Longitudinal guided super-resolution reconstruction of neonatal brain MR images

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Neonatal images have low spatial resolution and insufficient tissue contrast. Generally, interpolation methods are used to upsample neonatal images to a higher resolution for more effective image analysis. However, the resulting images are often blurry and are susceptible to partial volume effect. In this paper, we propose an algorithm that utilizes longitudinal prior information for effective super-resolution reconstruction of neonatal images. We use a non-local approach to learn the spatial relationships of brain structures in highresolution longitudinal images and apply this information to the superresolution reconstruction of the neonatal image. In other words, the recurring patterns throughout the longitudinal scans are leveraged for reconstructing the neonatal image with high resolution. To solve this otherwise ill-posed inverse problem, low-rank and total-variation regularizations are enforced. Experiments performed on both T1- and T2-weighted MR images of 28 neonates demonstrate that the proposed method is capable of recovering more structural details and outperforms methods such as nearest neighbor interpolation, spline-based interpolation, non-local means upsampling, and both low-rank and total variation based super-resolution.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages67-76
Number of pages10
Volume8682
ISBN (Print)9783319149042
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event3rd International Workshop on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, STIA 2014 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014 - Boston, United States
Duration: 2014 Sep 182014 Sep 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8682
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Workshop on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, STIA 2014 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014
CountryUnited States
CityBoston
Period14/9/1814/9/18

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Shi, F., Cheng, J., Wang, L., Yap, P. T., & Shen, D. (2015). Longitudinal guided super-resolution reconstruction of neonatal brain MR images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8682, pp. 67-76). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8682). Springer Verlag. https://doi.org/10.1007/978-3-319-14905-9_6