title: Application of autoregressive modelling in magnetic resonance imaging to remove noise and truncation artifacts.
abstract: Magnetic resonance imaging data is conventionally reconstructed using two dimensional discrete Fourier transforms. However, there is 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 using a modified autoregressive model, the Transient Error method.
authors: Department of Electrical Engineering, University of Calgary, Alberta, Canada.
authors: M R
authors: MR
authors: Smith
authors: S T
authors: ST
authors: Nichols
authors: R M
authors: RM
authors: Henkelman
authors: M L
authors: ML
authors: Wood
mesh_terms: D000465:Algorithms; D005583:Fourier Analysis; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical