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Reducing the Complexity of Model-Based MRI Reconstructions via Sparsification.
IEEE Transactions on Medical Imaging
|May 17, 2021
Summary
This study introduces a new framework to speed up model-based MRI reconstruction. By simplifying the forward model, it reduces computational costs for faster, more efficient magnetic resonance imaging (MRI).
Area of Science:
- Medical Imaging
- Computational Science
Background:
- Classical Fourier-based MRI methods have limitations in handling field inhomogeneities and non-Cartesian sampling.
- Model-based reconstruction offers advantages but often incurs high computational costs due to complex forward models.
Purpose of the Study:
- To develop an algorithmic framework to reduce the computational burden of model-based MRI reconstruction.
- To enable faster and more efficient model-based MRI reconstruction without compromising accuracy.
Main Methods:
- Introduced a novel algorithmic framework for model-based MRI reconstruction.
- Employed strategic sparsification of forward operators to create computationally efficient approximations.
- Applied these approximations to iterative first-order reconstruction methods.
Main Results:
- Demonstrated a significant reduction in computational complexity for model-based MRI reconstruction.
- Validated the approach using both synthetic and experimental magnetic resonance imaging data.
- Showcased the viability and efficiency of the proposed sparsification technique.
Conclusions:
- The developed framework effectively reduces computational costs in model-based MRI reconstruction.
- Sparsification of forward operators is a viable strategy for accelerating these complex imaging tasks.
- This approach enhances the practical applicability of advanced MRI reconstruction techniques.
