Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors

查看全文 作　　者：Gengsheng [1,2]L.Zeng;Edward [1]V.DiBella

机构地区：[1]Department of Radiology and Imaging Sciences,University of Utah,729 Arapeen Drive,Salt Lake City,UT 84108,USA;[2]Department of Engineering,Utah Valley University,800 West University Parkway,Orem 84058,USA

出　　处：《Visual Computing for Industry,Biomedicine,and Art》索引2020年第3卷第1期,共8页

基　　金：supported by American Heart Association,No.18AJML34280074. 摘　　要：The state-of-the-art approaches for image reconstruction using under-sampled k-space data are compressed sensing based.They are iterative algorithms that optimize objective functions with spatial and/or temporal constraints.This paper proposes a non-iterative algorithm to estimate the un-measured data and then to reconstruct the image with the efficient filtered backprojection algorithm.The feasibility of the proposed method is demonstrated with a patient magnetic resonance imaging study.The proposed method is also compared with the state-of-the-art iterative compressed-sensing image reconstruction method using the total-variation optimization norm. 关键词：Tomographic image reconstruction Under-sampled measurements Fast magnetic resonance imaging Analytics reconstruction

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Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors

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