Comments on "Data truncation artifact reduction in MR imaging using a multilayer neural network"

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.