Sparsity-Based deconvolution of low-dose perfusion CT using learned dictionaries

  • Authors:

  • Ruogu Fang;Tsuhan Chen;Pina C. Sanelli

  • Affiliations:

  • Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY;Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY;Department of Radiology, Weill Cornell Medical College, NYC, NY

  • Venue:
  • MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
  • Year:
  • 2012

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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.