Tissue-specific sparse deconvolution for brain CT perfusion. (December 2015)

• Record Type: Journal Article
• Authors: Fang, Ruogu; Jiang, Haodi; Huang, Junzhou
• Is Part Of: Computerized medical imaging and graphics. Volume 46:Part 1(2015)
• Page Start: 64
• Page End: 72
• Publication Date: 2015-12
• Subjects: Low-dose CT perfusion -- Tissue-specific -- Dictionary learning -- Ischemic detection -- Deconvolution; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2015.04.008
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Highlights: Tissue-specific deconvolution is proposed to preserve the low-contrast tissues. Tissue-specific dictionaries are learned from tissue segments of high-dose maps. Weighted sparse deconvolution is based on the tissue classification probability. A unified reconstruction framework for the low-dose perfusion deconvolution. Outperforms state-of-art in extensive evaluation on clinical datasets. Abstract: Enhancing perfusion maps in low-dose computed tomography perfusion (CTP) for cerebrovascular disease diagnosis is a challenging task, especially for low-contrast tissue categories where infarct core and ischemic penumbra usually occur. Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra are likely to occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we propose a tissue-specific sparse deconvolution approach to preserve the subtle perfusion information in the low-contrast tissue classes. We first build tissue-specific dictionaries from segmentations of high-dose perfusion maps using online dictionary learning, and then perform deconvolution-based hemodynamic parameters estimation for block-wise tissue segments on the low-dose CTP data. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method compared to state-of-art, and potentially improve diagnostic accuracy by increasing the differentiation between normal and ischemic tissues in the brain.