基于扩散模型的路径学感知 MRI 到 PET 跨模态翻译框架 PASTA
摘要
正电导影 (PET) 是一种已确立的功能性成像技术，用于诊断脑障碍。然而，PET 的高成本和辐射暴露限制了其广泛应用。相比之下，磁共振成像 (MRI) 没有这些限制。尽管 MRI 也捕捉神经退行性变化，但 MRI 是比 PET 不够灵敏的诊断工具。为弥合这一差距，我们旨在生成合成 PET 图像。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.
引用
@article{arxiv.2405.16942,
title = {PASTA: Pathology-Aware MRI to PET Cross-Modal Translation with Diffusion Models},
author = {Yitong Li and Igor Yakushev and Dennis M. Hedderich and Christian Wachinger},
journal= {arXiv preprint arXiv:2405.16942},
year = {2024}
}