mlsanityData to 14 Oct 2026

MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer's Disease Imaging

arXiv 2512.15947 ↗First posted 17 Dec 2025eess.IV cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 26 of its 83 references that are in the map. First posted on arXiv 17 Dec 2025.

Builds on

26 of 83 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  3. 2022Generative Adversarial Networkscited by 5,627
  4. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  6. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
  7. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  8. 2017Improved Training of Wasserstein GANscited by 2,235
  9. 2016Least Squares Generative Adversarial Networkscited by 1,001
  10. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  11. 2018Which Training Methods for GANs do actually Converge?cited by 679
  12. 2022Brain Imaging Generation with Latent Diffusion Modelscited by 268
14 more
  1. 2021Image-to-Image Translation: Methods and Applicationscited by 116
  2. 2017Deep MR to CT Synthesis using Unpaired Datacited by 86
  3. 2017A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpeningcited by 54
  4. 2018Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data sizecited by 42
  5. 2018Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGANcited by 31
  6. 2018Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNITcited by 18
  7. 2021MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Diseasecited by 16
  8. 2020GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesiscited by 15
  9. 2022Evaluating the Performance of StyleGAN2-ADA on Medical Imagescited by 11
  10. 2018Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRIcited by 10
  11. 2018SUSAN: Segment Unannotated image Structure using Adversarial Networkcited by 9
  12. 2018MRI Cross-Modality NeuroImage-to-NeuroImage Translationcited by 8
  13. 2023Denoising Diffusion Medical Modelscited by 6
  14. 2023Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networkscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Can Unified Generation and Understanding Models Maintain Semantic Equivalence Across Different Output Modalities?cited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025TauGenNet: Plasma-Driven Tau PET Image Synthesis via Text-Guided 3D Diffusion Models
  2. 2026Precision Synthesis of Multi-Tracer PET via VLM-Modulated Rectified Flow for Stratifying Mild Cognitive Impairment
  3. 2023TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models
  4. 2024Tau Anomaly Detection in PET Imaging via Bilateral-Guided Deterministic Diffusion Model
  5. 2026RelA-Diffusion: Relativistic Adversarial Diffusion for Multi-Tracer PET Synthesis from Multi-Sequence MRI
  6. 2024MCAD: Multi-modal Conditioned Adversarial Diffusion Model for High-Quality PET Image Reconstruction
  7. 2026Structural MRI Synthesis for Alzheimer's Disease via Conditional Diffusion on Anatomical Masks
  8. 2023Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection