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implementation of the matrix function solution of the bloch equations with relaxation terms  (MathWorks Inc)


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    MathWorks Inc implementation of the matrix function solution of the bloch equations with relaxation terms
    Implementation Of The Matrix Function Solution Of The Bloch Equations With Relaxation Terms, 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/product/bloch+function/pm29414063-46-25-13
    Average 90 stars, based on 1 article reviews
    implementation of the matrix function solution of the bloch equations with relaxation terms - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    Comparison:

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



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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