Implicit Neural Representations (INRs) are a learning-based approach to
accelerate Magnetic Resonance Imaging (MRI) acquisitions, particularly in
scan-specific settings when only data from the under-sampled scan itself are
available. Previous work demonstrates that INRs improve rapid MRI through
inherent regularization imposed by neural network architectures. Typically
parameterized by fully-connected neural networks, INRs support continuous image
representations by taking a physical coordinate location as input and
outputting the intensity at that coordinate. Previous work has applied
unlearned regularization priors during INR training and have been limited to 2D
or low-resolution 3D acquisitions. Meanwhile, diffusion based generative models
have received recent attention as they learn powerful image priors decoupled
from the measurement model. This work proposes INFusion, a technique that
regularizes the optimization of INRs from under-sampled MR measurements with
pre-trained diffusion models for improved image reconstruction. In addition, we
propose a hybrid 3D approach with our diffusion regularization that enables INR
application on large-scale 3D MR datasets. 2D experiments demonstrate improved
INR training with our proposed diffusion regularization, and 3D experiments
demonstrate feasibility of INR training with diffusion regularization on 3D
matrix sizes of 256 by 256 by 80.

INFusion 是一种技术，通过使用预训练的扩散模型，为基于学习的隐式神经表示（INRs）的优化提供正则化，以改善图像重建。2D 实验证明了使用我们提出的扩散正则化改进了 INR 训练的效果，而 3D 实验则证明了在 256x256x80 大小的 3D 矩阵上使用扩散正则化进行 INR 训练的可行性。