  Positron emission tomography (PET) is a well-established functional imaging
technique for diagnosing brain disorders. However, PET's high costs and
radiation exposure limit its widespread use. In contrast, magnetic resonance
imaging (MRI) does not have these limitations. Although it also captures
neurodegenerative changes, MRI is a less sensitive diagnostic tool than PET. To
close this gap, we aim to generate synthetic PET from MRI. Herewith, we
introduce PASTA, a novel pathology-aware image translation framework based on
conditional diffusion models. Compared to the state-of-the-art methods, PASTA
excels in preserving both structural and pathological details in the target
modality, which is achieved through its highly interactive dual-arm
architecture and multi-modal condition integration. A cycle exchange
consistency and volumetric generation strategy elevate PASTA's capability to
produce high-quality 3D PET scans. Our qualitative and quantitative results
confirm that the synthesized PET scans from PASTA not only reach the best
quantitative scores but also preserve the pathology correctly. For Alzheimer's
classification, the performance of synthesized scans improves over MRI by 4%,
almost reaching the performance of actual PET. Code is available at
https://github.com/ai-med/PASTA.
