标题： SFNet：用于高效阿尔茨海默病诊断的空域-频域深度学习网络 显示英文标题

标题： SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis

摘要： 阿尔茨海默病（AD）是一种主要影响老年人群的进行性神经退行性疾病，目前尚无治愈方法。磁共振成像（MRI）作为一种非侵入性成像技术，在AD的早期诊断中至关重要。MRI固有地包含空间和频率信息，因为原始信号是在频率域中获取的，并通过傅里叶变换重建为空间图像。然而，大多数现有的AD诊断模型仅从单一领域提取特征，限制了它们全面捕捉疾病复杂神经影像特征的能力。尽管一些研究结合了空间和频率信息，但它们大多局限于2D MRI，使得3D MRI中双域分析的潜力尚未被探索。为了克服这一限制，我们提出了一种称为时空网络（SFNet）的端到端深度学习框架，这是首个同时利用空间和频率域信息以增强基于3D MRI的AD诊断的框架。SFNet集成了一个增强的密集卷积网络来提取局部空间特征，以及一个全局频率模块来捕捉全局频率域表示。此外，提出了一种新颖的多尺度注意力模块以进一步优化空间特征提取。在阿尔茨海默病神经影像倡议（ADNI）数据集上的实验表明，SFNet优于现有基线，并在分类认知正常（CN）和AD时减少了计算开销，达到了95.1%的准确率。 显示英文摘要

摘要： Alzheimer's disease (AD) is a progressive neurodegenerative disorder that predominantly affects the elderly population and currently has no cure. Magnetic Resonance Imaging (MRI), as a non-invasive imaging technique, is essential for the early diagnosis of AD. MRI inherently contains both spatial and frequency information, as raw signals are acquired in the frequency domain and reconstructed into spatial images via the Fourier transform. However, most existing AD diagnostic models extract features from a single domain, limiting their capacity to fully capture the complex neuroimaging characteristics of the disease. While some studies have combined spatial and frequency information, they are mostly confined to 2D MRI, leaving the potential of dual-domain analysis in 3D MRI unexplored. To overcome this limitation, we propose Spatio-Frequency Network (SFNet), the first end-to-end deep learning framework that simultaneously leverages spatial and frequency domain information to enhance 3D MRI-based AD diagnosis. SFNet integrates an enhanced dense convolutional network to extract local spatial features and a global frequency module to capture global frequency-domain representations. Additionally, a novel multi-scale attention module is proposed to further refine spatial feature extraction. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that SFNet outperforms existing baselines and reduces computational overhead in classifying cognitively normal (CN) and AD, achieving an accuracy of 95.1%.