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.

翻译：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影像学手段，但与高场（HF）MRI相比，其信噪比低且诊断质量差。我们提出了MRIQT，一种用于从uLF到HF MRI图像质量迁移（IQT）的三维条件扩散框架。MRIQT结合了物理一致的uLF模拟所需的逼真K空间退化方法、采用无分类器引导的v预测以实现稳定的图像到图像生成，以及用于解剖保真度的信噪比加权三维感知损失。该模型以同一扫描为条件，从加噪的uLF输入进行去噪，利用具有注意力机制的体积UNet架构实现结构保持的转换。在包含多种病理的新生儿队列数据上训练后，MRIQT在PSNR指标上以15.3%的优势超越近期GAN和CNN基线方法，并较现有最佳结果提升1.78%，同时医生评定其85%的输出为质量良好且病理特征清晰。MRIQT实现了基于扩散模型的高保真增强，为便携式超低场（uLF）MRI提供了可靠的新生儿脑部评估能力。