用MRI生成高保真tau PET图像，降低阿尔茨海默病诊断成本
MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer's Disease Imaging
- 基于多尺度残差与注意力机制改进VQGAN架构
- 合成图像在多项指标上优于现有方法（PSNR 30.65 dB）
- 适合临床研究和需要低成本影像替代的场景
Tau正电子发射断层扫描（PET）是阿尔茨海默病（AD）的关键诊断手段，但其广泛应用受限于辐射暴露、设备稀缺、临床负荷重及高昂成本。为此，本文提出多尺度CBAM残差向量量化生成对抗网络（MCR-VQGAN），从结构化T1加权MRI合成高质量tau PET图像。MCR-VQGAN通过引入多尺度卷积、ResNet模块和卷积块注意力模块（CBAM），增强局部与全局特征捕捉能力。基于ADNI数据库中的222对配对T1-MRI与tau PET数据，对比cGAN、WGAN-GP、CycleGAN和基线VQGAN，MCR-VQGAN在所有评估指标上表现更优（均方误差 = 0.0056 ± 0.0061，峰值信噪比 = 30.65 ± 4.47 dB，结构相似性 = 0.9263 ± 0.0469）。使用真实tau PET训练的CNN分类器，在真实（63.64%）与合成（65.91%）图像上达到相近准确率，表明诊断相关特征得以保留。基于Braak定义区域的标准化摄取值比（SUVR）分析显示，真实与合成图像间高度一致（皮尔逊相关系数r = 0.78–0.88；组内相关系数ICC = 0.71–0.84），其中Braak V/VI区一致性最强（ICC = 0.838）。结果表明，MCR-VQGAN可作为传统tau PET的可靠替代方案，提升tau生物标志物在研究与临床中的可及性。
原文摘要 · Abstract (English)
Tau positron emission tomography (PET) is a critical diagnostic modality for Alzheimer's disease (AD), but its widespread clinical adoption is hindered by radiation exposure, limited availability, high clinical workload, and substantial financial costs. To address these limitations, we propose the Multi-scale CBAM Residual Vector Quantized Generative Adversarial Network (MCR-VQGAN) to synthesize high-fidelity tau PET images from structural T1-weighted MRI. MCR-VQGAN advances the standard VQGAN architecture through three enhancements: multi-scale convolutions, ResNet blocks, and Convolutional Block Attention Modules (CBAM), which collectively improve the capture of local and global features. Using 222 paired T1-weighted MRI and tau PET scans from the ADNI database, we trained and compared MCR-VQGAN against cGAN, WGAN-GP, CycleGAN, and baseline VQGAN. MCR-VQGAN achieved superior image synthesis performance across all metrics (MSE = 0.0056 +/- 0.0061, PSNR = 30.65 +/- 4.47 dB, SSIM = 0.9263 +/- 0.0469). A CNN-based AD classifier trained on real tau PET achieved comparable accuracy on real (63.64%) and synthetic (65.91%) images, indicating that diagnostically relevant features are preserved. Regional SUVR-equivalent analysis across Braak-defined ROIs further indicated strong agreement between real and synthetic tau PET (Pearson r = 0.78-0.88; ICC = 0.71-0.84), with the strongest agreement in Braak V/VI (ICC = 0.838). Together, these results suggest that MCR-VQGAN offers a promising and scalable surrogate for conventional tau PET imaging, potentially improving the accessibility of tau biomarkers for AD research and clinical workflows.
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