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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 has shown that INRs improve rapid MRI through inherent regularization imposed by neural network architectures. Typically parameterized by fully connected neural networks, INRs provide continuous image representations by mapping a physical coordinate location to its intensity. Prior approaches have applied unlearned regularization priors during INR training and were limited to 2D or low-resolution 3D acquisitions. Meanwhile, diffusion-based generative models have recently gained attention for learning powerful image priors independent of the measurement model. This work proposes INFusion, a technique that regularizes INR optimization from under-sampled MR measurements using pre-trained diffusion models to enhance reconstruction quality. In addition, a hybrid 3D approach is introduced, enabling INR application on large-scale 3D MR datasets. Experimental results show that in 2D settings, diffusion regularization improves INR training, while in 3D, it enables feasible INR training on matrix sizes of 256 × 256 × 80.
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Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models
Diffusion models are powerful, but they require a lot of time and data to train. We propose Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training time costs while improving data efficiency, which thus helps democratize diffusion model training to broader users. At the core of our innovations is a new conditional score function at the patch level, where the patch location in the original image is included as additional coordinate channels, while the patch size is randomized and diversified throughout training to encode the cross-region dependency at multiple scales. Sampling with our method is as easy as in the original diffusion model. Through Patch Diffusion, we could achieve ≥2× faster training, while maintaining comparable or better generation quality. Patch Diffusion meanwhile improves the performance of diffusion models trained on relatively small datasets, e.g., as few as 5,000 images to train from scratch. We achieve outstanding FID scores in line with state-of-the-art benchmarks: 1.77 on CelebA-64×64, 1.93 on AFHQv2-Wild-64×64, and 2.72 on ImageNet-256×256. We share our code and pre-trained models in GitHub.
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- Award ID(s):
- 2212418
- PAR ID:
- 10536735
- Publisher / Repository:
- Neural Information Processing Systems
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
