Conditional generative adversarial network for 3D rigid-body motion correction in MRI

© 2019 International Society for Magnetic Resonance in Medicine Purpose: Subject motion in MRI remains an unsolved problem; motion during image acquisition may cause blurring and artifacts that severely degrade image quality. In this work, we approach motion correction as an image-to-image translation problem, which refers to the approach of training a deep neural network to predict an image in 1 domain from an image in another domain. Specifically, the purpose of this work was to develop and train a conditional generative adversarial network to predict artifact-free brain images from motion-corrupted data. Methods: An open source MRI data set comprising T2*-weighted, FLASH magnitude, and phase brain images for 53 patients was used to generate complex image data for motion simulation. To simulate rigid motion, rotations and translations were applied to the image data based on randomly generated motion profiles. A conditional generative adversarial network, comprising a generator and discriminator networks, was trained using the motion-corrupted and corresponding ground truth (original) images as training pairs. Results: The images predicted by the conditional generative adversarial network have improved image quality compared to the motion-corrupted images. The mean absolute error between the motion-corrupted and ground-truth images of the test set was 16.4% of the image mean value, whereas the mean absolute error between the conditional generative adversarial network-predicted and ground-truth images was 10.8% The network output also demonstrated improved peak SNR and structural similarity index for all test-set images. Conclusion: The images predicted by the conditional generative adversarial network have quantitatively and qualitatively improved image quality compared to the motion-corrupted images

SPARKLING: variable-density k-space filling curves for accelerated T 2 * -weighted MRI

International audienceFunding information Purpose: To present a new optimization-driven design of optimal k-space trajectories in the context of compressed sensing: Spreading Projection Algorithm for Rapid K-space sampLING (SPARKLING). Theory: The SPARKLING algorithm is a versatile method inspired from stippling techniques that automatically generates optimized sampling patterns compatible with MR hardware constraints on maximum gradient amplitude and slew rate. These non-Cartesian sampling curves are designed to comply with key criteria for optimal sampling: a controlled distribution of samples (e.g., variable density) and a locally uniform k-space coverage. Methods: Ex vivo and in vivo prospective T * 2-weighted acquisitions were performed on a 7 Tesla scanner using the SPARKLING tra-jectories for various setups and target densities. Our method was compared to radial and variable-density spiral trajectories for high resolution imaging. Results: Combining sampling efficiency with compressed sensing, the proposed sampling patterns allowed up to 20-fold reductions in MR scan time (compared to fully-sampled Cartesian acquisitions) for two-dimensional T * 2-weighted imaging without deterioration of image quality, as demonstrated by our experimental results at 7 Tesla on in vivo human brains for a high in-plane resolution of 390 µm. In comparison to existing non-Cartesian sampling strategies, the proposed technique also yielded superior image quality. Conclusion: The proposed optimization-driven design of k-space trajectories is a versatile framework that is able to enhance MR sampling performance in the context of compressed sensing

Towards a mechanistic understanding of the human subcortex

The human subcortex is a densely populated part of the brain, of which only 7% of the individual structures are depicted in standard MRI atlases. In vivo MRI of the subcortex is challenging owing to its anatomical complexity and its deep location in the brain. The technical advances that are needed to reliably uncover this 'terra incognita' call for an interdisciplinary human neuroanatomical approach. We discuss the emerging methods that could be used in such an approach and the incorporation of the data that are generated from these methods into model-based cognitive neuroscience frameworks