Dept. of Electr. & Comput. Eng., Calgary Univ., Alta.
IEEE Trans Med Imaging. 1995;14(2):409-12. doi: 10.1109/42.387722.
A recent paper by Yan and Mao (see ibid., vol.12, no.1, p.73-7, 1993) provided the results of using a neural network based nonlinear prediction algorithm to extrapolate truncated magnetic resonance data. The extrapolation is intended to reduce the truncation artifacts that result when reconstructing an image from a limited k-space magnetic resonance data set using the discrete Fourier transform. When attempting to quantitatively compare Yan and Mao's method with the authors' own existing constrained modeling algorithm, the authors discovered a systematic error in Yan and Mao's analysis. With the error corrected, it was found that Yan and Mao's approach worked significantly better than they have reported and was more stable in the presence of noise.
严和毛（见同期刊物，第 12 卷，第 1 期，第 73-7 页，1993）最近的一篇论文提供了使用基于神经网络的非线性预测算法来推断截断磁共振数据的结果。这种推断旨在减少在使用离散傅立叶变换从有限的 k 空间磁共振数据集重建图像时产生的截断伪影。当试图定量比较严和毛的方法与作者自己现有的约束建模算法时，作者发现严和毛的分析中存在系统误差。在纠正错误后，发现严和毛的方法的效果明显好于他们所报告的，并且在存在噪声的情况下更稳定。
