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
this https URL

提出了一种基于条件扩散模型的新型病理感知图像转换框架 PASTA，通过高度互动的双臂架构和多模态条件集成，PASTA 在保留目标模态中的结构和病理细节方面优于现有方法，并采用循环交换一致性和体积生成策略提高了生成的高质量 3D PET 扫描能力，定性和定量结果证实，通过 PASTA 合成的 PET 扫描不仅达到最佳定量分数，而且正确保留了病理学。对阿尔茨海默病分类而言，合成扫描的性能比 MRI 提高了 4%，几乎达到实际 PET 的性能。