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compressed matlab structure file  (MathWorks Inc)


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    MathWorks Inc compressed matlab structure file
    Compressed Matlab Structure File, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/compressed matlab structure file/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    compressed matlab structure file - by Bioz Stars, 2026-03
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    MathWorks Inc geometric coil compression matlab code
    Computational scaling with respect to image size for CG and HSS based reconstruction methods, see Figure 1 for algorithm flow-diagrams. R = 3 acceleration is applied to the T2 weighted images. A 10−6 tolerance is assumed for all algorithms to ensure consistent final image error. All methods include 5 iterations of Split Bregman with a TV weighting β = 3 · 10−3 and soft-thresholding ε = 2 · 10−1. The Jacobi pre-conditioner is used for all CG methods. The use of Cartesian optimized coil <t>compression</t> from 32 to 8-channels is explored for the Matrix Free method. The smallest and largest reconstruction times for HSS-Inverse are identified with arrows.
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    Computational scaling with respect to image size for CG and HSS based reconstruction methods, see Figure 1 for algorithm flow-diagrams. R = 3 acceleration is applied to the T2 weighted images. A 10−6 tolerance is assumed for all algorithms to ensure consistent final image error. All methods include 5 iterations of Split Bregman with a TV weighting β = 3 · 10−3 and soft-thresholding ε = 2 · 10−1. The Jacobi pre-conditioner is used for all CG methods. The use of Cartesian optimized coil compression from 32 to 8-channels is explored for the Matrix Free method. The smallest and largest reconstruction times for HSS-Inverse are identified with arrows.

    Journal: Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine

    Article Title: Fast Reconstruction for Multi-channel Compressed Sensing Using a Hierarchically Semiseparable Solver

    doi: 10.1002/mrm.25222

    Figure Lengend Snippet: Computational scaling with respect to image size for CG and HSS based reconstruction methods, see Figure 1 for algorithm flow-diagrams. R = 3 acceleration is applied to the T2 weighted images. A 10−6 tolerance is assumed for all algorithms to ensure consistent final image error. All methods include 5 iterations of Split Bregman with a TV weighting β = 3 · 10−3 and soft-thresholding ε = 2 · 10−1. The Jacobi pre-conditioner is used for all CG methods. The use of Cartesian optimized coil compression from 32 to 8-channels is explored for the Matrix Free method. The smallest and largest reconstruction times for HSS-Inverse are identified with arrows.

    Article Snippet: When investigating the impact of coil compression for CG based approaches, the Geometric Coil Compression MATLAB code associated with [ 11 ] was used.

    Techniques:

    Computational scaling of the HSS-Inverse method with respect to the number of parallel imaging channels and acceleration factor. A 10−6 tolerance is assumed for 5 iterations of Split Bregman with a TV weighting β = 3 · 10−3 and soft-thresholding ε = 2 · 10−1. Cartesian optimized coil compression is used to reduce from 32 to 8-channels. R = 2, 3, and 4 under-sampling is examined.

    Journal: Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine

    Article Title: Fast Reconstruction for Multi-channel Compressed Sensing Using a Hierarchically Semiseparable Solver

    doi: 10.1002/mrm.25222

    Figure Lengend Snippet: Computational scaling of the HSS-Inverse method with respect to the number of parallel imaging channels and acceleration factor. A 10−6 tolerance is assumed for 5 iterations of Split Bregman with a TV weighting β = 3 · 10−3 and soft-thresholding ε = 2 · 10−1. Cartesian optimized coil compression is used to reduce from 32 to 8-channels. R = 2, 3, and 4 under-sampling is examined.

    Article Snippet: When investigating the impact of coil compression for CG based approaches, the Geometric Coil Compression MATLAB code associated with [ 11 ] was used.

    Techniques: Imaging, Sampling