Truncation effects in SENSE reconstruction

Finite sampling is an important practical issue in Fourier imaging systems. Although data truncation effects are well understood in conventional Fourier imaging where a single uniform receiver channel is used for data acquisition, this issue is not yet fully addressed in parallel imaging where an array of nonuniform receiver channels is used for sensitivity encoding to enable sub-Nyquist sampling of k-space. This article presents a systematic analysis of the problem by comparing the truncation effects in parallel imaging with those in conventional Fourier imaging. Specifically, it derives a convolution kernel function to characterize the truncation effects, which is shown to be approximately equal to that associated with the conventional Fourier imaging scheme. This article also describes a set of conditions under which significant differences between the truncation effects in parallel imaging and conventional Fourier imaging occur. The results should provide useful insight into interpreting and reducing data truncation effects in parallel imaging.