期刊
MEDICAL IMAGE ANALYSIS
卷 17, 期 4, 页码 417-428出版社
ELSEVIER
DOI: 10.1016/j.media.2013.02.005
关键词
Computed tomography perfusion; Radiation dosage; Sparse representation; Online dictionary learning; Deconvolution algorithm
类别
资金
- National Institute of Neurological Disorders and Stroke (NINDS), a component of the National Institutes of Health (NIH) [5K23NS058387-03S]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据