Tomographic Reconstruction Using Global Statistical Priors

Preeti Gopal, Ritwick Chaudhry, Sharat Chandran, Imants Svalbe, Ajit Rajwade

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Recent research in tomographic reconstruction is motivated by the need to efficiently recover detailed anatomy from limited measurements. One of the ways to compensate for the increasingly sparse sets of measurements is to exploit the information from templates, i.e., prior data available in the form of already reconstructed, structurally similar images. Towards this, previous work has exploited using a set of global and patch based dictionary priors. In this paper, we propose a global prior to improve both the speed and quality of tomographic reconstruction within a Compressive Sensing framework. We choose a set of potential representative 2D images referred to as templates, to build an eigenspace; this is subsequently used to guide the iterative reconstruction of a similar slice from sparse acquisition data. Our experiments across a diverse range of datasets show that reconstruction using an appropriate global prior, apart from being faster, gives a much lower reconstruction error when compared to the state of the art.

Original languageEnglish
Title of host publicationDICTA 2017 - 2017 International Conference on Digital Image Computing
Subtitle of host publicationTechniques and Applications
EditorsYi Guo, Hongdong Li, Weidong (Tom) Cai, Manzur Murshed, Zhiyong Wang, Junbin Gao, David Dagan Feng
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781538628393
Publication statusPublished - 19 Dec 2017
EventDigital Image Computing Techniques and Applications 2017
- Novotel Sydney Manly Pacific , Sydney, Australia
Duration: 29 Nov 20171 Dec 2017
Conference number: 19th (Proceedings)


ConferenceDigital Image Computing Techniques and Applications 2017
Abbreviated titleDICTA 2017
Internet address


  • Compressive sensing
  • Filtered backprojection
  • K-SVD
  • Overcomplete dictionaries
  • Principal component analysis

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