Journal
MEDICAL IMAGE ANALYSIS
Volume 17, Issue 4, Pages 417-428Publisher
ELSEVIER
DOI: 10.1016/j.media.2013.02.005
Keywords
Computed tomography perfusion; Radiation dosage; Sparse representation; Online dictionary learning; Deconvolution algorithm
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Funding
- National Institute of Neurological Disorders and Stroke (NINDS), a component of the National Institutes of Health (NIH) [5K23NS058387-03S]
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Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute 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 robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose. We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on clinical data of patients with normal and pathological CBF maps. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain. (C) 2013 Elsevier B.V. All rights reserved.
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