ArticleIEEE transactions on medical imaging2024
Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion for Undersampled MRI Reconstruction.
Article in IEEE transactions on medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction.IEEE transactions on medical imaging · 2026Article
- Phase-Pole-Free Images and Smooth Coil Sensitivity Maps by Regularized Nonlinear Inversion.Magnetic resonance in medicine · 2026Article
- Enhancing computation speed and accuracy in deep image prior-based parameter mapping.Magnetic resonance in medicine · 2025Article
- Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion for Undersampled MRI Reconstruction.IEEE transactions on medical imaging · 2024Article
- A hybrid deep image prior and compressed sensing reconstruction method for highly accelerated 3D coronary magnetic resonance angiography.Frontiers in cardiovascular medicine · 2024Article
- Complexities of deep learning-based undersampled MR image reconstruction.European radiology experimental · 2023Review
- An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data.Magnetic resonance in medicine · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling. Recently, supervised deep learning has emerged as a promising technique for reconstructing sub-sampled MRI. However, supervised deep learning requires a large dataset of fully-sampled data. Although unsupervised or self-supervised deep learning methods have emerged to address the limitations of supervised deep learning approaches, they still require a database of images. In contrast, scan-specific deep learning methods learn and reconstruct using only the sub-sampled data from a single scan. Here, we introduce Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion (DNLINV) that does not require an auto calibration scan region. DNLINV utilizes a Deep Image Prior-type generative modeling approach and relies on approximate Bayesian inference to regularize the deep convolutional neural network. We demonstrate our approach on several anatomies, contrasts, and sampling patterns and show improved performance over existing approaches in scan-specific calibrationless parallel imaging and compressed sensing.
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