Super-resolution reconstruction in MRI: Better images faster?

Improving the resolution in magnetic resonance imaging (MRI) is always done at the expense of either the signal-to-noise ratio (SNR) or the acquisition time. This study investigates whether so-called super-resolution reconstruction (SRR) is an advantageous alternative to direct high-resolution (HR) acquisition in terms of the SNR and acquisition time trade-offs. An experimental framework was designed to accommodate the comparison of SRR images with direct high-resolution acquisitions with respect to these trade-offs. The framework consisted, on one side, of an image acquisition scheme, based on theoretical relations between resolution, SNR, and acquisition time, and, on the other side, of a protocol for reconstructing SRR images from a varying number of acquired low-resolution (LR) images. The quantitative experiments involved a physical phantom containing structures of known dimensions. Images reconstructed by three SRR methods, one based on iterative back-projection and two on regularized least squares, were quantitatively and qualitatively compared with direct HR acquisitions. To visually validate the quantitative evaluations, qualitative experiments were performed, in which images of three different subjects (a phantom, an ex-vivo rat knee, and a post-mortem mouse) were acquired with different MRI scanners. The quantitative results indicate that for long acquisition times, when multiple acquisitions are averaged to improve SNR, SRR can achieve better resolution at better SNR than direct HR acquisitions.