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Translating MRI to PET through conditional diffusion models with enhanced pathology awareness
Yitong Li1, Igor Yakushev2, Dennis M Hedderich3
1Lab for Artificial Intelligence in Medical Imaging, Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM), Munich, Germany; Munich Center for Machine Learning (MCML), Munich, Germany.
Medical Image Analysis
|March 22, 2026
Summary
We developed PASTA, a new AI framework to create synthetic Positron Emission Tomography (PET) scans from Magnetic Resonance Imaging (MRI). This pathology-aware method improves neurodegenerative disease diagnosis using MRI alone.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neuroscience
Background:
- Positron Emission Tomography (PET) is crucial for diagnosing neurodegenerative diseases but is limited by cost and radiation.
- Magnetic Resonance Imaging (MRI) is safer and more accessible but less sensitive for detecting subtle pathological changes.
- Existing methods for generating synthetic PET from MRI often fail to capture crucial pathological details.
Purpose of the Study:
- To introduce PASTA, a novel framework for generating pathology-aware synthetic PET images from MRI.
- To enhance the diagnostic capabilities of MRI by improving its sensitivity to neurodegenerative pathologies.
- To overcome the limitations of current cross-modality medical image translation techniques.
Main Methods:
- Utilized conditional diffusion models for image translation with a focus on pathology awareness.
- Developed a dual-arm architecture and multi-modal condition integration for preserving structural and pathological details.
- Implemented a novel cycle exchange consistency and volumetric generation strategy for high-quality 3D PET synthesis.
Main Results:
- PASTA demonstrated superior performance over state-of-the-art methods in preserving both structural and pathological information.
- Synthesized PET scans achieved high qualitative and quantitative accuracy, exhibiting significant pathology awareness.
- For Alzheimer's disease diagnosis, synthesized PET scans improved performance by 4% compared to MRI alone, nearing actual PET performance.
Conclusions:
- PASTA effectively generates high-quality, pathology-aware synthetic PET images from MRI, addressing a critical gap in medical image translation.
- The framework holds significant potential for improving the diagnosis of neurodegenerative diseases by enhancing MRI's capabilities.
- PASTA offers a promising, cost-effective, and radiation-free alternative for functional brain imaging in clinical settings.
