Sparsity-Based Deconvolution of Low-Dose Perfusion CT Using Learned Dictionaries View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Ruogu Fang , Tsuhan Chen , Pina C. Sanelli

ABSTRACT

Computational tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, such as stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a novel sparsity-base deconvolution method to estimate cerebral blood flow in CTP performed at low-dose. We first built an overcomplete dictionary from high-dose perfusion maps and then performed deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on a clinical dataset of ischemic patients. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain. More... »

PAGES

272-80

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-33415-3_34

DOI

http://dx.doi.org/10.1007/978-3-642-33415-3_34

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041905596

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23285561


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