Model-based super-resolution for MRI

Abstract

Conventional 1.5 T magnetic resonance imaging (MRI) systems suffer from poor out-of-plane resolution (slice dimension), usually with in-plane resolution being several times higher than the former. Post-acquisition, super-resolution (SR) filtering is a viable alternative and a less expensive, off-line image processing approach that is employed to improve tissue resolution and contrast on acquired three-dimensional (3D) MR images. We introduce an SR framework that models a true acquired volume information by taking into account slice thickness and spacing between slices. Previous SR schemes have not considered this type of acquisition information or they have required specialized MR acquisition techniques. Evaluations based on synthetic data and clinical knee MRI data show superior performance of this method over an existing averaging method.