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.

翻译：Tau正电子发射断层扫描（PET）是阿尔茨海默病（AD）的关键诊断方式，但其临床广泛应用受到辐射暴露、设备可及性有限、临床工作负荷高及财务成本高的制约。为解决这些局限，我们提出了多尺度CBAM残差向量量化生成对抗网络（MCR-VQGAN），用于从结构T1加权MRI合成高保真度Tau PET图像。MCR-VQGAN通过三项改进对标准VQGAN架构进行了升级：多尺度卷积、ResNet残差模块和卷积块注意力模块（CBAM），这些改进共同提升了局部与全局特征的捕捉能力。利用ADNI数据库中222对配对的T1加权MRI和Tau PET扫描数据，我们将MCR-VQGAN与cGAN、WGAN-GP、CycleGAN及基线VQGAN进行了训练与对比。MCR-VQGAN在所有评估指标上均达到更优的图像合成性能（MSE = 0.0056 ± 0.0061，PSNR = 30.65 ± 4.47 dB，SSIM = 0.9263 ± 0.0469）。基于真实Tau PET训练的CNN分类器在真实图像（63.64%）与合成图像（65.91%）上达到了可比的分类准确率，表明诊断相关特征得以保留。对Braak分区感兴趣区域（ROI）进行的区域标准化摄取值比值（SUVR）等效分析进一步表明，真实与合成Tau PET图像间具有高度一致性（Pearson r = 0.78–0.88，ICC = 0.71–0.84），其中Braak V/VI区域的一致性最强（ICC = 0.838）。综上，这些结果提示MCR-VQGAN为传统Tau PET成像提供了一种可行且可扩展的替代方案，有望提升AD研究与临床工作流中Tau生物标志物的可及性。