Sparsity-based deconvolution of low-dose perfusion CT using learned dictionaries. Academic Article uri icon

Overview

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.

publication date

  • January 1, 2012

Research

keywords

  • Brain
  • Tomography, X-Ray Computed

Identity

PubMed Central ID

  • PMC3657293

Scopus Document Identifier

  • 84872553999

PubMed ID

  • 23285561

Additional Document Info

volume

  • 15

issue

  • Pt 1