LOW-RANK APPROXIMATIONS FOR DYNAMIC IMAGING

被引：0

作者：

Haldar, Justin P. ^{[1
]}

Liang, Zhi-Pei ^{[1
]}

机构：

[1] Univ Illinois, Beckman Inst, Dept Elect & Comp Engn, Urbana, IL 61801 USA

来源：

2011 8TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO | 2011年

关键词：

Dynamic Imaging; Low-Rank Matrix Recovery; Partial Separability; K-T BLAST; MATRIX COMPLETION; RECONSTRUCTION; ACQUISITION; RESOLUTION;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

This paper describes a framework for dynamic imaging based on the representation of a spatiotemporal image as a low-rank matrix. This kind of image modeling is flexible enough to accurately and parsimoniously represent a wide range of dynamic imaging data. Representation using a low-rank model leads to new schemes for data acquisition and image reconstruction, enabling reconstruction from highly-undersampled datasets. Theoretical considerations and algorithms are discussed, and empirical results are provided to illustrate the performance of the approach.

引用

页码：1052 / 1055

页数：4

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