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Learning-based method for k-space trajectory design in MRI.

Shubham Sharma, K V S Hari, Geert Leus

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary

    This study introduces novel data-driven methods, greedy non-Cartesian (GNC) and stochastic greedy non-Cartesian (SGNC), for designing MRI k-space sampling trajectories. These methods significantly improve image reconstruction quality compared to existing approaches.

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    Area of Science:

    • Medical Imaging
    • Magnetic Resonance Imaging (MRI)
    • Computational Imaging

    Background:

    • Variable density sampling in k-space is crucial for MRI trajectory design.
    • Data-driven trajectory methods offer superior image reconstruction compared to fixed or parametric approaches.

    Purpose of the Study:

    • To propose a data-driven strategy for designing non-Cartesian continuous k-space sampling trajectories in MRI within a compressed sensing framework.
    • To introduce a computationally efficient stochastic version of the proposed algorithm.

    Main Methods:

    • Development of the greedy non-Cartesian (GNC) algorithm for data-driven trajectory design.
    • Introduction of the stochastic greedy non-Cartesian (SGNC) algorithm to reduce computation time.
    • Comparison with traveling salesman problem (TSP)-based and stochastic greedy-Cartesian (SGC) trajectories using fastMRI knee dataset.

    Main Results:

    • The proposed GNC and SGNC algorithms demonstrate superior performance over TSP-based and SGC trajectories.
    • Improved image reconstruction quality was observed for the proposed data-driven trajectories.
    • The SGNC algorithm offers reduced computation time while maintaining high performance.

    Conclusions:

    • Data-driven k-space trajectory design, particularly with GNC and SGNC methods, enhances MRI image reconstruction.
    • The proposed algorithms provide a more effective approach for MRI trajectory design compared to conventional methods.
    • These findings have implications for accelerating MRI acquisition and improving diagnostic accuracy.