Super-resolution MRI Using Finite Rate of Innovation Curves

TL;DR

This paper introduces a novel two-stage super-resolution method for MRI that leverages finite rate of innovation theory to accurately estimate image edges and improve resolution, outperforming traditional methods.

Contribution

The work extends FRI theory to 2D MRI, proposing a robust edge mask estimation and a super-resolution reconstruction framework that enhances image quality.

Findings

Improved super-resolution performance over total variation methods.

Effective edge detection in noisy and noiseless conditions.

Enhanced image resolution from low-frequency k-space samples.

Abstract

We propose a two-stage algorithm for the super-resolution of MR images from their low-frequency k-space samples. In the first stage we estimate a resolution-independent mask whose zeros represent the edges of the image. This builds off recent work extending the theory of sampling signals of finite rate of innovation (FRI) to two-dimensional curves. We enable its application to MRI by proposing extensions of the signal models allowed by FRI theory, and by developing a more robust and efficient means to determine the edge mask. In the second stage of the scheme, we recover the super-resolved MR image using the discretized edge mask as an image prior. We evaluate our scheme on simulated single-coil MR data obtained from analytical phantoms, and compare against total variation reconstructions. Our experiments show improved performance in both noiseless and noisy settings.