用新架构提升儿童低场MRI图像分辨率，让资源有限地区也能用上高质量影像。
GAMBAS: Generalised-Hilbert Mamba for Super-resolution of Paediatric Ultra-Low-Field MRI
- 融合CNN与状态空间模型，设计3D转1D序列化结构捕捉长程上下文。
- 在儿童超低场MRI上实现更优图像重建，细节保留更好、清晰度更高。
- 适合医疗影像增强、低资源地区医学成像研究者使用。
磁共振成像（MRI）对神经发育研究至关重要，但高场（HF）设备在低收入和中等收入国家因成本高昂而难以普及。超低场（ULF）系统缓解了获取不平等问题，但其信噪比低限制了科研与临床应用。深度学习方法可在不增加成本的情况下提升低场扫描质量。例如，结合卷积神经网络（CNN）与变换器模块的方法表现出强大捕捉局部信息与长程上下文的能力。然而，变换器的二次复杂度导致长程敏感性与局部精度之间的不良权衡。本文提出一种混合CNN与状态空间模型（SSM）的架构（GAMBAS），采用新型3D到1D序列化机制，在不牺牲空间精度的前提下学习长程上下文。实验表明，该方法在儿童超低场MRI图像重建任务中优于其他先进医学图像到图像转换模型。
原文摘要 · Abstract (English)
Magnetic resonance imaging (MRI) is critical for neurodevelopmental research, however access to high-field (HF) systems in low- and middle-income countries is severely hindered by their cost. Ultra-low-field (ULF) systems mitigate such issues of access inequality, however their diminished signal-to-noise ratio limits their applicability for research and clinical use. Deep-learning approaches can enhance the quality of scans acquired at lower field strengths at no additional cost. For example, Convolutional neural networks (CNNs) fused with transformer modules have demonstrated a remarkable ability to capture both local information and long-range context. Unfortunately, the quadratic complexity of transformers leads to an undesirable trade-off between long-range sensitivity and local precision. We propose a hybrid CNN and state-space model (SSM) architecture featuring a novel 3D to 1D serialisation (GAMBAS), which learns long-range context without sacrificing spatial precision. We exhibit improved performance compared to other state-of-the-art medical image-to-image translation models.
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