REDUCED LIPID CONTAMINATION IN IN-VIVO H-1 MRSI USING TIME-DOMAIN FITTING AND NEURAL-NETWORK CLASSIFICATION

被引：20

作者：

DEBEER, R

MICHELS, F

VANORMONDT, D

VANTONGEREN, BPO

LUYTEN, PR

VANVROONHOVEN, H

机构：

[1] University of Technology Delft, Department of Applied Physics, 2600 GA Delft

[2] MR Physics Department, Philips Medical Systems, 5680 DA Best

来源：

MAGNETIC RESONANCE IMAGING | 1993年 / 11卷 / 07期

关键词：

SPECTROSCOPIC IMAGING; LIPID CONTAMINATION; TIME-DOMAIN FITTING; NEURAL NETWORK CLASSIFICATION;

D O I：

10.1016/0730-725X(93)90220-8

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

It is a well-known problem that metabolite maps, reconstructed from in vivo H-1 MRSI data sets, may suffer from contamination caused by the presence of strong lipid signals. In the present investigation, the lipid problem was addressed by applying specific signal processing and data-analysis techniques, combined with pattern recognition based on the concept of the artificial neural network. In order to arrive at images, cleaned from lipid artifacts, we have applied our previously introduced iterative and noniterative time-domain fitting procedures. Furthermore, reduction in computational time of the image reconstructions could be realized by using information provided by a neural network classification of the spectra, calculated from the MRSI data sets.

引用

页码：1019 / 1026

页数：8

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