Image imputation in cardiac MRI and quality assessment

Missing data is common in medical image research. For instance, corrupted or unusable slices owing to the presence of artifacts such as respiratory or motion ghosting, aliasing, and signal loss in images significantly reduce image quality and diagnostic accuracy. Also, medical image acquisition time is often limited by cost and physical or patient care constraints, resulting in highly under-sampled images, which can be formulated as missing in-between slices. Such clinically acquired scans violate underlying assumptions of many downstream algorithms. Another important application lies in multi-modal/multi-contrast imaging, where different medical images contain complementary information for improving the diagnosis. However, a complete set of different images is often difficult to obtain. All of these can be considered as missing image data, which can lead to a reduced statistical power and potentially biased results, if not handled appropriately. Thanks to the recent advances in deep neural networks and generative adversarial networks (GANs), the problem of missing image imputation can be viewed as an image synthesis problem, and its performance has been remarkably improved. In this chapter, we present cardiac MR imaging as a use case and investigate a robust approach, namely Image Imputation Generative Adversarial Network (I2-GAN), and compare it with several traditional and state-of-the-art image imputation techniques in context of missing slices.