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

Clinical Applications of Spectral CT

Dual-energy computed tomography (DECT) has evolved from a research tool to an established clinical imaging modality since its first commercial introduction in the mid-2000s. The possibility to characterize the composition of different human tissues and the quantification of certain materials like iodine, calcium, or fat have shown clinical benefit for various body regions. Virtual monoenergetic imaging (VMI) and multi-material decomposition (MMD) imaging (see Chap. 12) are the most popular and investigated applications of DECT that can be used to improve detection and conspicuity of disease as well as objective and subjective image quality. Furthermore, virtual non-contrast (VNC) imaging can reduce the radiation exposure to the patient by omitting the need for a conventional non-contrast CT scan. In this chapter we review clinically established applications of DECT for the main body regions from head to toe. Moreover, we highlight interesting experimental and preclinical research topics that may become clinically available in the future. Concluding this chapter, we discuss the potential pitfalls associated with DECT