用克罗内克参数化技术，让MRI重建模型更轻量高效。
Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach
- 用克罗内克模块替代传统网络，大幅减少参数量。
- 在8倍和16倍加速下仍保持高图像质量，PSNR等指标优秀。
- 适合算力受限的医疗设备，泛化性好且不易过拟合。
磁共振成像（MRI）对临床诊断至关重要，但扫描时间长限制了其应用。当前深度学习模型虽提升了重建效果，却常因内存占用高难以部署于资源受限系统。本文提出一种轻量级MRI重建模型，采用克罗内克参数化的超复数神经网络，在显著降低参数量的同时保持优异性能。通过引入克罗内克多层感知机、克罗内克窗口注意力和克罗内克卷积等模块，模型高效提取空间特征并保留表征能力。所提克罗内克U-Net与克罗内克SwinMR相比现有模型参数量减少约50%，在FastMRI数据集上的实验表明，即使在8倍和16倍加速度下，其峰值信噪比（PSNR）、结构相似性（SSIM）和感知图像质量指标（LPIPS）表现仍具竞争力，无明显性能下降。此外，克罗内克变体在小样本数据上展现出更强泛化能力与更低过拟合风险，适用于硬件受限环境下的高效医学图像重建。该方法为参数高效医疗影像模型树立了新基准。
原文摘要 · Abstract (English)
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.
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