mlsanityData to 7 Oct 2026

Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging

arXiv 2204.01979 ↗First posted 5 Apr 2022eess.IV cs.CVcited by 0

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Abstract (arXiv)

Parallel imaging is widely used in magnetic resonance imaging as an acceleration technology. Traditional linear reconstruction methods in parallel imaging often suffer from noise amplification. Recently, a non-linear robust artificial-neural-network for k-space interpolation (RAKI) exhibits superior noise resilience over other linear methods. However, RAKI performs poorly at high acceleration rates, and needs a large amount of autocalibration signals as the training samples. In order to tackle these issues, we propose a multi-weight method that implements multiple weighting matrices on the undersampled data, named as MW-RAKI. Enforcing multiple weighted matrices on the measurements can effectively reduce the influence of noise and increase the data constraints. Furthermore, we incorporate the strategy of multiple weighting matrixes into a residual version of RAKI, and form MW-rRAKI.Experimental compari-sons with the alternative methods demonstrated noticeably better reconstruction performances, particularly at high acceleration rates.

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References · 15

  1. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 384
  2. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 202
  3. 2017Deep learning for undersampled MRI reconstructioncited by 91
  4. 2021Zero-Shot Self-Supervised Learning for MRI Reconstructioncited by 53
  5. 2021Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancementcited by 17
  6. 2021Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularizationcited by 17
  7. 2017End-to-End Supervised Product Quantization for Image Search and Retrievalcited by 16
  8. 2020Homotopic Gradients of Generative Density Priors for MR Image Reconstructioncited by 14
  9. 2019IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRIcited by 13
  10. 2019Complex-valued neural networks for machine learning on non-stationary physical datacited by 7
  11. 2019Accelerated Coronary MRI with sRAKI: A Database-Free Self-Consistent Neural Network k-space Reconstruction for Arbitrary Undersamplingcited by 6
  12. 2020Deblurring for Spiral Real-Time MRI Using Convolutional Neural Networkscited by 6
3 more
  1. 2022Scan-specific Self-supervised Bayesian Deep Non-linear Inversion for Undersampled MRI Reconstructioncited by 4
  2. 2020Improved Supervised Training of Physics-Guided Deep Learning Image Reconstruction with Multi-Maskingcited by 3
  3. 2020High-dimensional Fast Convolutional Framework (HICU) for Calibrationless MRIcited by 1

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