Multi-dimensional low rank plus sparse decomposition for reconstruction of under-sampled dynamic MRI
| Autor: | Jacob L. Jaremko, Dornoosh Zonoobi, Shahrooz Faghih Roohi, Ashraf A. Kassim |
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| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Optimization problem Rank (linear algebra) 02 engineering and technology Iterative reconstruction Sparse approximation Function (mathematics) 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Compressed sensing Artificial Intelligence Tensor (intrinsic definition) Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Representation (mathematics) Algorithm Software Mathematics |
| Zdroj: | Pattern Recognition. 63:667-679 |
| ISSN: | 0031-3203 |
| DOI: | 10.1016/j.patcog.2016.09.040 |
| Popis: | In this paper, we introduce a multi-dimensional approach to the problem of reconstruction of MR image sequences that are highly undersampled in k-space. By formulating the reconstruction as a high-order low rank tensor plus sparse tensor decomposition problem, we propose an efficient numerical algorithm based on the alternating direction method of multipliers (ADMM) to solve the optimization. Using Tucker representation, the sparse component is learnt efficiently with different sparsifying matrices along the modes of dynamic MR data. To estimate the low rank tensor, a convex cost function is defined to be the weighted sum of nuclear norms of its 3 unfolding matrices. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, compared to the state-of-the-art reconstruction methods. Graphical abstractDisplay Omitted HighlightsA novel multi-dimensional analysis model is learnt to recover higher quality MRI sequences.The dynamic MRI reconstruction is formulated as a higher-dimensional low rank plus sparse tensor reconstruction problem.An efficient numerical algorithm based on ADMM is proposed to solve the optimization problem. |
| Databáze: | OpenAIRE |
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