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Subspace Model-Assisted Deep Learning for Improved Image Reconstruction
- Source :
- IEEE Transactions on Medical Imaging; December 2023, Vol. 42 Issue: 12 p3833-3846, 14p
- Publication Year :
- 2023
-
Abstract
- Image reconstruction from limited and/or sparse data is known to be an ill-posed problem and a priori information/constraints have played an important role in solving the problem. Early constrained image reconstruction methods utilize image priors based on general image properties such as sparsity, low-rank structures, spatial support bound, etc. Recent deep learning-based reconstruction methods promise to produce even higher quality reconstructions by utilizing more specific image priors learned from training data. However, learning high-dimensional image priors requires huge amounts of training data that are currently not available in medical imaging applications. As a result, deep learning-based reconstructions often suffer from two known practical issues: a) sensitivity to data perturbations (e.g., changes in data sampling scheme), and b) limited generalization capability (e.g., biased reconstruction of lesions). This paper proposes a new method to address these issues. The proposed method synergistically integrates model-based and data-driven learning in three key components. The first component uses the linear vector space framework to capture global dependence of image features; the second exploits a deep network to learn the mapping from a linear vector space to a nonlinear manifold; the third is an unrolling-based deep network that captures local residual features with the aid of a sparsity model. The proposed method has been evaluated with magnetic resonance imaging data, demonstrating improved reconstruction in the presence of data perturbation and/or novel image features. The method may enhance the practical utility of deep learning-based image reconstruction.
Details
- Language :
- English
- ISSN :
- 02780062 and 1558254X
- Volume :
- 42
- Issue :
- 12
- Database :
- Supplemental Index
- Journal :
- IEEE Transactions on Medical Imaging
- Publication Type :
- Periodical
- Accession number :
- ejs64806907
- Full Text :
- https://doi.org/10.1109/TMI.2023.3313421