Abstract
Model-based reconstruction methods have emerged as a powerful alternative to classical Fourier-based MRI techniques, largely because of their ability to explicitly model (and therefore, potentially overcome) moderate field inhomogeneities, streamline reconstruction from non-Cartesian sampling, and even allow for the use of custom designed non-Fourier encoding methods. Their application in such scenarios, however, often comes with a substantial increase in computational cost, owing to the fact that the corresponding forward model in such settings no longer possesses a direct Fourier Transform based implementation. This paper introduces an algorithmic framework designed to reduce the computational burden associated with model-based MRI reconstruction tasks. The key innovation is the strategic sparsification of the corresponding forward operators for these models, giving rise to approximations of the forward models (and their adjoints) that admit low computational complexity application. This enables overall a reduced computational complexity application of popular iterative first-order reconstruction methods for these reconstruction tasks. Computational results obtained on both synthetic and experimental data illustrate the viability and efficiency of the approach.
Original language | English (US) |
---|---|
Article number | 9432799 |
Pages (from-to) | 2477-2486 |
Number of pages | 10 |
Journal | IEEE Transactions on Medical Imaging |
Volume | 40 |
Issue number | 9 |
DOIs | |
State | Published - Sep 2021 |
Bibliographical note
Publisher Copyright:© 1982-2012 IEEE.
Keywords
- MRI
- frequency-swept pulses
- model-based image reconstruction
- nonlinear field
- operator approximation
- sparsification