MRIQT：面向新生儿超低场MRI的物理感知扩散模型用于图像质量迁移
MRIQT: Physics-Aware Diffusion Model for Image Quality Transfer in Neonatal Ultra-Low-Field MRI
AI总结：
MRIQT通过物理感知的扩散模型实现新生儿超低场MRI到高场MRI的图像质量迁移，提升诊断准确性与病理可视化效果。
摘要：
便携式超低场MRI（uLF-MRI，0.064 T）为新生儿护理提供了可及的神经成像，但其信噪比低、诊断质量差于高场（HF）MRI。我们提出了MRIQT，一种用于从uLF到HF MRI图像质量迁移（IQT）的3D条件扩散框架。MRIQT结合了现实的K空间退化以实现物理一致的uLF模拟，v预测与无分类器引导以实现稳定的图像到图像生成，以及加权的3D感知损失以确保解剖学保真度。该模型从受污染的uLF输入中去噪，基于相同的扫描进行条件化，利用体积分层注意力UNet架构进行结构保持的翻译。在具有多样化病理的新生儿队列上训练，MRIQT在PSNR上比最近的GAN和CNN基线高出15.3%，比当前最先进的方法高出1.78%，同时医生对其中85%的输出评价为高质量，病理特征清晰。MRIQT实现了便携式超低场（uLF）MRI的高保真度、基于扩散的增强，从而实现可信赖的新生儿脑评估。
英文摘要：
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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