Translating MRI to PET through Conditional Diffusion Models with Enhanced Pathology Awareness

TL;DR

This paper introduces PASTA, a novel conditional diffusion model framework that generates high-quality synthetic PET images from MRI scans, enhancing pathology preservation and diagnostic accuracy for neurodegenerative diseases.

Contribution

PASTA is the first framework to integrate pathology awareness into MRI-to-PET translation using a dual-arm architecture and novel cycle exchange consistency, outperforming existing methods.

Findings

Synthesized PET images show high structural and pathological fidelity.

Performance in Alzheimer's diagnosis improves by 4% over MRI.

Almost matches the diagnostic accuracy of real PET scans.

Abstract

Positron emission tomography (PET) is a widely recognized technique for diagnosing neurodegenerative diseases, offering critical functional insights. However, its high costs and radiation exposure hinder its widespread use. In contrast, magnetic resonance imaging (MRI) does not involve such limitations. While MRI also detects neurodegenerative changes, it is less sensitive for diagnosis compared to PET. To overcome such limitations, one approach is to generate synthetic PET from MRI. Recent advances in generative models have paved the way for cross-modality medical image translation; however, existing methods largely emphasize structural preservation while neglecting the critical need for pathology awareness. To address this gap, we propose PASTA, a novel image translation framework built on conditional diffusion models with enhanced pathology awareness. PASTA surpasses state-of-the-art methods by preserving both structural and pathological details through its highly interactive dual-arm architecture and multi-modal condition integration. Additionally, we introduce a novel cycle exchange consistency and volumetric generation strategy that significantly enhances PASTA's ability to produce high-quality 3D PET images. Our qualitative and quantitative results demonstrate the high quality and pathology awareness of the synthesized PET scans. For Alzheimer's diagnosis, the performance of these synthesized scans improves over MRI by 4%, almost reaching the performance of actual PET. Our code is available at https://github.com/ai-med/PASTA.