Benchmarking learned non-Cartesian k-space trajectories and reconstruction networks
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Abstract (arXiv)
We benchmark the current existing methods to jointly learn non-Cartesian k-space trajectory and reconstruction: PILOT, BJORK, and compare them with those obtained from the recently developed generalized hybrid learning (HybLearn) framework. We present the advantages of using projected gradient descent to enforce MR scanner hardware constraints as compared to using added penalties in the cost function. Further, we use the novel HybLearn scheme to jointly learn and compare our results through a retrospective study on fastMRI validation dataset.
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References · 7
- 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
- 2019Learning-based Optimization of the Under-sampling Pattern in MRIcited by 32
- 2021B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRIcited by 29
- 2019PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRIcited by 24
- 2021Hybrid learning of Non-Cartesian k-space trajectory and MR image reconstruction networkscited by 5
- 20203D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRIcited by 4
- 2021Learning the sampling density in 2D SPARKLING MRI acquisition for optimized image reconstructioncited by 3
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