Authors
Yongqin Zhang, Feng Shi, Jian Cheng, Li Wang, Pew-Thian Yap, Dinggang Shen
Publication date
2018/1/9
Journal
IEEE transactions on cybernetics
Volume
49
Issue
2
Pages
662-674
Publisher
IEEE
Description
Neonatal magnetic resonance (MR) images typically have low spatial resolution and insufficient tissue contrast. Interpolation methods are commonly used to upsample the images for the subsequent analysis. However, the resulting images are often blurry and susceptible to partial volume effects. In this paper, we propose a novel longitudinally guided super-resolution (SR) algorithm for neonatal images. This is motivated by the fact that anatomical structures evolve slowly and smoothly as the brain develops after birth. We propose a strategy involving longitudinal regularization, similar to bilateral filtering, in combination with low-rank and total variation constraints to solve the ill-posed inverse problem associated with image SR. Experimental results on neonatal MR images demonstrate that the proposed algorithm recovers clear structural details and outperforms state-of-the-art methods both qualitatively and …
Total citations
20182019202020212022202320243788632
Scholar articles
Y Zhang, F Shi, J Cheng, L Wang, PT Yap, D Shen - IEEE transactions on cybernetics, 2018