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geometric coil compression matlab code  (MathWorks Inc)
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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.
Geometric Coil Compression Matlab Code, 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
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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