Abstract
We propose a novel approach to improve the reproducibility of neuroimaging results by converting statistic maps across different functional MRI pipelines. We make the assumption that pipelines can be considered as a style component of data and propose to use different generative models, among which, Diffusion Models (DM) to convert data between pipelines. We design a new DM-based unsupervised multi-domain image-to-image transition framework and constrain the generation of 3D fMRI statistic maps using the latent space of an auxiliary classifier that distinguishes statistic maps from different pipelines. We extend traditional sampling techniques used in DM to improve the transition performance. Our experiments demonstrate that our proposed methods are successful: pipelines can indeed be transferred, providing an important source of data augmentation for future medical studies.
Abstract (translated)
我们提出了一个新方法来提高神经影像结果的可重复性，通过将不同功能MRI流程中的统计图进行转换。我们假设流程可以被视为数据的一种样式组件，并提出使用不同的生成模型，其中扩散模型（DM）用于在流程之间转换数据。我们设计了一个基于扩散模型的多域图像到图像无监督转换框架，并通过辅助分类器的潜在空间对3D fMRI统计图进行生成限制。我们扩展了在DM中使用的传统采样技术，以提高转换性能。我们的实验结果表明，我们所提出的方法是成功的：流程确实可以转移，为未来的医学研究提供了重要的数据增强来源。
URL
https://arxiv.org/abs/2404.03703
