A comparison of models used as alternative magnetic resonance image reconstruction methods

In magnetic resonance (MR) imaging, excellent reconstructions are obtained on large data sets using the inverse discrete Fourier transform (IDFT). Modeling procedures have been proposed to overcome the image artifacts from the truncation of small data sets. In this paper, a relationship between image reconstruction using modeling and the standard IDFT is presented. A comparison of the assumptions behind the Smith and Haacke models is given and an experimental evaluation of the validity of the models provided. Various methods of evaluating model coefficients are discussed. Images are reconstructed from both models using the Transient Error reconstruction approach (TERA) algorithm. TERA is an algorithm that reintroduces data information components that cannot be modeled: useful when the assumed model characteristics do not completely match all portions of the image. Although very different in their basic assumptions, both the Smith and Haacke models were found to reduce truncation artifacts and improve resolution when used with the TERA algorithm.