A neural network approach for image reconstruction in electron magnetic resonance tomography
- 作者：D. Christopher Durairaj ; Murali C. Krishna ; Ramach ; ran Murugesan
- 关键词：Electron magnetic resonance tomography ; Image reconstruction ; Artificial neural networks ; Back propagation ; Filtered back projection ; Multiplicative algebraic reconstruction technique
- 刊名：Computers in Biology and Medicine
- 出版年：2007
- 期刊代码：70_00104825
- 类别：med
- 出版时间：October 2007
- 卷：37
- 期：10
- 页码：1492-1501
- 文件大小：601 K
摘要
An object-oriented, artificial neural network (ANN) based, application system for reconstruction of two-dimensional spatial images in electron magnetic resonance (EMR) tomography is presented. The standard back propagation algorithm is utilized to train a three-layer sigmoidal feed-forward, supervised, ANN to perform the image reconstruction. The network learns the relationship between the ‘ideal’ images that are reconstructed using filtered back projection (FBP) technique and the corresponding projection data (sinograms). The input layer of the network is provided with a training set that contains projection data from various phantoms as well as in vivo objects, acquired from an EMR imager. Twenty five different network configurations are investigated to test the ability of the generalization of the network. The trained ANN then reconstructs two-dimensional temporal spatial images that present the distribution of free radicals in biological systems. Image reconstruction by the trained neural network shows better time complexity than the conventional iterative reconstruction algorithms such as multiplicative algebraic reconstruction technique (MART). The network is further explored for image reconstruction from ‘noisy’ EMR data and the results show better performance than the FBP method. The network is also tested for its ability to reconstruct from limited-angle EMR data set.