Robust compartmental model fitting in direct emission tomography reconstruction
Abstract Dynamic tomography reconstructs a time activity curve (TAC) for every voxel assuming that the algebraic form of the function is known a priori. The algebraic form derived from the analysis of compartmental models depends nonlinearly on the nonnegative parameters to be determined. Direct met...
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| Autor*in: |
Szirmay-Kalos, László [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2021 |
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| Übergeordnetes Werk: |
Enthalten in: The visual computer - Springer Berlin Heidelberg, 1985, 38(2021), 2 vom: 06. Feb., Seite 655-668 |
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volume:38 ; year:2021 ; number:2 ; day:06 ; month:02 ; pages:655-668 |
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10.1007/s00371-020-02041-x |
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| 520 | |a Abstract Dynamic tomography reconstructs a time activity curve (TAC) for every voxel assuming that the algebraic form of the function is known a priori. The algebraic form derived from the analysis of compartmental models depends nonlinearly on the nonnegative parameters to be determined. Direct methods apply fitting in every iteration step. Because of the iterative nature of the maximum likelihood–expectation maximization (ML–EM) reconstruction, the fitting result of the previous step can serve as a good starting point in the current step; thus, after the first iteration we have a guess that is not far from the solution, which allows the use of gradient-based local optimization methods. However, finding good initial guesses for the first ML–EM iteration is a critical problem since gradient-based local optimization algorithms do not guarantee convergence to the global optimum if they are started at an inappropriate location. This paper examines the robust solution of the fitting problem both in the initial phase and during the ML–EM iteration. This solution is implemented on GPUs and is built into the 4D reconstruction module of the TeraTomo software. | ||
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| 700 | 1 | |a Magdics, Milán |4 aut | |
| 700 | 1 | |a Tóth, Balázs |4 aut | |
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10.1007/s00371-020-02041-x doi (DE-627)OLC2078094978 (DE-He213)s00371-020-02041-x-p DE-627 ger DE-627 rakwb eng 004 VZ Szirmay-Kalos, László verfasserin (orcid)0000-0002-8523-2315 aut Robust compartmental model fitting in direct emission tomography reconstruction 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Dynamic tomography reconstructs a time activity curve (TAC) for every voxel assuming that the algebraic form of the function is known a priori. The algebraic form derived from the analysis of compartmental models depends nonlinearly on the nonnegative parameters to be determined. Direct methods apply fitting in every iteration step. Because of the iterative nature of the maximum likelihood–expectation maximization (ML–EM) reconstruction, the fitting result of the previous step can serve as a good starting point in the current step; thus, after the first iteration we have a guess that is not far from the solution, which allows the use of gradient-based local optimization methods. However, finding good initial guesses for the first ML–EM iteration is a critical problem since gradient-based local optimization algorithms do not guarantee convergence to the global optimum if they are started at an inappropriate location. This paper examines the robust solution of the fitting problem both in the initial phase and during the ML–EM iteration. This solution is implemented on GPUs and is built into the 4D reconstruction module of the TeraTomo software. Position emission tomography ML–EM methods GPU Kacsó, Ágota aut Magdics, Milán aut Tóth, Balázs aut Enthalten in The visual computer Springer Berlin Heidelberg, 1985 38(2021), 2 vom: 06. Feb., Seite 655-668 (DE-627)12917985X (DE-600)52035-4 (DE-576)014455897 0178-2789 nnns volume:38 year:2021 number:2 day:06 month:02 pages:655-668 https://doi.org/10.1007/s00371-020-02041-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-GWK GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 38 2021 2 06 02 655-668 |
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10.1007/s00371-020-02041-x doi (DE-627)OLC2078094978 (DE-He213)s00371-020-02041-x-p DE-627 ger DE-627 rakwb eng 004 VZ Szirmay-Kalos, László verfasserin (orcid)0000-0002-8523-2315 aut Robust compartmental model fitting in direct emission tomography reconstruction 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Dynamic tomography reconstructs a time activity curve (TAC) for every voxel assuming that the algebraic form of the function is known a priori. The algebraic form derived from the analysis of compartmental models depends nonlinearly on the nonnegative parameters to be determined. Direct methods apply fitting in every iteration step. Because of the iterative nature of the maximum likelihood–expectation maximization (ML–EM) reconstruction, the fitting result of the previous step can serve as a good starting point in the current step; thus, after the first iteration we have a guess that is not far from the solution, which allows the use of gradient-based local optimization methods. However, finding good initial guesses for the first ML–EM iteration is a critical problem since gradient-based local optimization algorithms do not guarantee convergence to the global optimum if they are started at an inappropriate location. This paper examines the robust solution of the fitting problem both in the initial phase and during the ML–EM iteration. This solution is implemented on GPUs and is built into the 4D reconstruction module of the TeraTomo software. Position emission tomography ML–EM methods GPU Kacsó, Ágota aut Magdics, Milán aut Tóth, Balázs aut Enthalten in The visual computer Springer Berlin Heidelberg, 1985 38(2021), 2 vom: 06. Feb., Seite 655-668 (DE-627)12917985X (DE-600)52035-4 (DE-576)014455897 0178-2789 nnns volume:38 year:2021 number:2 day:06 month:02 pages:655-668 https://doi.org/10.1007/s00371-020-02041-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-GWK GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 38 2021 2 06 02 655-668 |
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10.1007/s00371-020-02041-x doi (DE-627)OLC2078094978 (DE-He213)s00371-020-02041-x-p DE-627 ger DE-627 rakwb eng 004 VZ Szirmay-Kalos, László verfasserin (orcid)0000-0002-8523-2315 aut Robust compartmental model fitting in direct emission tomography reconstruction 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Dynamic tomography reconstructs a time activity curve (TAC) for every voxel assuming that the algebraic form of the function is known a priori. The algebraic form derived from the analysis of compartmental models depends nonlinearly on the nonnegative parameters to be determined. Direct methods apply fitting in every iteration step. Because of the iterative nature of the maximum likelihood–expectation maximization (ML–EM) reconstruction, the fitting result of the previous step can serve as a good starting point in the current step; thus, after the first iteration we have a guess that is not far from the solution, which allows the use of gradient-based local optimization methods. However, finding good initial guesses for the first ML–EM iteration is a critical problem since gradient-based local optimization algorithms do not guarantee convergence to the global optimum if they are started at an inappropriate location. This paper examines the robust solution of the fitting problem both in the initial phase and during the ML–EM iteration. This solution is implemented on GPUs and is built into the 4D reconstruction module of the TeraTomo software. Position emission tomography ML–EM methods GPU Kacsó, Ágota aut Magdics, Milán aut Tóth, Balázs aut Enthalten in The visual computer Springer Berlin Heidelberg, 1985 38(2021), 2 vom: 06. Feb., Seite 655-668 (DE-627)12917985X (DE-600)52035-4 (DE-576)014455897 0178-2789 nnns volume:38 year:2021 number:2 day:06 month:02 pages:655-668 https://doi.org/10.1007/s00371-020-02041-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-GWK GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 38 2021 2 06 02 655-668 |
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10.1007/s00371-020-02041-x doi (DE-627)OLC2078094978 (DE-He213)s00371-020-02041-x-p DE-627 ger DE-627 rakwb eng 004 VZ Szirmay-Kalos, László verfasserin (orcid)0000-0002-8523-2315 aut Robust compartmental model fitting in direct emission tomography reconstruction 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Dynamic tomography reconstructs a time activity curve (TAC) for every voxel assuming that the algebraic form of the function is known a priori. The algebraic form derived from the analysis of compartmental models depends nonlinearly on the nonnegative parameters to be determined. Direct methods apply fitting in every iteration step. Because of the iterative nature of the maximum likelihood–expectation maximization (ML–EM) reconstruction, the fitting result of the previous step can serve as a good starting point in the current step; thus, after the first iteration we have a guess that is not far from the solution, which allows the use of gradient-based local optimization methods. However, finding good initial guesses for the first ML–EM iteration is a critical problem since gradient-based local optimization algorithms do not guarantee convergence to the global optimum if they are started at an inappropriate location. This paper examines the robust solution of the fitting problem both in the initial phase and during the ML–EM iteration. This solution is implemented on GPUs and is built into the 4D reconstruction module of the TeraTomo software. Position emission tomography ML–EM methods GPU Kacsó, Ágota aut Magdics, Milán aut Tóth, Balázs aut Enthalten in The visual computer Springer Berlin Heidelberg, 1985 38(2021), 2 vom: 06. Feb., Seite 655-668 (DE-627)12917985X (DE-600)52035-4 (DE-576)014455897 0178-2789 nnns volume:38 year:2021 number:2 day:06 month:02 pages:655-668 https://doi.org/10.1007/s00371-020-02041-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-GWK GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 38 2021 2 06 02 655-668 |
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10.1007/s00371-020-02041-x doi (DE-627)OLC2078094978 (DE-He213)s00371-020-02041-x-p DE-627 ger DE-627 rakwb eng 004 VZ Szirmay-Kalos, László verfasserin (orcid)0000-0002-8523-2315 aut Robust compartmental model fitting in direct emission tomography reconstruction 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Dynamic tomography reconstructs a time activity curve (TAC) for every voxel assuming that the algebraic form of the function is known a priori. The algebraic form derived from the analysis of compartmental models depends nonlinearly on the nonnegative parameters to be determined. Direct methods apply fitting in every iteration step. Because of the iterative nature of the maximum likelihood–expectation maximization (ML–EM) reconstruction, the fitting result of the previous step can serve as a good starting point in the current step; thus, after the first iteration we have a guess that is not far from the solution, which allows the use of gradient-based local optimization methods. However, finding good initial guesses for the first ML–EM iteration is a critical problem since gradient-based local optimization algorithms do not guarantee convergence to the global optimum if they are started at an inappropriate location. This paper examines the robust solution of the fitting problem both in the initial phase and during the ML–EM iteration. This solution is implemented on GPUs and is built into the 4D reconstruction module of the TeraTomo software. Position emission tomography ML–EM methods GPU Kacsó, Ágota aut Magdics, Milán aut Tóth, Balázs aut Enthalten in The visual computer Springer Berlin Heidelberg, 1985 38(2021), 2 vom: 06. Feb., Seite 655-668 (DE-627)12917985X (DE-600)52035-4 (DE-576)014455897 0178-2789 nnns volume:38 year:2021 number:2 day:06 month:02 pages:655-668 https://doi.org/10.1007/s00371-020-02041-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-GWK GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 38 2021 2 06 02 655-668 |
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Abstract Dynamic tomography reconstructs a time activity curve (TAC) for every voxel assuming that the algebraic form of the function is known a priori. The algebraic form derived from the analysis of compartmental models depends nonlinearly on the nonnegative parameters to be determined. Direct methods apply fitting in every iteration step. Because of the iterative nature of the maximum likelihood–expectation maximization (ML–EM) reconstruction, the fitting result of the previous step can serve as a good starting point in the current step; thus, after the first iteration we have a guess that is not far from the solution, which allows the use of gradient-based local optimization methods. However, finding good initial guesses for the first ML–EM iteration is a critical problem since gradient-based local optimization algorithms do not guarantee convergence to the global optimum if they are started at an inappropriate location. This paper examines the robust solution of the fitting problem both in the initial phase and during the ML–EM iteration. This solution is implemented on GPUs and is built into the 4D reconstruction module of the TeraTomo software. © The Author(s) 2021 |
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Abstract Dynamic tomography reconstructs a time activity curve (TAC) for every voxel assuming that the algebraic form of the function is known a priori. The algebraic form derived from the analysis of compartmental models depends nonlinearly on the nonnegative parameters to be determined. Direct methods apply fitting in every iteration step. Because of the iterative nature of the maximum likelihood–expectation maximization (ML–EM) reconstruction, the fitting result of the previous step can serve as a good starting point in the current step; thus, after the first iteration we have a guess that is not far from the solution, which allows the use of gradient-based local optimization methods. However, finding good initial guesses for the first ML–EM iteration is a critical problem since gradient-based local optimization algorithms do not guarantee convergence to the global optimum if they are started at an inappropriate location. This paper examines the robust solution of the fitting problem both in the initial phase and during the ML–EM iteration. This solution is implemented on GPUs and is built into the 4D reconstruction module of the TeraTomo software. © The Author(s) 2021 |
| abstract_unstemmed |
Abstract Dynamic tomography reconstructs a time activity curve (TAC) for every voxel assuming that the algebraic form of the function is known a priori. The algebraic form derived from the analysis of compartmental models depends nonlinearly on the nonnegative parameters to be determined. Direct methods apply fitting in every iteration step. Because of the iterative nature of the maximum likelihood–expectation maximization (ML–EM) reconstruction, the fitting result of the previous step can serve as a good starting point in the current step; thus, after the first iteration we have a guess that is not far from the solution, which allows the use of gradient-based local optimization methods. However, finding good initial guesses for the first ML–EM iteration is a critical problem since gradient-based local optimization algorithms do not guarantee convergence to the global optimum if they are started at an inappropriate location. This paper examines the robust solution of the fitting problem both in the initial phase and during the ML–EM iteration. This solution is implemented on GPUs and is built into the 4D reconstruction module of the TeraTomo software. © The Author(s) 2021 |
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Robust compartmental model fitting in direct emission tomography reconstruction |
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Kacsó, Ágota Magdics, Milán Tóth, Balázs |
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Kacsó, Ágota Magdics, Milán Tóth, Balázs |
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