对比3D与2.5D网络在脑MRI超分辨率中的表现，3D模型效果更优。
Comparative Analysis of 3D Convolutional and 2.5D Slice-Conditioned U-Net Architectures for MRI Super-Resolution via Elucidated Diffusion Models
- 采用3D卷积和2.5D切片条件U-Net，结合扩散模型进行脑MRI超分。
- 3D模型在测试集上达到37.75 dB PSNR，显著优于2.5D模型（35.82 dB）。
- 适合关注医学图像超分辨率与扩散模型应用的研究者。
磁共振成像（MRI）超分辨率（SR）方法通过计算增强低分辨率数据以逼近高分辨率质量，为昂贵高场扫描提供替代方案。本文研究了一种阐明扩散模型（EDM）框架用于脑部MRI超分辨率，并对比了两种U-Net主干结构：(i) 完全3D卷积U-Net，使用3D卷积和多头自注意力处理体积分块；(ii) 2.5D切片条件U-Net，独立超分辨每一切片并利用邻近切片提供跨切片上下文。两者均采用Karras等提出的连续σ噪声条件，在FOMO60K数据集的NKI队列上训练。在包含5名受试者（6个体积，993张切片）的独立测试集上，3D模型取得37.75 dB PSNR、0.997 SSIM和0.020 LPIPS，优于预训练的EDSR基线（35.57 dB / 0.024 LPIPS）和2.5D变体（35.82 dB），三项指标均提升。
原文摘要 · Abstract (English)
Magnetic resonance imaging (MRI) super-resolution (SR) methods that computationally enhance low-resolution acquisitions to approximate high-resolution quality offer a compelling alternative to expensive high-field scanners. In this work we investigate an elucidated diffusion model (EDM) framework for brain MRI SR and compare two U-Net backbone architectures: (i) a full 3D convolutional U-Net that processes volumetric patches with 3D convolutions and multi-head self-attention, and (ii) a 2.5D slice-conditioned U-Net that super-resolves each slice independently while conditioning on an adjacent slice for inter-slice context. Both models employ continuous-sigma noise conditioning following Karras et al. and are trained on the NKI cohort of the FOMO60K dataset. On a held-out test set of 5 subjects (6 volumes, 993 slices), the 3D model achieves 37.75 dB PSNR, 0.997 SSIM, and 0.020 LPIPS, improving on the off-the-shelf pretrained EDSR baseline (35.57 dB / 0.024 LPIPS) and the 2.5D variant (35.82 dB) across all three metrics under the same test data and degradation pipeline.
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