Journal article
Fast Low Rank Column-Wise Compressive Sensing for Accelerated Dynamic MRI
IEEE transactions on computational imaging, Vol.9, pp.409-424
01/01/2023
DOI: 10.1109/TCI.2023.3263810
Abstract
This work develops a novel set of algorithms, alternating Gradient Descent (GD) and minimization for MRI (altGDmin-MRI1 and altGDmin-MRI2), for accelerated dynamic MRI by assuming an approximate low-rank (LR) model on the matrix formed by the vectorized images of the sequence. The LR model itself is well-known in the MRI literature; our contribution is the novel GD-based algorithms which are much faster, memory-efficient, and ‘general’ compared with existing work; and careful use of a 3-level hierarchical LR model. By ‘general,’ we mean that, with a single choice of parameters, our method provides accurate reconstructions for multiple accelerated dynamic MRI applications, multiple sampling rates and sampling schemes. We show that our methods outperform many of the popular existing approaches while also being faster than all of them, on average. This claim is based on comparisons on 8 different retrospectively undersampled multi-coil dynamic MRI applications, sampled using either 1D Cartesian or 2D pseudo-radial undersampling, at multiple sampling rates. Evaluations on some prospectively undersampled datasets are also provided. Our second contribution is a mini-batch subspace tracking extension that can process new measurements and return reconstructions within a short delay after they arrive. The recovery algorithm itself is also faster than its batch counterpart.
Details
- Title: Subtitle
- Fast Low Rank Column-Wise Compressive Sensing for Accelerated Dynamic MRI
- Creators
- Silpa Babu - Iowa State UniversitySajan Goud Lingala - University of IowaNamrata Vaswani - Iowa State University
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on computational imaging, Vol.9, pp.409-424
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- DOI
- 10.1109/TCI.2023.3263810
- ISSN
- 2573-0436
- eISSN
- 2333-9403
- Grant note
- name: NSF, award: CIF-2115200
- Language
- English
- Date published
- 01/01/2023
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984406144502771
Metrics
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