A function approximation approach to the segmentation step in IMRT planning
Abstract In the segmentation step of intensity modulated radiation therapy planning, given intensity profiles have to be decomposed into a number of leaf positions of a multileaf collimator (MLC) such that the superposition of the corresponding field shapes is close to the desired profile. Until now...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kiesel, Antje [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2009 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2009 |
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| Übergeordnetes Werk: |
Enthalten in: OR spectrum - Berlin : Springer, 1979, 34(2009), 1 vom: 01. Dez., Seite 181-198 |
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| Übergeordnetes Werk: |
volume:34 ; year:2009 ; number:1 ; day:01 ; month:12 ; pages:181-198 |
| Links: |
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| DOI / URN: |
10.1007/s00291-009-0187-2 |
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| Katalog-ID: |
SPR003782859 |
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| 520 | |a Abstract In the segmentation step of intensity modulated radiation therapy planning, given intensity profiles have to be decomposed into a number of leaf positions of a multileaf collimator (MLC) such that the superposition of the corresponding field shapes is close to the desired profile. Until now, these decomposition problems have been formulated as discrete optimization problems where the profiles are nonnegative integer matrices. The segments are modeled as 0-1-matrices, 1 indicating that radiation is transmitted through this part of the field and 0 for the areas that are covered by the leaves of the MLC. But in physical reality, radiation has a penumbra at the boundary of the segment causing a decline of the intensity, that is not modeled in these formulations. This paper embeds the segmentation task into the wider context of function approximation and models both profiles and segments as real-valued functions of two variables. This leads to convex optimization problems whose objective is to minimize the approximation error between the profile and the superposition of the real weighted segments. Thus, a more realistic model of radiation is used and may enable an improvement in treatment quality. | ||
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| 650 | 4 | |a Continuous approximation |7 (dpeaa)DE-He213 | |
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10.1007/s00291-009-0187-2 doi (DE-627)SPR003782859 (SPR)s00291-009-0187-2-e DE-627 ger DE-627 rakwb eng Kiesel, Antje verfasserin aut A function approximation approach to the segmentation step in IMRT planning 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2009 Abstract In the segmentation step of intensity modulated radiation therapy planning, given intensity profiles have to be decomposed into a number of leaf positions of a multileaf collimator (MLC) such that the superposition of the corresponding field shapes is close to the desired profile. Until now, these decomposition problems have been formulated as discrete optimization problems where the profiles are nonnegative integer matrices. The segments are modeled as 0-1-matrices, 1 indicating that radiation is transmitted through this part of the field and 0 for the areas that are covered by the leaves of the MLC. But in physical reality, radiation has a penumbra at the boundary of the segment causing a decline of the intensity, that is not modeled in these formulations. This paper embeds the segmentation task into the wider context of function approximation and models both profiles and segments as real-valued functions of two variables. This leads to convex optimization problems whose objective is to minimize the approximation error between the profile and the superposition of the real weighted segments. Thus, a more realistic model of radiation is used and may enable an improvement in treatment quality. Multileaf collimator (dpeaa)DE-He213 IMRT planning (dpeaa)DE-He213 Intensity profile (dpeaa)DE-He213 Continuous approximation (dpeaa)DE-He213 Enthalten in OR spectrum Berlin : Springer, 1979 34(2009), 1 vom: 01. Dez., Seite 181-198 (DE-627)266018998 (DE-600)1467029-X 1436-6304 nnns volume:34 year:2009 number:1 day:01 month:12 pages:181-198 https://dx.doi.org/10.1007/s00291-009-0187-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2009 1 01 12 181-198 |
| spelling |
10.1007/s00291-009-0187-2 doi (DE-627)SPR003782859 (SPR)s00291-009-0187-2-e DE-627 ger DE-627 rakwb eng Kiesel, Antje verfasserin aut A function approximation approach to the segmentation step in IMRT planning 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2009 Abstract In the segmentation step of intensity modulated radiation therapy planning, given intensity profiles have to be decomposed into a number of leaf positions of a multileaf collimator (MLC) such that the superposition of the corresponding field shapes is close to the desired profile. Until now, these decomposition problems have been formulated as discrete optimization problems where the profiles are nonnegative integer matrices. The segments are modeled as 0-1-matrices, 1 indicating that radiation is transmitted through this part of the field and 0 for the areas that are covered by the leaves of the MLC. But in physical reality, radiation has a penumbra at the boundary of the segment causing a decline of the intensity, that is not modeled in these formulations. This paper embeds the segmentation task into the wider context of function approximation and models both profiles and segments as real-valued functions of two variables. This leads to convex optimization problems whose objective is to minimize the approximation error between the profile and the superposition of the real weighted segments. Thus, a more realistic model of radiation is used and may enable an improvement in treatment quality. Multileaf collimator (dpeaa)DE-He213 IMRT planning (dpeaa)DE-He213 Intensity profile (dpeaa)DE-He213 Continuous approximation (dpeaa)DE-He213 Enthalten in OR spectrum Berlin : Springer, 1979 34(2009), 1 vom: 01. Dez., Seite 181-198 (DE-627)266018998 (DE-600)1467029-X 1436-6304 nnns volume:34 year:2009 number:1 day:01 month:12 pages:181-198 https://dx.doi.org/10.1007/s00291-009-0187-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2009 1 01 12 181-198 |
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10.1007/s00291-009-0187-2 doi (DE-627)SPR003782859 (SPR)s00291-009-0187-2-e DE-627 ger DE-627 rakwb eng Kiesel, Antje verfasserin aut A function approximation approach to the segmentation step in IMRT planning 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2009 Abstract In the segmentation step of intensity modulated radiation therapy planning, given intensity profiles have to be decomposed into a number of leaf positions of a multileaf collimator (MLC) such that the superposition of the corresponding field shapes is close to the desired profile. Until now, these decomposition problems have been formulated as discrete optimization problems where the profiles are nonnegative integer matrices. The segments are modeled as 0-1-matrices, 1 indicating that radiation is transmitted through this part of the field and 0 for the areas that are covered by the leaves of the MLC. But in physical reality, radiation has a penumbra at the boundary of the segment causing a decline of the intensity, that is not modeled in these formulations. This paper embeds the segmentation task into the wider context of function approximation and models both profiles and segments as real-valued functions of two variables. This leads to convex optimization problems whose objective is to minimize the approximation error between the profile and the superposition of the real weighted segments. Thus, a more realistic model of radiation is used and may enable an improvement in treatment quality. Multileaf collimator (dpeaa)DE-He213 IMRT planning (dpeaa)DE-He213 Intensity profile (dpeaa)DE-He213 Continuous approximation (dpeaa)DE-He213 Enthalten in OR spectrum Berlin : Springer, 1979 34(2009), 1 vom: 01. Dez., Seite 181-198 (DE-627)266018998 (DE-600)1467029-X 1436-6304 nnns volume:34 year:2009 number:1 day:01 month:12 pages:181-198 https://dx.doi.org/10.1007/s00291-009-0187-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2009 1 01 12 181-198 |
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10.1007/s00291-009-0187-2 doi (DE-627)SPR003782859 (SPR)s00291-009-0187-2-e DE-627 ger DE-627 rakwb eng Kiesel, Antje verfasserin aut A function approximation approach to the segmentation step in IMRT planning 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2009 Abstract In the segmentation step of intensity modulated radiation therapy planning, given intensity profiles have to be decomposed into a number of leaf positions of a multileaf collimator (MLC) such that the superposition of the corresponding field shapes is close to the desired profile. Until now, these decomposition problems have been formulated as discrete optimization problems where the profiles are nonnegative integer matrices. The segments are modeled as 0-1-matrices, 1 indicating that radiation is transmitted through this part of the field and 0 for the areas that are covered by the leaves of the MLC. But in physical reality, radiation has a penumbra at the boundary of the segment causing a decline of the intensity, that is not modeled in these formulations. This paper embeds the segmentation task into the wider context of function approximation and models both profiles and segments as real-valued functions of two variables. This leads to convex optimization problems whose objective is to minimize the approximation error between the profile and the superposition of the real weighted segments. Thus, a more realistic model of radiation is used and may enable an improvement in treatment quality. Multileaf collimator (dpeaa)DE-He213 IMRT planning (dpeaa)DE-He213 Intensity profile (dpeaa)DE-He213 Continuous approximation (dpeaa)DE-He213 Enthalten in OR spectrum Berlin : Springer, 1979 34(2009), 1 vom: 01. Dez., Seite 181-198 (DE-627)266018998 (DE-600)1467029-X 1436-6304 nnns volume:34 year:2009 number:1 day:01 month:12 pages:181-198 https://dx.doi.org/10.1007/s00291-009-0187-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2009 1 01 12 181-198 |
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10.1007/s00291-009-0187-2 doi (DE-627)SPR003782859 (SPR)s00291-009-0187-2-e DE-627 ger DE-627 rakwb eng Kiesel, Antje verfasserin aut A function approximation approach to the segmentation step in IMRT planning 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2009 Abstract In the segmentation step of intensity modulated radiation therapy planning, given intensity profiles have to be decomposed into a number of leaf positions of a multileaf collimator (MLC) such that the superposition of the corresponding field shapes is close to the desired profile. Until now, these decomposition problems have been formulated as discrete optimization problems where the profiles are nonnegative integer matrices. The segments are modeled as 0-1-matrices, 1 indicating that radiation is transmitted through this part of the field and 0 for the areas that are covered by the leaves of the MLC. But in physical reality, radiation has a penumbra at the boundary of the segment causing a decline of the intensity, that is not modeled in these formulations. This paper embeds the segmentation task into the wider context of function approximation and models both profiles and segments as real-valued functions of two variables. This leads to convex optimization problems whose objective is to minimize the approximation error between the profile and the superposition of the real weighted segments. Thus, a more realistic model of radiation is used and may enable an improvement in treatment quality. Multileaf collimator (dpeaa)DE-He213 IMRT planning (dpeaa)DE-He213 Intensity profile (dpeaa)DE-He213 Continuous approximation (dpeaa)DE-He213 Enthalten in OR spectrum Berlin : Springer, 1979 34(2009), 1 vom: 01. Dez., Seite 181-198 (DE-627)266018998 (DE-600)1467029-X 1436-6304 nnns volume:34 year:2009 number:1 day:01 month:12 pages:181-198 https://dx.doi.org/10.1007/s00291-009-0187-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2009 1 01 12 181-198 |
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Kiesel, Antje |
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Kiesel, Antje misc Multileaf collimator misc IMRT planning misc Intensity profile misc Continuous approximation A function approximation approach to the segmentation step in IMRT planning |
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A function approximation approach to the segmentation step in IMRT planning Multileaf collimator (dpeaa)DE-He213 IMRT planning (dpeaa)DE-He213 Intensity profile (dpeaa)DE-He213 Continuous approximation (dpeaa)DE-He213 |
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A function approximation approach to the segmentation step in IMRT planning |
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function approximation approach to the segmentation step in imrt planning |
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A function approximation approach to the segmentation step in IMRT planning |
| abstract |
Abstract In the segmentation step of intensity modulated radiation therapy planning, given intensity profiles have to be decomposed into a number of leaf positions of a multileaf collimator (MLC) such that the superposition of the corresponding field shapes is close to the desired profile. Until now, these decomposition problems have been formulated as discrete optimization problems where the profiles are nonnegative integer matrices. The segments are modeled as 0-1-matrices, 1 indicating that radiation is transmitted through this part of the field and 0 for the areas that are covered by the leaves of the MLC. But in physical reality, radiation has a penumbra at the boundary of the segment causing a decline of the intensity, that is not modeled in these formulations. This paper embeds the segmentation task into the wider context of function approximation and models both profiles and segments as real-valued functions of two variables. This leads to convex optimization problems whose objective is to minimize the approximation error between the profile and the superposition of the real weighted segments. Thus, a more realistic model of radiation is used and may enable an improvement in treatment quality. © Springer-Verlag 2009 |
| abstractGer |
Abstract In the segmentation step of intensity modulated radiation therapy planning, given intensity profiles have to be decomposed into a number of leaf positions of a multileaf collimator (MLC) such that the superposition of the corresponding field shapes is close to the desired profile. Until now, these decomposition problems have been formulated as discrete optimization problems where the profiles are nonnegative integer matrices. The segments are modeled as 0-1-matrices, 1 indicating that radiation is transmitted through this part of the field and 0 for the areas that are covered by the leaves of the MLC. But in physical reality, radiation has a penumbra at the boundary of the segment causing a decline of the intensity, that is not modeled in these formulations. This paper embeds the segmentation task into the wider context of function approximation and models both profiles and segments as real-valued functions of two variables. This leads to convex optimization problems whose objective is to minimize the approximation error between the profile and the superposition of the real weighted segments. Thus, a more realistic model of radiation is used and may enable an improvement in treatment quality. © Springer-Verlag 2009 |
| abstract_unstemmed |
Abstract In the segmentation step of intensity modulated radiation therapy planning, given intensity profiles have to be decomposed into a number of leaf positions of a multileaf collimator (MLC) such that the superposition of the corresponding field shapes is close to the desired profile. Until now, these decomposition problems have been formulated as discrete optimization problems where the profiles are nonnegative integer matrices. The segments are modeled as 0-1-matrices, 1 indicating that radiation is transmitted through this part of the field and 0 for the areas that are covered by the leaves of the MLC. But in physical reality, radiation has a penumbra at the boundary of the segment causing a decline of the intensity, that is not modeled in these formulations. This paper embeds the segmentation task into the wider context of function approximation and models both profiles and segments as real-valued functions of two variables. This leads to convex optimization problems whose objective is to minimize the approximation error between the profile and the superposition of the real weighted segments. Thus, a more realistic model of radiation is used and may enable an improvement in treatment quality. © Springer-Verlag 2009 |
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A function approximation approach to the segmentation step in IMRT planning |
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