用扩散模型提升新生儿便携式低场MRI图像质量，让弱信号变清晰。
MRIQT: Physics-Aware Diffusion Model for Image Quality Transfer in Neonatal Ultra-Low-Field MRI
- 结合物理建模的K空间降噪，模拟真实低场成像过程。
- 在新生儿数据集上实现PSNR提升15.3%，超现有最优水平1.78%。
- 医生评估85%输出图像质量良好，病理清晰可见，适合临床使用。
便携式超低场磁共振（uLF-MRI，0.064 T）为新生儿神经影像提供可及性，但相比高场（HF）MRI存在信噪比低、诊断质量差的问题。本文提出MRIQT，一种3D条件扩散框架，用于从uLF到HF MRI的图像质量迁移（IQT）。MRIQT融合真实的K空间退化以实现物理一致的uLF模拟，采用v-prediction与无分类器引导实现稳定图像生成，并引入信噪比加权的3D感知损失保障解剖结构保真度。模型通过条件去噪，从同一扫描的噪声uLF输入中重建图像，采用体素注意力UNet架构实现结构保留转换。在包含多种病理的新生儿队列上训练，MRIQT在PSNR上优于近期GAN和CNN基线15.3%，较当前最优方法高出1.78%；医生评估显示85%的输出图像质量良好且病理清晰可见。MRIQT实现了基于扩散模型的高保真度uLF-MRI增强，支持可靠的新生儿脑部评估。
原文摘要 · Abstract (English)
Portable ultra-low-field MRI (uLF-MRI, 0.064 T) offers accessible neuroimaging for neonatal care but suffers from low signal-to-noise ratio and poor diagnostic quality compared to high-field (HF) MRI. We propose MRIQT, a 3D conditional diffusion framework for image quality transfer (IQT) from uLF to HF MRI. MRIQT combines realistic K-space degradation for physics-consistent uLF simulation, v-prediction with classifier-free guidance for stable image-to-image generation, and an SNR-weighted 3D perceptual loss for anatomical fidelity. The model denoises from a noised uLF input conditioned on the same scan, leveraging volumetric attention-UNet architecture for structure-preserving translation. Trained on a neonatal cohort with diverse pathologies, MRIQT surpasses recent GAN and CNN baselines in PSNR 15.3% with 1.78% over the state of the art, while physicians rated 85% of its outputs as good quality with clear pathology present. MRIQT enables high-fidelity, diffusion-based enhancement of portable ultra-low-field (uLF) MRI for deliable neonatal brain assessment.
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