Noise Reduction in Functional MR Images by Common Factor Models

摘要：

This study proposes the use of common factor models to reduce noise in functional magnetic resonance (MR) images. The models estimate the errors due to instrumental instability, voxel specific noises and other nontask-related contamination. After noise reduction, the functional images can be analyzed by t-test, correlation analysis, independent component analysis, and neural network algorithms. This study also suggests the regression method for estimating both intensity waveforms in the reduced space (i.e.t factor or component scores) and corrected waveforms in the original data space. The common factor models for noise reduction were tested in an event-related functional MR experiment using the concentric checkerboard pattern stimulus. The performance of common factor models was better than principal component analysis for noise reduction.

关键词： factor analysis fMRI independent component analysis principal component analysis

作者: Chien-Chih Huang Michelle Liou Philip E. Cheng Chien-Chung Chen

作者单位: Institute of Statistical Science, Academia Sinica, Taipei 11529 Neurometrics Institute, Berkeley, USA

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 457-461

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）