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

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.

Figures (9)

• Figure 1: For Alzheimer's disease, PET reveals distinctly reduced glucose uptake in the temporoparietal lobe (bottom circles), mirroring atrophy on MRI with higher sensitivity. Compared to the ground-truth (GT) PET, state-of-the-art diffusion models (SOTA DM) fail to recover such pathology in the synthesized (Syn) PET from MRI input. In contrast, PASTA shows improvement in preserving disease-specific pathology.
• Figure 2: PASTA features a symmetric dual-arm structure with a conditioner arm ($\phi_\omega$), a denoiser arm ($\mathbf{x}_\theta$), and adaptive conditional modules (AdaGN). Through AdaGN, PASTA conditions the feature maps $\boldsymbol{h}$ from $\mathbf{x}_\theta$ on timestep $t$, clinical data $\boldsymbol{c}$, and task representation $\boldsymbol{h}_m$ from $\phi_\omega$. It achieves high-quality 3D PET synthesis through a volumetric generation strategy.
• Figure 3: Cycle exchange consistency (CycleEx) strategy of PASTA. The two translation mappings $\boldsymbol{G}_p: \mathcal{M} \rightarrow \mathcal{P}$ and $\boldsymbol{G}_m: \mathcal{P} \rightarrow \mathcal{M}$ maintain cycle consistency. In addition, their network architectures are mirrored: both contain the same conditioner arm $\phi_\omega$ and denoiser arm $\mathbf{x}_\theta$, but with an exchanged position. This strategy ensures information sharing between the two arms.
• Figure 4: MetaROIs illustration on a brain MRI.
• Figure 5: Qualitative comparison of cross-modality synthesis methods. The first row shows images from a normal control subject with no obvious pathology, and the second row shows an AD patient with obvious hypometabolism in the temporoparietal lobe (bottom left and right parts). The left temporoparietal lobe is magnified in the third row. For the normal subject, most baselines recover the structure and metabolic information well; for the AD subject, PASTA demonstrates superior generation fidelity and pathology preservation.