Reconstructing high-fidelity magnetic resonance (MR) images from under-sampled k-space is a commonly used strategy to reduce scan time. The posterior sampling of diffusion models based on the real measurement data holds significant promise of improved reconstruction accuracy. However, traditional posterior sampling methods often lack effective data consistency guidance, leading to inaccurate and unstable reconstructions. Implicit neural representation (INR) has emerged as a powerful paradigm for solving inverse problems by modeling a signal’s attributes as a continuous function of spatial coordinates. In this study, we present a novel posterior sampler for diffusion models using INR, named DiffINR. The INR-based component incorporates both the diffusion prior distribution and the MRI physical model to ensure high data fidelity. DiffINR demonstrates superior performance on in-distribution datasets with remarkable accuracy, even under high acceleration factors (up to R = 12 in single-channel reconstruction). Furthermore, DiffINR exhibits excellent generalizability across various tissue contrasts and anatomical structures with low uncertainty. Overall, DiffINR significantly improves MRI reconstruction in terms of accuracy, generalizability and stability, paving the way for further accelerating MRI acquisition. Notably, our proposed framework can be a generalizable framework to solve inverse problems in other medical imaging tasks.

中文翻译：

---

通过内隐神经表示引导弥散模型的后验采样进行高度加速的 MRI


从采样不足的 k 空间重建高保真磁共振 （MR） 图像是缩短扫描时间的常用策略。基于真实测量数据的扩散模型后验采样有望提高重建精度。然而，传统的后验采样方法往往缺乏有效的数据一致性指导，导致重建不准确和不稳定。隐式神经表示 （INR） 已成为解决逆问题的一种强大范式，通过将信号的属性建模为空间坐标的连续函数。在这项研究中，我们提出了一种使用 INR 的扩散模型的新型后验采样器，名为 DiffINR。基于 INR 的组件结合了扩散先验分布和 MRI 物理模型，以确保高数据保真度。DiffINR 在分布内数据集上表现出卓越的性能，即使在高加速因子下（在单通道重建中高达 R = 12）也是如此。此外，DiffINR 在各种组织对比和解剖结构中表现出优异的泛化性，且不确定性低。总体而言，DiffINR 在准确性、泛化性和稳定性方面显着提高了 MRI 重建，为进一步加速 MRI 采集铺平了道路。值得注意的是，我们提出的框架可以是一个可推广的框架，用于解决其他医学成像任务中的逆问题。