DCE-MRI Analysis Using Sparse Adaptive Representations

被引：0

作者：

Chiusano, Gabriele ^{[1
]}

Stagliano, Alessandra ^{[1
]}

Basso, Curzio ^{[1
]}

Verri, Alessandro ^{[1
]}

机构：

[1] Univ Genoa, DISI, I-16146 Genoa, Italy

来源：

MACHINE LEARNING IN MEDICAL IMAGING | 2011年 / 7009卷

关键词：

CONTRAST-ENHANCED MRI;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Dynamic contrast-enhanced MRI (DCE-MRI) plays an important role as an imaging method for the diagnosis and evaluation of several diseases. Indeed, clinically relevant, per-voxel quantitative information may be extracted through the analysis of the enhanced MR signal. This paper presents a method for the automated analysis of DCE-MRI data that works by decomposing the enhancement curves as sparse linear combinations of elementary curves learned without supervision from the data. Experimental results show that performances in denoising and unsupervised segmentation improve over parametric methods.

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收藏

页码：67 / 74

页数：8

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