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

  • Ruogu Fang
  • Tsuhan Chen
  • Pina C. Sanelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7510)

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.

Keywords

Cerebral Blood Flow Sparse Code Compute Tomography Perfusion Perfusion Parameter Learn Dictionary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ruogu Fang
    • 1
  • Tsuhan Chen
    • 1
  • Pina C. Sanelli
    • 2
  1. 1.Department of Electrical and Computer EngineeringCornell UniversityIthacaUSA
  2. 2.Department of RadiologyWeill Cornell Medical CollegeNYCUSA

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