用物理模型生成高低场磁共振图像，提升低场图像质量
HULFSynth : An INR based Super-Resolution and Ultra Low-Field MRI Synthesis via Contrast factor estimation
- 基于组织信号噪声比估计，模拟高低场图像转换
- 合成低场图像的灰白质对比度提升52%，实测数据提升37%
- 无需真实低场数据，适合医学影像增强与设备兼容性研究
我们提出一种无监督单图双向磁共振图像合成方法，可将高场（HF）幅度图像转为类超低场（ULF）图像，反之亦然。不同于现有方法，本方案基于高低场成像中对比度变化的物理机制。前向模型通过目标对比度值估计组织类型信噪比，实现从HF到ULF的仿真转换。针对超分辨率任务，采用隐式神经表示（INR）网络，在无真实高场数据的情况下，同时预测组织分割与图像强度。方法在由标准3T T₁加权图像生成的合成类64mT ULF数据上进行定性评估，并使用真实配对的3T-64mT T₁加权图像进行验证。结果显示，合成类ULF图像灰质-白质对比度提升52%，实际64mT图像提升37%。敏感性实验表明，前向模型对目标对比度、噪声及初始种子具有强鲁棒性。
原文摘要 · Abstract (English)
We present an unsupervised single image bidirectional Magnetic Resonance Image (MRI) synthesizer that synthesizes an Ultra-Low Field (ULF) like image from a High-Field (HF) magnitude image and vice-versa. Unlike existing MRI synthesis models, our approach is inspired by the physics that drives contrast changes between HF and ULF MRIs. Our forward model simulates a HF to ULF transformation by estimating the tissue-type Signal-to-Noise ratio (SNR) values based on target contrast values. For the Super-Resolution task, we used an Implicit Neural Representation (INR) network to synthesize HF image by simultaneously predicting tissue-type segmentations and image intensity without observed HF data. The proposed method is evaluated using synthetic ULF-like data from generated from standard 3T T$_1$-weighted images for qualitative assessments and paired 3T-64mT T$_1$-weighted images for validation experiments. WM-GM contrast improved by 52% in synthetic ULF-like images and 37% in 64mT images. Sensitivity experiments demonstrated the robustness of our forward model to variations in target contrast, noise and initial seeding.
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