Abstract: A computer-implemented method of processing complex magnetic resonance (MR) images is provided. The method includes receiving a pair of corrupted complex data and pristine complex images. The method also includes training a neural network model using the pair by inputting the corrupted complex data to the neural network model, setting the pristine complex images as target outputs, and processing the corrupted complex data using the neural network model to derive output complex images of the corrupted complex data. Training a neural network model also includes comparing the output complex images with the target outputs by computing a phase-sensitive structural similarity index measure (PS-SSIM) between each of the output complex images and its corresponding target complex image, wherein the PS-SSIM is real-valued and varies with phases of the output complex image and phases of the target complex image, and adjusting the neural network model based on the comparison.

Abstract: K-space data obtained from a magnetic resonance imaging scan where motion was detected is split into two parts in accordance with the timing of the motion to produce first and second sets of k-space data corresponding to different poses. Sub-images are reconstructed from the k first and second sets of k-space data, which are used as inputs to a deep neural network which transforms them into a motion-corrected image.

Abstract: A magnetic resonance (MR) imaging method of detecting and scoring motion artifacts in MR images of an object is provided. The method includes computing a k-space difference map based at least in part on first MR signals of the object acquired with a first coil and second MR signals of the object acquired with a second coil. The method also includes generating a difference plot based on the k-space difference map, the difference plot including a curve. The method further includes calculating a motion score based on the curve in the difference plot, wherein the motion score indicates the level of motion artifacts in the image caused by motion of the object during acquisition of the first MR signals and the second MR signals, and the motion score includes an area under the curve. Moreover, the method includes outputting the motion score.

Abstract: A computer-implemented method of processing complex magnetic resonance (MR) images is provided. The method includes receiving a pair of corrupted complex data and pristine complex images. The method also includes training a neural network model using the pair by inputting the corrupted complex data to the neural network model, setting the pristine complex images as target outputs, and processing the corrupted complex data using the neural network model to derive output complex images of the corrupted complex data. Training a neural network model also includes comparing the output complex images with the target outputs by computing a phase-sensitive structural similarity index measure (PS-SSIM) between each of the output complex images and its corresponding target complex image, wherein the PS-SSIM is real-valued and varies with phases of the output complex image and phases of the target complex image, and adjusting the neural network model based on the comparison.

Abstract: The techniques discussed herein relate to a reduced acoustic noise and vibration magnetic resonance imaging (MRI) acquisition. In certain implementations acoustic noise levels for one or more MRI pulse sequences are characterized and modified by limiting the frequencies and amplitudes of the gradient waveforms so as to produce less noise and vibration when the modified waveform is used during an MRI examination. In this manner, relatively low sound pressure levels can be attained.

Abstract: The subject matter discussed herein relates to a fast magnetic resonance imaging (MRI) method to suppress fine-line artifact in Fast-Spin-Echo (FSE) images reconstructed with a deep-learning network. The network is trained using fully sampled NEX=2 (Number of Excitations equals to 2) data. In each case, the two excitations are combined to generate fully sampled ground-truth images with no fine-line artifact, which are used for comparison with the network generated image in the loss function. However, only one of the excitations is retrospectively undersampled and inputted into the network during training. In this way, the network learns to remove both undersampling and fine-line artifacts. At inferencing, only NEX=1 undersampled data are acquired and reconstructed.

Abstract: A magnetic resonance (MR) imaging method of detecting and scoring motion artifacts in MR images of an object is provided. The method includes computing a k-space difference map based at least in part on first MR signals of the object acquired with a first coil and second MR signals of the object acquired with a second coil. The method also includes generating a difference plot based on the k-space difference map, the difference plot including a curve. The method further includes calculating a motion score based on the curve in the difference plot, wherein the motion score indicates the level of motion artifacts in the image caused by motion of the object during acquisition of the first MR signals and the second MR signals, and the motion score includes an area under the curve. Moreover, the method includes outputting the motion score.

Abstract: A method is provided that includes acquiring coil data from a magnetic resonance imaging device. The coil data includes undersampled k-space data. The method includes processing the coil data using an image reconstruction technique to generate an initial undersampled image. The method includes generating a reconstructed image based on the coil data, the initial undersampled image, and multiple iterative blocks of a residual deep-learning image reconstruction network. A first iterative block of the residual deep-learning image reconstruction network receives the initial undersampled image. Each of the multiple iterative blocks includes a data-consistency unit that preserves the fidelity of the coil data in a respective output of a respective iterative block utilizing zeroed data consistency. The initial undersampled image is added to an output of the last iterative block via a residual connection.

Abstract: The techniques discussed herein relate to a reduced acoustic noise and vibration magnetic resonance imaging (MRI) acquisition. In certain implementations acoustic noise levels for one or more MRI pulse sequences are characterized and modified by limiting the frequencies and amplitudes of the gradient waveforms so as to produce less noise and vibration when the modified waveform is used during an MRI examination. In this manner, relatively low sound pressure levels can be attained.

Abstract: The subject matter discussed herein relates to a fast magnetic resonance imaging (MRI) method to suppress fine-line artifact in Fast-Spin-Echo (FSE) images reconstructed with a deep-learning network. The network is trained using fully sampled NEX=2 (Number of Excitations equals to 2) data. In each case, the two excitations are combined to generate fully sampled ground-truth images with no fine-line artifact, which are used for comparison with the network generated image in the loss function. However, only one of the excitations is retrospectively undersampled and inputted into the network during training. In this way, the network learns to remove both undersampling and fine-line artifacts. At inferencing, only NEX=1 undersampled data are acquired and reconstructed.

Abstract: A method of reconstructing imaging data into a reconstructed image may include training a generative adversarial network (GAN) to reconstruct the imaging data. The GAN may include a generator and a discriminator. Training the GAN may include determining a combined loss by adaptively adjusting an adversarial loss based at least in part on a difference between the adversarial loss and a pixel-wise loss. Additionally, the combined loss may be a combination of the adversarial loss and the pixel-wise loss. Training the GAN may also include updating the generator based at least in part on the combined loss. The method may also include receiving, into the generator, the imaging data and reconstructing, via the generator, the imaging data into a reconstructed image.

Abstract: A magnetic resonance imaging (MRI) system includes control and analysis circuitry having programming to acquire magnetic resonance (MR) data using coil elements of the MRI system, analyze the MR data, and reconstruct the MR data into MR sub-images. The system also includes a trained neural network associated with the control and analysis circuitry to transform the MR sub-images into a prediction relating to a presence and extent of motion corruption in the MR sub-images. The programming of the control and analysis circuitry includes instructions to control operations of the MRI system based at least in part on the prediction of the trained neural network.

Abstract: K-space data obtained from a magnetic resonance imaging scan where motion was detected is split into two parts in accordance with the timing of the motion to produce first and second sets of k-space data corresponding to different poses. Sub-images are reconstructed from the k first and second sets of k-space data, which are used as inputs to a deep neural network which transforms them into a motion-corrected image.

Abstract: A magnetic resonance imaging (MRI) system includes control and analysis circuitry having programming to acquire magnetic resonance (MR) data using coil elements of the MRI system, analyze the MR data, and reconstruct the MR data into MR sub-images. The system also includes a trained neural network associated with the control and analysis circuitry to transform the MR sub-images into a prediction relating to a presence and extent of motion corruption in the MR sub-images. The programming of the control and analysis circuitry includes instructions to control operations of the MRI system based at least in part on the prediction of the trained neural network.

Abstract: A system and method for detecting, timing, and adapting to patient motion during an MR scan includes using the inconsistencies between calculated images from different coil-array elements to detect the presence of patient motion and, together with the k-space scan-order information, determine the timing of the motion during the scan. Once the timing is known, various actions may be taken, including restarting the scan, reacquiring those portions of k-space acquired before the movement, or correcting for the motion using the existing data and reconstructing a motion-corrected image from the data.

Abstract: A system and method for detecting, timing, and adapting to patient motion during an MR scan includes using the inconsistencies between calculated images from different coil-array elements to detect the presence of patient motion and, together with the k-space scan-order information, determine the timing of the motion during the scan. Once the timing is known, various actions may be taken, including restarting the scan, reacquiring those portions of k-space acquired before the movement, or correcting for the motion using the existing data and reconstructing a motion-corrected image from the data.

Abstract: A method of reconstructing imaging data into a reconstructed image may include training a generative adversarial network (GAN) to reconstruct the imaging data. The GAN may include a generator and a discriminator. Training the GAN may include determining a combined loss by adaptively adjusting an adversarial loss based at least in part on a difference between the adversarial loss and a pixel-wise loss. Additionally, the combined loss may be a combination of the adversarial loss and the pixel-wise loss. Training the GAN may also include updating the generator based at least in part on the combined loss. The method may also include receiving, into the generator, the imaging data and reconstructing, via the generator, the imaging data into a reconstructed image.

Abstract: A method is provided that includes acquiring coil data from a magnetic resonance imaging device. The coil data includes undersampled k-space data. The method includes processing the coil data using an image reconstruction technique to generate an initial undersampled image. The method includes generating a reconstructed image based on the coil data, the initial undersampled image, and multiple iterative blocks of a residual deep-learning image reconstruction network. A first iterative block of the residual deep-learning image reconstruction network receives the initial undersampled image. Each of the multiple iterative blocks includes a data-consistency unit that preserves the fidelity of the coil data in a respective output of a respective iterative block utilizing zeroed data consistency. The initial undersampled image is added to an output of the last iterative block via a residual connection.

Abstract: A magnetic resonance (MR) imaging method performed by an MR imaging system includes acquiring MR data in multiple shots and multiple acquisitions (NEX), separately reconstructing the component magnitude and phase of images corresponding to the multiple shots and multiple NEX, removing the respective phase from each of the images, and combining, after removal of the respective phase, the shot images and the NEX images to produce a combined image. The method further includes using the combined image to calculate the full k-space data for each shot and NEX and replacing unacquired k-space data points with calculated k-space data points. The operations are repeated until the combined image reaches a convergence.

Abstract: A method for sparse image reconstruction includes acquiring coil data from a magnetic resonance imaging device. The coil data includes undersampled k-space data corresponding to a subject. The method further includes processing the coil data using an image reconstruction technique to generate an initial undersampled image. The method also includes generating a reconstructed image based on the coil data, the initial undersampled image, and a plurality of iterative blocks of a flared network. A first iterative block of the flared network receives the initial undersampled image. Each of the plurality of iterative blocks includes a data consistency unit and a regularization unit and the iterative blocks are connected both by direct connections from one iterative block to the following iterative block and by a plurality of dense skip connections to non-adjacent iterative blocks. The flared network is based on a neural network trained using previously acquired coil data.