# Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model arXiv: https://arxiv.org/abs/2305.03901 ยท HTML: https://mlsanity.com/paper/2305.03901/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 38 references that are in the map. - First posted on arXiv 6 May 2023. ## Builds on (most cited first, 20 of 20) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015) - [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022) - [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019) - [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013) - [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019) - [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018) - [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016) - [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018) - [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022) - [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019) - [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021) - [SegDiff: Image Segmentation with Diffusion Probabilistic Models](https://mlsanity.com/paper/2112.00390/) (2021) - [Generating Images with Sparse Representations](https://mlsanity.com/paper/2103.03841/) (2021) - [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019) - [LOGAN: Latent Optimisation for Generative Adversarial Networks](https://mlsanity.com/paper/1912.00953/) (2019) - [Brain PET Synthesis from MRI Using Joint Probability Distribution of Diffusion Model at Ultrahigh Fields](https://mlsanity.com/paper/2211.08901/) (2022) - [Brain MRI-to-PET Synthesis using 3D Convolutional Attention Networks](https://mlsanity.com/paper/2211.12082/) (2022) ## Built on by (most cited first, 14 of 14) - [3D Wavelet Latent Diffusion Model for Whole-Body MR-to-CT Modality Translation](https://mlsanity.com/paper/2507.11557/) (2025) - [Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET](https://mlsanity.com/paper/2406.12632/) (2024) - [Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model](https://mlsanity.com/paper/2403.18139/) (2024) - [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) - [RelA-Diffusion: Relativistic Adversarial Diffusion for Multi-Tracer PET Synthesis from Multi-Sequence MRI](https://mlsanity.com/paper/2602.21345/) (2026) - [Multiple Noises in Diffusion Model for Semi-Supervised Multi-Domain Translation](https://mlsanity.com/paper/2309.14394/) (2023) - [Predicting Hypoxia in Brain Tumors from Multiparametric MRI](https://mlsanity.com/paper/2401.14171/) (2024) - [Synthesizing beta-amyloid PET images from T1-weighted Structural MRI: A Preliminary Study](https://mlsanity.com/paper/2409.18282/) (2024) - [A Prior-Guided Joint Diffusion Model in Projection Domain for PET Tracer Conversion](https://mlsanity.com/paper/2506.16733/) (2025) - [Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction](https://mlsanity.com/paper/2509.00395/) (2025) - [Precision Synthesis of Multi-Tracer PET via VLM-Modulated Rectified Flow for Stratifying Mild Cognitive Impairment](https://mlsanity.com/paper/2604.11176/) (2026) - [A Dual Path Framework with Hotspot Guided Fusion for Three Dimensional CT to PET Synthesis in Head and Neck Cancer](https://mlsanity.com/paper/2607.21800/) (2026) - [Flow Matching-Based PET Image Reconstruction](https://mlsanity.com/paper/2608.20112/) (2026) - [Bridging Modalities on the Cortex: Surface-based MRI to PET Translation with a Diffusion Bridge](https://mlsanity.com/paper/2609.20147/) (2026) ## Abstract (arXiv) MRI and PET are crucial diagnostic tools for brain diseases, as they provide complementary information on brain structure and function. However, PET scanning is costly and involves radioactive exposure, resulting in a lack of PET. Moreover, simultaneous PET and MRI at ultra-high-field are currently hardly infeasible. Ultra-high-field imaging has unquestionably proven valuable in both clinical and academic settings, especially in the field of cognitive neuroimaging. These motivate us to propose a method for synthetic PET from high-filed MRI and ultra-high-field MRI. From a statistical perspective, the joint probability distribution (JPD) is the most direct and fundamental means of portraying the correlation between PET and MRI. This paper proposes a novel joint diffusion attention model which has the joint probability distribution and attention strategy, named JDAM. JDAM has a diffusion process and a sampling process. The diffusion process involves the gradual diffusion of PET to Gaussian noise by adding Gaussian noise, while MRI remains fixed. JPD of MRI and noise-added PET was learned in the diffusion process. The sampling process is a predictor-corrector. PET images were generated from MRI by JPD of MRI and noise-added PET. The predictor is a reverse diffusion process and the corrector is Langevin dynamics. Experimental results on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed method outperforms state-of-the-art CycleGAN for high-field MRI (3T MRI). Finally, synthetic PET images from the ultra-high-field (5T MRI and 7T MRI) be attempted, providing a possibility for ultra-high-field PET-MRI imaging.