Longitudinally Guided Super-Resolution of Neonatal Brain Magnetic Resonance Images

Yongqin Zhang, Feng Shi, Jian Cheng, Li Wang, Pew Thian Yap, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

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 quantitatively.

Original languageEnglish
Article number8252756
Pages (from-to)662-674
Number of pages13
JournalIEEE Transactions on Cybernetics
Volume49
Issue number2
DOIs
Publication statusPublished - 2019 Feb

Keywords

  • Guided bilateral filtering (GBF)
  • image interpolation
  • image super-resolution (SR)
  • magnetic resonance imaging (MRI)
  • total variation (TV)

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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