Image registration and fusion of PET and MRI images using neural network

Weifu Wang; Frank Q.H. Ngo; Jyh-Cheng Chen

doi:10.1117/12.441670

20 September 2001 Image registration and fusion of PET and MRI images using neural network

Weifu Wang, Frank Q.H. Ngo, Jyh-Cheng Chen

Author Affiliations +

Proceedings Volume 4555, Neural Network and Distributed Processing; (2001) https://doi.org/10.1117/12.441670
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

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.

Citation Download Citation

Weifu Wang, Frank Q.H. Ngo, and Jyh-Cheng Chen "Image registration and fusion of PET and MRI images using neural network", Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); https://doi.org/10.1117/12.441670

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