Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning-Reference-Cited by-同舟云学术

Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning

Published:2013-05 Issue:4 Volume:17 Page:417-428
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Fang Ruogu,Chen Tsuhan,Sanelli Pina C.

Funder

National Institute of Neurological Disorders and Stroke (NINDS)

National Institutes of Health (NIH)

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference57 articles.

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4. Quantification of bolus-tracking MRI: improved characterization of the tissue residue function using Tikhonov regularization;Calamante;Magnetic Resonance in Medicine,2003

5. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information;Candès;IEEE Transactions on Information Theory,2006

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