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Proceedings Paper

Image registration and fusion of PET and MRI images using neural network
Author(s): Weifu Wang; Frank Q.H. Ngo; Jyh-Cheng Chen
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Paper Abstract

Multimodality image registration and fusion are widely used in clinical diagnosis and treatment planning because combining information from different modalities can offer more information than single modality. Several image fusion techniques have been developed for these purposes. Neural network technique is commonly used in image or pattern recognition, but up until now there are very few studies on image fusion using neural network. Magnetic resonance imaging (MRI) is an anatomical imaging with high spatial resolution while positron emission tomography (PET) provides biochemical and physiological information but with poor spatial resolution. In this paper, we present a neural network approach for the registration and fusion of PET and MRI images. In our study, we use a multi-layer backpropagation neural network to train spatial characteristic points and to obtain translational range and rotational angles. After image registration and fusion using this method, we show that fused image with transformation has better biochemically consistent result than the one without transformation. Since the method relies on anatomic information in the images rather than on external fiducial markers, ti can be applied retrospectively.

Paper Details

Date Published: 20 September 2001
PDF: 6 pages
Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); doi: 10.1117/12.441670
Show Author Affiliations
Weifu Wang, National Yang-Ming Univ. (Taiwan)
Frank Q.H. Ngo, National Yang-Ming Univ. (Taiwan)
Jyh-Cheng Chen, National Yang-Ming Univ. (Taiwan)

Published in SPIE Proceedings Vol. 4555:
Neural Network and Distributed Processing
Xubang Shen; Jianguo Liu, Editor(s)

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