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。

INFusion: 基于扩散正则化的隐式神经表示用于二维和三维加速 MRI 重建

INFusion: Diffusion Regularized Implicit Neural Representations for 2D  and 3D accelerated MRI reconstruction

Text-to-image (T2I) customization aims to create images that embody specific
visual concepts delineated in textual descriptions. However, existing works
still face a main challenge, concept overfitting. To tackle this challenge, we
first analyze overfitting, categorizing it into concept-agnostic overfitting,
which undermines non-customized concept knowledge, and concept-specific
overfitting, which is confined to customize on limited modalities, i.e,
backgrounds, layouts, styles. To evaluate the overfitting degree, we further
introduce two metrics, i.e, Latent Fisher divergence and Wasserstein metric to
measure the distribution changes of non-customized and customized concept
respectively. Drawing from the analysis, we propose Infusion, a T2I
customization method that enables the learning of target concepts to avoid
being constrained by limited training modalities, while preserving
non-customized knowledge. Remarkably, Infusion achieves this feat with
remarkable efficiency, requiring a mere 11KB of trained parameters. Extensive
experiments also demonstrate that our approach outperforms state-of-the-art
methods in both single and multi-concept customized generation.

通过分析过拟合问题，我们提出了一种名为 Infusion 的 T2I 个性化方法，使学习目标概念不受训练模态的限制，同时保留非个性化知识，且仅需要 11KB 的训练参数。大量实验证明，我们的方法在单概念和多概念个性化生成方面都优于现有技术。