Spec2VolCAMU-Net：基于多方向时频卷积注意力编码器和Vision-Mamba U-Net的脑电图到功能磁共振成像重建的频谱图到体素模型
Spec2VolCAMU-Net: A Spectrogram-to-Volume Model for EEG-to-fMRI Reconstruction based on Multi-directional Time-Frequency Convolutional Attention Encoder and Vision-Mamba U-Net
AI总结：
本文提出Spec2VolCAMU-Net，一种轻量级EEG到fMRI重建模型，采用多方向时频卷积注意力编码器和Vision-Mamba U-Net解码器，在三个公共基准上实现了最先进的空间保真度，并显著提升SSIM和PSNR。
摘要：
高分辨率功能磁共振成像（fMRI）对于绘制人脑活动图谱至关重要；然而，其成本高昂且在后勤上具有挑战性。如果能够直接从广泛可用的头皮脑电图（EEG）生成相当的体素图像，先进的神经影像学将变得更加可及。现有的EEG到fMRI生成器依赖于无法捕捉跨通道时频线索的普通卷积神经网络（CNN），或依赖于占用内存且稳定性差的沉重Transformer/生成对抗网络（GAN）解码器。为解决这些局限性，我们提出了Spec2VolCAMU-Net，一种轻量级架构，其特点包括用于丰富特征提取的多方向时频卷积注意力编码器，以及使用线性时间状态空间块进行高效长程空间建模的Vision-Mamba U-Net解码器。我们将这项工作的目标定义为在单个体素重建的空间保真度方面建立新的最先进水平，这是最终生成时间连贯的fMRI时间序列的基础前提。Spec2VolCAMU-Net使用混合SSI-MSE损失进行端到端训练，在三个公共基准上实现了最先进的保真度，在NODDI上记录的结构相似性指数（SSIM）为0.693，在Oddball上为0.725，在CN-EPFL上为0.788，分别比之前最佳的SSIM分数提高了14.5%、14.9%和16.9%。此外，它取得了有竞争力的峰值信噪比（PSNR）分数，特别是在CN-EPFL数据集上表现出色，比之前最佳的PSNR提高了4.6%，从而在重建质量上取得了更好的平衡。所提出的模型轻量且高效，适用于临床和研究环境中的实时应用。代码可在https://github.com/hdy6438/Spec2VolCAMU-Net获取。
英文摘要：
High-resolution functional magnetic resonance imaging (fMRI) is essential for mapping human brain activity; however, it remains costly and logistically challenging. If comparable volumes could be generated directly from widely available scalp electroencephalography (EEG), advanced neuroimaging would become significantly more accessible. Existing EEG-to-fMRI generators rely on plain Convolutional Neural Networks (CNNs) that fail to capture cross-channel time-frequency cues or on heavy transformer/Generative Adversarial Network (GAN) decoders that strain memory and stability. To address these limitations, we propose Spec2VolCAMU-Net, a lightweight architecture featuring a Multi-directional Time-Frequency Convolutional Attention Encoder for rich feature extraction and a Vision-Mamba U-Net decoder that uses linear-time state-space blocks for efficient long-range spatial modelling. We frame the goal of this work as establishing a new state of the art in the spatial fidelity of single-volume reconstruction, a foundational prerequisite for the ultimate aim of generating temporally coherent fMRI time series. Trained end-to-end with a hybrid SSI-MSE loss, Spec2VolCAMU-Net achieves state-of-the-art fidelity on three public benchmarks, recording Structural Similarity Index (SSIM) of 0.693 on NODDI, 0.725 on Oddball and 0.788 on CN-EPFL, representing improvements of 14.5%, 14.9%, and 16.9% respectively over previous best SSIM scores. Furthermore, it achieves competitive Signal-to-Noise Ratio (PSNR) scores, particularly excelling on the CN-EPFL dataset with a 4.6% improvement over the previous best PSNR, thus striking a better balance in reconstruction quality. The proposed model is lightweight and efficient, making it suitable for real-time applications in clinical and research settings. The code is available at https://github.com/hdy6438/Spec2VolCAMU-Net.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
