Harnessing group-sparsity regularization for resolution enhancement of lung 4D-CT

Arnav Bhavsar, Guorong Wu, Dinggang Shen

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

    Abstract

    A critical concern with lung 4D-CT is the low superior-inferior resolution, due to the consideration of radiation dose. We propose a resolution enhancement approach that reconstructs missing intermediate slices by exploiting the idea that information lost in one respiratory phase can be found in others, according to the complimentary nature of inter-phase information. Our approach is based on a patch-based framework that explores the role of group-sparsity involving groups of similar neighbouring patches. We discuss the regularizing role of group-sparsity, which helps in reducing the effect of noise and enables better enhancement of anatomical structures. Our results positively demonstrate the potential of group-sparsity for 4D-CT resolution enhancement.

    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
    Pages139-146
    Number of pages8
    EditionPART 3
    DOIs
    Publication statusPublished - 2013
    Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
    Duration: 2013 Sept 222013 Sept 26

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 3
    Volume8151 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
    Country/TerritoryJapan
    CityNagoya
    Period13/9/2213/9/26

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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