mlsanityData to 14 Oct 2026

CoilDrop-MRI: Self-supervised physics-guided MRI reconstruction with coil dropout

arXiv 2606.00100 ↗First posted 26 May 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Self-supervised deep learning-based methods have shown great promise for accelerated magnetic resonance imaging (MRI) reconstruction, achieving high image quality without requiring fully sampled data for training. These methods typically partition the acquired data into two disjoint subsets to construct input-target pairs for optimizing the reconstruction network. However, existing approaches perform this partition exclusively within the spatial frequency (k-space) domain, leaving the coil dimension unexplored. To enforce full exploitation of signal correlation across receiver coils, we propose CoilDrop-MRI, which applies coil-wise dropout to the input and uses the dropped data as training targets in a self-supervised framework. This method is integrated into unrolled architectures in both image-domain (SENSE) and k-space (SPIRiT) formulations. We further demonstrate its versatility by extending CoilDrop-MRI to multi-shot, phase-corrected diffusion MRI (dMRI) reconstruction. CoilDrop-MRI is extensively validated on multi-site, multi-field-strength (0.3T, 0.55T, and 3T), and multi-modality (T1-weighted, T2-weighted, T2-FLAIR, and dMRI) datasets and consistently outperforms state-of-the-art self-supervised methods, achieving quality comparable to supervised reconstruction methods without requiring fully sampled reference training data. Moreover, CoilDrop-MRI exhibits strong data efficiency and robust generalization across imaging conditions, establishing it as a practical and versatile framework for self-supervised parallel MRI reconstruction.

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References · 16

  1. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
  2. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 533
  3. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
  4. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 387
  5. 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 364
  6. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 342
  7. 2021Zero-Shot Self-Supervised Learning for MRI Reconstructioncited by 54
  8. 2023Self-Supervised MRI Reconstruction with Unrolled Diffusion Modelscited by 38
  9. 2022A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noisecited by 30
  10. 2020Patch2Self: Denoising Diffusion MRI with Self-Supervised Learningcited by 29
  11. 2021SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRIcited by 16
  12. 2023One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstructioncited by 15
4 more
  1. 2022Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDUcited by 10
  2. 2020CG-SENSE revisited: Results from the first ISMRM reproducibility challengecited by 7
  3. 2024Self-navigated 3D diffusion MRI using an optimized CAIPI sampling and structured low-rank reconstructioncited by 2
  4. 2023Wireless, Customizable Coaxially-shielded Coils for Magnetic Resonance Imagingcited by 1

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