Delta-WKV: A Novel Meta-in-Context Learner for MRI Super-Resolution
Journal:
arXiv
Published Date:
Feb 28, 2025
Abstract
Magnetic Resonance Imaging (MRI) Super-Resolution (SR) addresses the
challenges such as long scan times and expensive equipment by enhancing image
resolution from low-quality inputs acquired in shorter scan times in clinical
settings. However, current SR techniques still have problems such as limited
ability to capture both local and global static patterns effectively and
efficiently. To address these limitations, we propose Delta-WKV, a novel MRI
super-resolution model that combines Meta-in-Context Learning (MiCL) with the
Delta rule to better recognize both local and global patterns in MRI images.
This approach allows Delta-WKV to adjust weights dynamically during inference,
improving pattern recognition with fewer parameters and less computational
effort, without using state-space modeling. Additionally, inspired by
Receptance Weighted Key Value (RWKV), Delta-WKV uses a quad-directional
scanning mechanism with time-mixing and channel-mixing structures to capture
long-range dependencies while maintaining high-frequency details. Tests on the
IXI and fastMRI datasets show that Delta-WKV outperforms existing methods,
improving PSNR by 0.06 dB and SSIM by 0.001, while reducing training and
inference times by over 15\%. These results demonstrate its efficiency and
potential for clinical use with large datasets and high-resolution imaging.
