Factor Analysis of Dynamic Sequence with Spatial Prior for 2D Cardiac Spect Sequences Analysis

被引：1

作者：

Filippi, Marc ^{[1
]}

Desvignes, Michel ^{[1
]}

Moisan, Eric ^{[1
]}

Ghezzi, Catherine ^{[1
]}

Perret, Pascale ^{[1
]}

Fagret, Daniel ^{[2
]}

机构：

[1] Univ Grenoble Alpes, GIPSA LAB, St Martin Dheres, France

[2] Univ Grenoble Alpes, INSERM1039, St Martin Dheres, France

来源：

ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, ACIVS 2016 | 2016年 / 10016卷

关键词：

Source separation; SPECT; Factor Analysis; Spatial priors; Penalized least squares; SEPARATION; FAMIS;

D O I：

10.1007/978-3-319-48680-2_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Unmixing is often a necessary step to analyze 2D SPECT image sequence. However, factor analysis of dynamic sequences (FADS), the commonly used method for unmixing SPECT sequences, suffers from non-uniqueness issue. Optimization-based methods were developed to overcome this issue. These methods are effective but need improvement when the mixing is important or with very low SNR. In this paper, a new objective function using soft spatial prior knowledge is developed. Comparison with previous methods, efficiency and robustness to the choice of priors are illustrated with tests on synthetic dataset. Results on 2D SPECT sequences with high level of noise are also presented and compared.

引用

页码：228 / 237

页数：10

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