each of the first plurality of pixels for which it is determined to categorize the pixel as an erroneous pixel, categorizing the pixel as an erroneous pixel.

Abstract: In a method for determining the T1 time and also of at least one tissue proportion per voxel in a predetermined volume segment of an examination object with a magnetic resonance (MR) sequence: a radio frequency (RF) preparation pulse is radiated in; a readout module is repeatedly run after the RF preparation pulse to acquire MR data; and the T1 time and the at least one tissue proportion per voxel is determined as a function of the MR data. The readout module can include: an RF excitation pulse at a beginning of the readout module, a phase encoding gradient, and a number of readout gradients (3a-3g) for acquiring the MR data. During running of the readout module, the MR data may be acquired, at least at times, with more than two echoes.

Abstract: For reconstruction in medical imaging, such as reconstruction in MR imaging, an iterative, hierarchal network for regularization may decrease computational complexity. The machine-learned network of the regularizer is unrolled or made iterative. For each iteration, nested U-blocks form a hierarchy so that some of the down-sampling and up-sampling of some U-blocks begin and end with lower resolution data or features, reducing computational complexity.

Abstract: A method of generating biomarker parameters includes acquiring imaging data depicting a patient using a MRI system. The imaging data is acquired for a plurality of contrasts resulting from application of a pulse on the patient's anatomy. A process is executed to generate a MoCoAve image for each contrast. This process includes dividing the imaging data for the contrast into bins corresponding to one of a plurality of respiratory motion phases, and reconstructing the imaging data in each bin to yield bin images. The process further includes selecting a reference bin image from the bin images, and warping the bin images based on the reference bin image. The warped bin images and the reference bin image are averaged to generate the MoCoAve image for the contrast. One or more biomarker parameter maps are calculated based on the MoCoAve images generated for the contrasts.

Abstract: A method for acquiring magnetic resonance imaging data with respiratory motion compensation using one or more motion signals includes acquiring a plurality of gradient-delay-corrected radial readout views of a subject using a free-breathing multi-echo pulse sequence, and sampling a plurality of data points of the gradient-delay-corrected radial readout views to yield a self-gating signal. The self-gating signal is used to determine a plurality of respiratory motion states corresponding to the plurality of gradient-delay-corrected radial readout views. The respiratory motion states are used to correct respiratory motion bias in the gradient-delay-corrected radial readout views, thereby yielding gradient-delay-corrected and motion-compensated multi-echo data. One or more images are reconstructed using the gradient-delay-corrected and motion-compensated multi-echo data.

Abstract: For reconstruction in medical imaging, such as reconstruction in MR imaging, the number of iterations in deep learning-based reconstruction may be reduced by including a learnable extrapolation in one or more iterations. Regularization may be provided in fewer than all of the iterations of the reconstruction. The result of either approach alone or both together is better quality reconstruction and/or less computationally expensive reconstruction.

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: The disclosure relates to MRI reconstruction of a sequence of MRI images. The MRI images are associated with different contrasts. The MRI images are based on multiple MRI measurement datasets that are acquired at different time offsets with respect to at least one excitation pulse and/or with respect to at least one refocusing pulse.

Abstract: The disclosure relates to MRI reconstruction of multiple MRI measurement datasets acquired throughout a measurement time duration. Patient motion can occur during the measurement time duration. Warping operators, sometimes also referred to as motion field, are incorporated into an iterative optimization of the MRI reconstruction.

Abstract: Magnetic resonance imaging (MRI) image reconstruction using machine learning is described. A variational or unrolled deep neural network can be used in the context of an iterative optimization. In particular, a regularization operation can be based on a deep neural network. The deep neural network can take, as an input, an aliasing data structure being indicative of aliasing artifacts in one or prior images of the iterative optimization. The deep neural networks can be trained to suppress aliasing artifacts.