Bayesian reconstruction in synthetic magnetic resonance imaging

摘要

In magnetic resonance imaging (MRI), three unobservable physical quantities are combined at the pixel level to produce the image. Control parameters can be pre-set to highlight contrast between different tissue types but the optimal values may be problem- and patient-specific and not known in advance. The aim in synthetic MRI is to estimate the underlying physical quantities from three images, taken at conventional settings, and to use these to synthesize images for arbitrary control parameters. Standard least squares methods are inadequate for this ill-conditioned inverse problem. The paper describes several forms of Bayesian reconstruction and suggests that these provide satisfactory alternatives.