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.

翻译：我们提出了一种无监督单图像双向磁共振成像合成方法，该方法能从高场幅度图像合成超低场类图像，反之亦然。与现有磁共振成像合成模型不同，我们的方法受驱动高场与超低场磁共振成像之间对比度变化的物理学原理启发。前向模型通过基于目标对比度值估计组织类型信噪比，模拟高场到超低场的变换。在超分辨率任务中，我们使用隐式神经表示网络，通过同时预测组织类型分割和图像强度来合成高场图像，且无需观察高场数据。所提方法通过从标准3T T₁加权图像生成的合成超低场类数据进行定性评估，并采用配对3T-64mT T₁加权图像进行验证实验。合成超低场类图像的灰质-白质对比度提升52%，64mT图像提升37%。灵敏度实验表明，我们的前向模型对目标对比度、噪声和初始种子点的变化具有鲁棒性。