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: 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 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 method of designing a nanostructure, comprises: receiving a far field optical response and material properties; feeding the synthetic far field optical response and material properties to an artificial neural network having at least three hidden layers; and extracting from the artificial neural network a shape of a nanostructure corresponding to the far field optical response.

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.

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.