Tissue-Specific Sparse Deconvolution for Low-Dose CT Perfusion View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2013

AUTHORS

Ruogu Fang , Tsuhan Chen , Pina C Sanelli

ABSTRACT

Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra usually occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we extend this line of work by introducing tissue-specific sparse deconvolution to preserve the subtle perfusion information in the low-contrast tissue classes by learning tissue-specific dictionaries for each tissue class, and restore the low-dose perfusion maps by joining the tissue segments reconstructed from the corresponding dictionaries. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method with the advantage of better differentiation between abnormal and normal tissue in these patients. More... »

PAGES

114-21

References to SciGraph publications

  • 2012. Sparsity-Based Deconvolution of Low-Dose Perfusion CT Using Learned Dictionaries in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2012
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-40811-3_15

    DOI

    http://dx.doi.org/10.1007/978-3-642-40811-3_15

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1016165719

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24505656


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