A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise

• URL: http://arxiv.org/abs/2205.10278v5
• Date: Tue, 18 Jul 2023 08:27:16 GMT
• Title: A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise
• Authors: Charles Millard, Mark Chiew
• Abstract summary: We use the Noisier2Noise framework to analytically explain the performance of Self-samplingd Learning via Data UnderSupervise. We propose partitioning the sampling set so that the subsets have the same type of distribution as the original sampling mask.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, there has been attention on leveraging the statistical modeling capabilities of neural networks for reconstructing sub-sampled Magnetic Resonance Imaging (MRI) data. Most proposed methods assume the existence of a representative fully-sampled dataset and use fully-supervised training. However, for many applications, fully sampled training data is not available, and may be highly impractical to acquire. The development and understanding of self-supervised methods, which use only sub-sampled data for training, are therefore highly desirable. This work extends the Noisier2Noise framework, which was originally constructed for self-supervised denoising tasks, to variable density sub-sampled MRI data. We use the Noisier2Noise framework to analytically explain the performance of Self-Supervised Learning via Data Undersampling (SSDU), a recently proposed method that performs well in practice but until now lacked theoretical justification. Further, we propose two modifications of SSDU that arise as a consequence of the theoretical developments. Firstly, we propose partitioning the sampling set so that the subsets have the same type of distribution as the original sampling mask. Secondly, we propose a loss weighting that compensates for the sampling and partitioning densities. On the fastMRI dataset we show that these changes significantly improve SSDU's image restoration quality and robustness to the partitioning parameters.

Related papers

• Improved Distribution Matching Distillation for Fast Image Synthesis [54.72356560597428]
  We introduce DMD2, a set of techniques that lift this limitation and improve DMD training. First, we eliminate the regression loss and the need for expensive dataset construction. Second, we integrate a GAN loss into the distillation procedure, discriminating between generated samples and real images.
  arXiv  Detail & Related papers  (2024-05-23T17:59:49Z)
• SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution [49.205865715776106]
  We propose the SAM-DiffSR model, which can utilize the fine-grained structure information from SAM in the process of sampling noise to improve the image quality without additional computational cost during inference. Experimental results demonstrate the effectiveness of our proposed method, showcasing superior performance in suppressing artifacts, and surpassing existing diffusion-based methods by 0.74 dB at the maximum in terms of PSNR on DIV2K dataset.
  arXiv  Detail & Related papers  (2024-02-27T01:57:02Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion. This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement. We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Stable Deep MRI Reconstruction using Generative Priors [13.400444194036101]
  We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. The results demonstrate competitive performance, on par with state-of-the-art end-to-end deep learning methods.
  arXiv  Detail & Related papers  (2022-10-25T08:34:29Z)
• Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction [18.264778497591603]
  This paper proposes a fully-sampled-data-free score-based diffusion model for MRI reconstruction. It learns the fully sampled MR image prior in a self-supervised manner on undersampled data. Experiments on the public dataset show that the proposed method outperforms existing self-supervised MRI reconstruction methods.
  arXiv  Detail & Related papers  (2022-09-02T06:21:42Z)
• PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI [47.24613772568027]
  Compressed Sensing MRI aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging. We propose a novel end-to-end Probabilistic Under-sampling and Explicable Reconstruction neTwork, dubbed PUERT, to jointly optimize the sampling pattern and the reconstruction network. Experiments on two widely used MRI datasets demonstrate that our proposed PUERT achieves state-of-the-art results in terms of both quantitative metrics and visual quality.
  arXiv  Detail & Related papers  (2022-04-24T04:23:57Z)
• Conditional Variational Autoencoder for Learned Image Reconstruction [5.487951901731039]
  We develop a novel framework that approximates the posterior distribution of the unknown image at each query observation. It handles implicit noise models and priors, it incorporates the data formation process (i.e., the forward operator), and the learned reconstructive properties are transferable between different datasets.
  arXiv  Detail & Related papers  (2021-10-22T10:02:48Z)
• Learning Energy-Based Models by Diffusion Recovery Likelihood [61.069760183331745]
  We present a diffusion recovery likelihood method to tractably learn and sample from a sequence of energy-based models. After training, synthesized images can be generated by the sampling process that initializes from Gaussian white noise distribution. On unconditional CIFAR-10 our method achieves FID 9.58 and inception score 8.30, superior to the majority of GANs.
  arXiv  Detail & Related papers  (2020-12-15T07:09:02Z)
• Multi-task MR Imaging with Iterative Teacher Forcing and Re-weighted Deep Learning [14.62432715967572]
  We develop a re-weighted multi-task deep learning method to learn prior knowledge from the existing big dataset. We then utilize them to assist simultaneous MR reconstruction and segmentation from the under-sampled k-space data. Results show that the proposed method possesses encouraging capabilities for simultaneous and accurate MR reconstruction and segmentation.
  arXiv  Detail & Related papers  (2020-11-27T09:08:05Z)
• Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning [70.57193072829288]
  Low-rank Multi-view Subspace Learning has shown great potential in cross-view classification in recent years. Existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously. We propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy.
  arXiv  Detail & Related papers  (2020-03-22T03:57:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.