Abstract：
Diffusion models have garnered great interest lately in Magnetic Resonance (MR) image reconstruction. A key component of generating high-quality samples from noise is iterative denoising for thousands of steps. However, the complexity of inference steps has limited its applications. To solve the challenge in obtaining high-quality reconstructed images with fewer inference steps and computational complexity, we introduce a novel straight flow matching, based on a neural ordinary differential equation (ODE) generative model. Our model creates a linear path between undersampled images and reconstructed images, which can be accurately simulated with a few Euler steps. Furthermore, we propose a multi-modal straight flow matching model, which uses relatively easily available modalities as supplementary information to guide the reconstruction of target modalities. We introduce the low frequency fusion layer and the high frequency fusion layer into our multi-modal model, which has been proved to produce promising results in fusion tasks. The proposed multi-modal straight flow matching (MMSflow) achieves state-of-the-art performances in task of reconstruction in fastMRI and Brats-2020 and improves the sampling rate by an order of magnitude than other methods based on stochastic differential equations (SDE).
摘要：
扩散模型最近在磁共振（MR）图像重建中引起了极大的兴趣。从噪声中生成高质量样本的关键组成部分是数千步的迭代去噪。然而,推理步骤的复杂性限制了其应用。为了解决以更少的推理步骤和计算复杂度获得高质量重建图像的挑战，我们介绍了一种新颖的直流匹配，基于神经常微分方程(ODE)生成模型。我们的模型在欠采样图像和重建图像之间创建了一条线性路径，只需几个欧拉步骤就可以准确地模拟。此外，我们提出了一种多模态直线流匹配模型，它使用相对容易获得的模式作为补充信息来指导目标模式的重建。我们将低频融合层和高频融合层引入到我们的多模态模型中，这已被证明在融合任务中产生有希望的结果。所提出的多模态直流匹配（MMSflow）在fastMRI和Brats-2020中的重建任务中实现了最先进的性能，并且比基于随机微分方程的其他方法将采样率提高了一个数量级（SDE）。
