Unsupervised super-resolution: creating high-resolution medical images from low-resolution anisotropic examples

Jörg Sander; Bob D. de Vos; Ivana Išgum

doi:10.1117/12.2580412

15 February 2021 Unsupervised super-resolution: creating high-resolution medical images from low-resolution anisotropic examples

Jörg Sander, Bob D. de Vos, Ivana Išgum

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115960E (2021) https://doi.org/10.1117/12.2580412
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Although high resolution isotropic 3D medical images are desired in clinical practice, their acquisition is not always feasible. Instead, lower resolution images are upsampled to higher resolution using conventional interpolation methods. Sophisticated learning-based super-resolution approaches are frequently unavailable in clinical setting, because such methods require training with high-resolution isotropic examples. To address this issue, we propose a learning-based super-resolution approach that can be trained using solely anisotropic images, i.e. without high-resolution ground truth data. The method exploits the latent space, generated by autoencoders trained on anisotropic images, to increase spatial resolution in low-resolution images. The method was trained and evaluated using 100 publicly available cardiac cine MR scans from the Automated Cardiac Diagnosis Challenge (ACDC). The quantitative results show that the proposed method performs better than conventional interpolation methods. Furthermore, the qualitative results indicate that especially finer cardiac structures are synthesized with high quality. The method has the potential to be applied to other anatomies and modalities and can be easily applied to any 3D anisotropic medical image dataset.

Conference Presentation

Citation Download Citation

Jörg Sander, Bob D. de Vos, and Ivana Išgum "Unsupervised super-resolution: creating high-resolution medical images from low-resolution anisotropic examples", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115960E (15 February 2021); https://doi.org/10.1117/12.2580412

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available