mlsanityData to 7 Oct 2026

Model Adaptation for Image Reconstruction using Generalized Stein's Unbiased Risk Estimator

arXiv 2102.00047 ↗First posted 29 Jan 2021cs.LG cs.CV eess.IVcited by 4

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Abstract (arXiv)

Deep learning image reconstruction algorithms often suffer from model mismatches when the acquisition scheme differs significantly from the forward model used during training. We introduce a Generalized Stein's Unbiased Risk Estimate (GSURE) loss metric to adapt the network to the measured k-space data and minimize model misfit impact. Unlike current methods that rely on the mean square error in kspace, the proposed metric accounts for noise in the measurements. This makes the approach less vulnerable to overfitting, thus offering improved reconstruction quality compared to schemes that rely on mean-square error. This approach may be useful to rapidly adapt pre-trained models to new acquisition settings (e.g., multi-site) and different contrasts than training data

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 18 references that are in the map. First posted on arXiv 29 Jan 2021.

Builds on

10 of 18 references.

References · 10

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017Deep Image Priorcited by 1,097
  3. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 384
  4. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 340
  5. 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 231
  6. 2016A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstructioncited by 87
  7. 2017Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Losscited by 82
  8. 2018Unsupervised Learning with Stein's Unbiased Risk Estimatorcited by 47
  9. 2019Sigma-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstructioncited by 7
  10. 2020High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networkscited by 2

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 0; 2025: 1.

4 papers.

  1. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
  2. 2021Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformerscited by 76
  3. 2022Federated Learning of Generative Image Priors for MRI Reconstructioncited by 33
  4. 2025Denoising via Repainting: an image denoising method using layer wise medical image repaintingcited by 0