TENDER: Tensor non-local deconvolution enabled radiation reduction in CT perfusion

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• 作者：Ruogu Fang^a ; ^{rfang@cs.fiu.edu}Author Vitae ; Ajay Gupta^b ; ^{ajg9004@med.cornell.edu}Author Vitae ; Junzhou Huang^c ; ^{jzhuang@exchange.uta.edu}Author Vitae ; Pina Sanelli^d ; ^{psanelli@northwell.edu}Author Vitae
• 关键词：Low radiation dose ; CT perfusion ; Tensor non-local deconvolution ; Total variation ; Stroke
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：15 March 2017
• 年：2017
• 卷：229
• 期：Complete
• 页码：13-22
• 全文大小：1815 K
• 卷排序：229

文摘

Stroke is the leading cause of long-term disability and the second leading cause of mortality in the world, and exerts an enormous burden on the public health. Computed Tomography (CT) remains one of the most widely used imaging modality for acute stroke diagnosis. However when coupled with CT perfusion, the excessive radiation exposure in repetitive imaging to assess treatment response and prognosis has raised significant public concerns regarding its potential hazards to both short- and long-term health outcomes. Tensor total variation has been proposed to reduce the necessary radiation dose in CT perfusion without comprising the image quality by fusing the information of the local anatomical structure with the temporal blood flow model. However the local search in the TTV framework fails to leverage the non-local information in the spatio-temporal data. In this paper, we propose TENDER, an efficient framework of non-local tensor deconvolution to maintain the accuracy of the hemodynamic parameters and the diagnostic reliability in low radiation dose CT perfusion. The tensor total variation is extended using non-local spatio-temporal cubics for regularization, and an efficient algorithm is proposed to reduce the time complexity with speedy similarity computation. Evaluations on clinical data of patients subjects with cerebrovascular disease and normal subjects demonstrate the advantage of non-local tensor deconvolution for reducing radiation dose in CT perfusion.