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

A microbial perspective of human developmental biology

When most people think of human development, they tend to consider only human cells and organs. Yet there is another facet that involves human-associated microbial communities. A microbial perspective of human development provides opportunities to refine our definitions of healthy prenatal and postnatal growth and to develop innovative strategies for disease prevention and treatment. Given the dramatic changes in lifestyles and disease patterns that are occurring with globalization, we issue a call for the establishment of 'human microbial observatories' designed to examine microbial community development in birth cohorts representing populations with diverse anthropological characteristics, including those undergoing rapid change