| Paper: | MLSP-P2.4 | ||
| Session: | Bioinformatics and Biomedical Applications | ||
| Time: | Wednesday, May 19, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Biomedical Applications and Neural Engineering | ||
| Title: | MIXTURE OF COMPETITIVE LINEAR MODELS FOR PHASED-ARRAY MAGNETIC RESONANCE IMAGING | ||
| Authors: | Deniz Erdogmus; University of Florida | ||
| Rui Yan; University of Florida | |||
| Erik G. Larsson; The George Washington University | |||
| Jose C. Principe; University of Florida | |||
| Jeffrey R. Fitzsimmons; University of Florida | |||
| Abstract: | Phased-array magnetic resonance imaging is an important contemporary research field in terms of the expected clinical gains in medical imaging technology. Recent research focused on heuristic coil image recombination methods as well as statistical signal processing approaches. In this paper, we investigate the performance of an adaptive signal processing approach, namely mixture of competitively trained models. The proposed method has the ability to train on a set of images and generalize its performance to previously unseen images. Performance evaluations on real data validate the effectiveness of this method. | ||
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