Application of autoregressive modelling in magnetic resonance imaging to remove noise and truncation artifacts.
作者： M.R. Smith , S.T. Nichols , R.M. Henkelman , M.L. Wood
DOI: 10.1016/0730-725X(86)91067-2
关键词: Spectral density estimation 、 Magnetic resonance imaging 、 Algorithm 、 Fourier analysis 、 Autoregressive model 、 Nuclear magnetic resonance 、 k-space 、 Noise 、 Transient (oscillation) 、 Truncation (statistics) 、 Mathematics
摘要: Magnetic resonance imaging data is conventionally reconstructed using two dimensional discrete Fourier transforms. However, there growing interest in other types of spectral estimation which minimize noise and artifacts due to truncated data. This note presents preliminary results--showing the improvement obtainable a modified autoregressive model, Transient Error method.
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