# MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net For Multi-Modal Alzheimer's Classification

arXiv: https://arxiv.org/abs/1807.10111 · HTML: https://mlsanity.com/paper/1807.10111/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

## Built on by (most cited first, 9 of 9)

- [MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2108.02160/) (2021)
- [Synthesizing Multi-Tracer PET Images for Alzheimer's Disease Patients using a 3D Unified Anatomy-aware Cyclic Adversarial Network](https://mlsanity.com/paper/2107.05491/) (2021)
- [Brain MRI-to-PET Synthesis using 3D Convolutional Attention Networks](https://mlsanity.com/paper/2211.12082/) (2022)
- [Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET](https://mlsanity.com/paper/2406.12632/) (2024)
- [U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images](https://mlsanity.com/paper/2206.08078/) (2022)
- [GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models](https://mlsanity.com/paper/2505.07364/) (2025)
- [Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization](https://mlsanity.com/paper/2402.01191/) (2024)
- [Synthesizing beta-amyloid PET images from T1-weighted Structural MRI: A Preliminary Study](https://mlsanity.com/paper/2409.18282/) (2024)
- [Score-based Generative Diffusion Models to Synthesize Full-dose FDG Brain PET from MRI in Epilepsy Patients](https://mlsanity.com/paper/2506.11297/) (2025)

## Abstract (arXiv)

Recent studies suggest that combined analysis of Magnetic resonance imaging~(MRI) that measures brain atrophy and positron emission tomography~(PET) that quantifies hypo-metabolism provides improved accuracy in diagnosing Alzheimer's disease. However, such techniques are limited by the availability of corresponding scans of each modality. Current work focuses on a cross-modal approach to estimate FDG-PET scans for the given MR scans using a 3D U-Net architecture. The use of the complete MR image instead of a local patch based approach helps in capturing non-local and non-linear correlations between MRI and PET modalities. The quality of the estimated PET scans is measured using quantitative metrics such as MAE, PSNR and SSIM. The efficacy of the proposed method is evaluated in the context of Alzheimer's disease classification. The accuracy using only MRI is 70.18% while joint classification using synthesized PET and MRI is 74.43% with a p-value of 0.06. The significant improvement in diagnosis demonstrates the utility of the synthesized PET scans for multi-modal analysis.