用少量配对数据直接学习高场到低场MRI的退化过程，生成更真实的低场图像。
Subject-Specific Low-Field MRI Synthesis via a Neural Operator
- 提出新型神经算子H2LO，分离坐标与图像建模以捕捉退化机制。
- 在T1w和T2w图像上模拟出比传统方法更接近真实低场影像的结果。
- 适用于低场MRI算法开发与设备虚拟评估，提升诊断潜力。
低场（LF）磁共振成像（MRI）虽降低成本并提升可及性，但信噪比更低、对比度下降，限制临床应用。从高场（HF）MRI模拟低场影像，可实现新设备虚拟评估与低场算法开发。现有模拟方法依赖噪声注入与平滑，无法捕捉真实低场中的对比度退化。为此，本文提出端到端的低场MRI合成框架，仅需少量配对的高低场MRI数据，直接学习从高场到低场的图像退化过程。具体地，引入一种新型的高场到低场坐标-图像解耦神经算子（H2LO），有效建模高频噪声纹理与图像结构。在T1w和T2w MRI上的实验表明，H2LO生成的模拟图像比现有参数化噪声模型和主流图像到图像翻译模型更逼真。此外，其在下游图像增强任务中表现更优，展现了提升低场MRI诊断能力的潜力。
原文摘要 · Abstract (English)
Low-field (LF) magnetic resonance imaging (MRI) improves accessibility and reduces costs but generally has lower signal-to-noise ratios and degraded contrast compared to high field (HF) MRI, limiting its clinical utility. Simulating LF MRI from HF MRI enables virtual evaluation of novel imaging devices and development of LF algorithms. Existing low field simulators rely on noise injection and smoothing, which fail to capture the contrast degradation seen in LF acquisitions. To this end, we introduce an end-to-end LF-MRI synthesis framework that learns HF to LF image degradation directly from a small number of paired HF-LF MRIs. Specifically, we introduce a novel HF to LF coordinate-image decoupled neural operator (H2LO) to model the underlying degradation process, and tailor it to capture high-frequency noise textures and image structure. Experimental results in T1w and T2w MRI demonstrate that H2LO produces more faithful simulated low-field images than existing parameterized noise synthesis models and popular image-to-image translation models. Furthermore, it improves performance in downstream image enhancement tasks, showcasing its potential to enhance LF MRI diagnostic capabilities.
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