SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging
This addresses the speed-resolution trade-off in MRI for medical imaging applications, presenting an incremental improvement over existing two-step methods.
The paper tackles the challenge of improving MRI resolution and acquisition speed by combining reconstruction and super-resolution into a single step to recover high-resolution images from low-resolution undersampled k-space data, achieving good visual and perceptual quality in experiments on in-vivo brain data.
Improving the image resolution and acquisition speed of magnetic resonance imaging (MRI) is a challenging problem. There are mainly two strategies dealing with the speed-resolution trade-off: (1) $k$-space undersampling with high-resolution acquisition, and (2) a pipeline of lower resolution image reconstruction and image super-resolution. However, these approaches either have limited performance at certain high acceleration factor or suffer from the error accumulation of two-step structure. In this paper, we combine the idea of MR reconstruction and image super-resolution, and work on recovering HR images from low-resolution under-sampled $k$-space data directly. Particularly, the SR-involved reconstruction can be formulated as a variational problem, and a learnable network unrolled from its solution algorithm is proposed. A discriminator was introduced to enhance the detail refining performance. Experiment results using in-vivo HR multi-coil brain data indicate that the proposed SRR-Net is capable of recovering high-resolution brain images with both good visual quality and perceptual quality.