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bloch function  (COMSOL Inc)

 
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    COMSOL Inc bloch function
    Bloch Function, supplied by COMSOL 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/bloch function/product/COMSOL Inc
    Average 90 stars, based on 1 article reviews
    bloch function - by Bioz Stars, 2026-04
    90/100 stars

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    Image Search Results


    (a) Original image and simulated sensitivity map, (b) comparison of pseudo-inverse and Bloch-LSQR solved B 1 -RRFC image reconstruction results, and (c) the corresponding deviation maps from original image (in |%|).

    Journal: Computational and Mathematical Methods in Medicine

    Article Title: Model for B 1 Imaging in MRI Using the Rotating RF Field

    doi: 10.1155/2014/461647

    Figure Lengend Snippet: (a) Original image and simulated sensitivity map, (b) comparison of pseudo-inverse and Bloch-LSQR solved B 1 -RRFC image reconstruction results, and (c) the corresponding deviation maps from original image (in |%|).

    Article Snippet: Instead of applying ( ) and the linear map equation , the nonlinear Bloch-LSQR solution was modeled by populating the matrix E whilst calling the Bloch function written in Matlab script, followed by the solution of ( ) by using the lsqr method in Matlab.

    Techniques: Comparison

    3D- B 1 -RRFC encoded results using Bloch-LSQR solver: (a) original 3D image, (b) normalized sinc (line) and sech (dotted line) RF pulses (absolute magnetic flux density in μ T over time), (c) 3D- B 1 -RRFC using sinc , and (d) 3D- B 1 -RRFC using sech RF pulse.

    Journal: Computational and Mathematical Methods in Medicine

    Article Title: Model for B 1 Imaging in MRI Using the Rotating RF Field

    doi: 10.1155/2014/461647

    Figure Lengend Snippet: 3D- B 1 -RRFC encoded results using Bloch-LSQR solver: (a) original 3D image, (b) normalized sinc (line) and sech (dotted line) RF pulses (absolute magnetic flux density in μ T over time), (c) 3D- B 1 -RRFC using sinc , and (d) 3D- B 1 -RRFC using sech RF pulse.

    Article Snippet: Instead of applying ( ) and the linear map equation , the nonlinear Bloch-LSQR solution was modeled by populating the matrix E whilst calling the Bloch function written in Matlab script, followed by the solution of ( ) by using the lsqr method in Matlab.

    Techniques:

    Performance evaluation of the pseudo-inverse and Bloch-LSQR method versus the strength of encoding B 1 + -gradient. The encoding system performs best (<5%) when the unit B 1 -gradient is at least 10 −9 T/m.

    Journal: Computational and Mathematical Methods in Medicine

    Article Title: Model for B 1 Imaging in MRI Using the Rotating RF Field

    doi: 10.1155/2014/461647

    Figure Lengend Snippet: Performance evaluation of the pseudo-inverse and Bloch-LSQR method versus the strength of encoding B 1 + -gradient. The encoding system performs best (<5%) when the unit B 1 -gradient is at least 10 −9 T/m.

    Article Snippet: Instead of applying ( ) and the linear map equation , the nonlinear Bloch-LSQR solution was modeled by populating the matrix E whilst calling the Bloch function written in Matlab script, followed by the solution of ( ) by using the lsqr method in Matlab.

    Techniques:

    Comparison of B 1 -RRFC image reconstruction results including the (normalized) original, pseudo-inverse, and Bloch-LSQR as well as the absolute value intensity deviation maps (from the original image) in |%|. The results are based on the experimentally acquired sensitivity map in . The image size is N × M = 100 × 100.

    Journal: Computational and Mathematical Methods in Medicine

    Article Title: Model for B 1 Imaging in MRI Using the Rotating RF Field

    doi: 10.1155/2014/461647

    Figure Lengend Snippet: Comparison of B 1 -RRFC image reconstruction results including the (normalized) original, pseudo-inverse, and Bloch-LSQR as well as the absolute value intensity deviation maps (from the original image) in |%|. The results are based on the experimentally acquired sensitivity map in . The image size is N × M = 100 × 100.

    Article Snippet: Instead of applying ( ) and the linear map equation , the nonlinear Bloch-LSQR solution was modeled by populating the matrix E whilst calling the Bloch function written in Matlab script, followed by the solution of ( ) by using the lsqr method in Matlab.

    Techniques: Comparison