基于多层神经网络的磁共振图像分辨率增强

[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems Pub Date : 1992-06-14 DOI:10.1109/CBMS.1992.245027

Hong Yan, J. Mao, Benjamin Chen

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引用次数: 0

摘要

当使用传统的傅里叶变换方法进行重构时，如果没有足够的高频数据，则磁共振图像可能包含截断伪影。作者提出了一种利用多层神经网络减少伪影的方法。该网络由一个线性输出层和至少一个非线性隐藏层组成。该方法基于已知的低频分量预测缺失的高频分量，并用于提高图像的分辨率。用仿真数据对该方法进行了验证，取得了良好的效果。

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MR image resolution enhancement using a multi-layer neural network

A magnetic resonance image may contain truncation artifacts if there are not enough high-frequency data when the conventional Fourier transform method is used for reconstruction. The authors propose a method for reducing the artifacts using a multilayer neural network. The network consists of one linear output layer and at least one nonlinear hidden layer. In this method the missing high-frequency components are predicted based on known low-frequency components and are used to improve the resolution of the image. The method is tested with simulated data with good results.<>

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[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems

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