Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach
Journal:
arXiv
Published Date:
Mar 7, 2025
Abstract
Magnetic Resonance Imaging (MRI) is crucial for clinical diagnostics but is
hindered by prolonged scan times. Current deep learning models enhance MRI
reconstruction but are often memory-intensive and unsuitable for
resource-limited systems. This paper introduces a lightweight MRI
reconstruction model leveraging Kronecker-Parameterized Hypercomplex Neural
Networks to achieve high performance with reduced parameters. By integrating
Kronecker-based modules, including Kronecker MLP, Kronecker Window Attention,
and Kronecker Convolution, the proposed model efficiently extracts spatial
features while preserving representational power. We introduce Kronecker U-Net
and Kronecker SwinMR, which maintain high reconstruction quality with
approximately 50% fewer parameters compared to existing models. Experimental
evaluation on the FastMRI dataset demonstrates competitive PSNR, SSIM, and
LPIPS metrics, even at high acceleration factors (8x and 16x), with no
significant performance drop. Additionally, Kronecker variants exhibit superior
generalization and reduced overfitting on limited datasets, facilitating
efficient MRI reconstruction on hardware-constrained systems. This approach
sets a new benchmark for parameter-efficient medical imaging models.
