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Partial Fourier MRI: AR models with SVD

机译:部分傅立叶MRI:具有SVD的AR模型

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We investigated several partial Fourier imaging techniques to reconstruct magnetic resonance images from the central fifty percent phase encodings. Parametric models were chosen and compared for accuracy in predicting the full time-domain data, and for final image quality. The auto-regressive model, with parameter estimation from Single Value Decomposition (AR-SVD) was compared against the iterative Levinson-Durbin approach, and against auto-regressive moving-average models suggested by Marple and Smith. Suitable MR images from a phantom were obtained for testing the models. AR-SVD reconstruction most closely matched the full image intensity function, giving the lowest MSE, especially with gradient-echo and fast spin echo images, thus demonstrating superior tolerance to noise, at the cost of increased computing load.
机译:我们研究了几种部分傅立叶成像技术来重建来自中央阶段编码的磁共振图像。选择参数模型并进行比较,以准确地预测全时域数据,以及用于最终图像质量。与单值分解(AR-SVD)的具有参数估计的自动回归模型与迭代Levinson-Durbin方法进行比较,并针对Marple和Smith建议的自动回归移动平均模型。获得来自幻像的合适的MR图像以测试模型。 AR-SVD重建最接近匹配全部图像强度函数,给出最低的MSE,特别是梯度回波和快速旋转回波图像,从而以提高计算负荷的成本展示了卓越的噪声容差。

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