Xia, Y, Zhang, L, Ravikumar, N et al. (5 more authors) (2021) Recovering from missing data in population imaging – Cardiac MR image imputation via conditional generative adversarial nets. Medical Image Analysis, 67. 101812. ISSN 1361-8415
Abstract
Accurate ventricular volume measurements are the primary indicators of normal/abnormal cardiac function and are dependent on the Cardiac Magnetic Resonance (CMR) volumes being complete. However, missing or unusable slices owing to the presence of image artefacts such as respiratory or motion ghosting, aliasing, ringing and signal loss in CMR sequences, significantly hinder accuracy of anatomical and functional cardiac quantification, and recovering from those is insufficiently addressed in population imaging. In this work, we propose a new robust approach, coined Image Imputation Generative Adversarial Network (I2-GAN), to learn key features of cardiac short axis (SAX) slices near missing information, and use them as conditional variables to infer missing slices in the query volumes. In I2-GAN, the slices are first mapped to latent vectors with position features through a regression net. The latent vector corresponding to the desired position is then projected onto the slice manifold, conditioned on intensity features through a generator net. The generator comprises residual blocks with normalisation layers that are modulated with auxiliary slice information, enabling propagation of fine details through the network. In addition, a multi-scale discriminator was implemented, along with a discriminator-based feature matching loss, to further enhance performance and encourage the synthesis of visually realistic slices. Experimental results show that our method achieves significant improvements over the state-of-the-art, in missing slice imputation for CMR, with an average SSIM of 0.872. Linear regression analysis yields good agreement between reference and imputed CMR images for all cardiac measurements, with correlation coefficients of 0.991 for left ventricular volume, 0.977 for left ventricular mass and 0.961 for right ventricular volume.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Published by Elsevier B.V. This is an author produced version of an article published in Medical Image Analysis. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Deep learning; Data imputation; Conditional generative adversarial net; Conditional batch normalisation; Multi-scale discriminator; Cardiac MRI |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Jul 2021 13:23 |
Last Modified: | 02 Oct 2021 00:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.media.2020.101812 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176404 |