Pseudo-T2 mapping for normalization of T2-weighted prostate MRI
Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creati...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sørland, Kaia Ingerdatter [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Magnetic resonance materials in physics, biology and medicine - Heidelberg : Springer, 1993, 35(2022), 4 vom: 12. Feb., Seite 573-585 |
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| Übergeordnetes Werk: |
volume:35 ; year:2022 ; number:4 ; day:12 ; month:02 ; pages:573-585 |
| Links: |
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| DOI / URN: |
10.1007/s10334-022-01003-9 |
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| Katalog-ID: |
SPR04780565X |
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| 100 | 1 | |a Sørland, Kaia Ingerdatter |e verfasserin |0 (orcid)0000-0001-9335-9968 |4 aut | |
| 245 | 1 | 0 | |a Pseudo-T2 mapping for normalization of T2-weighted prostate MRI |
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| 520 | |a Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle ($ AutoRef_{F} $), femoral head/muscle ($ AutoRef_{FH} $) and pelvic bone/muscle ($ AutoRef_{PB} $). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results $ AutoRef_{FH} $ pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 ($ AutoRef_{FH} $), 0.739 ($ AutoRef_{F} $) and 0.726 ($ AutoRef_{PB} $). Discussion All AutoRef versions reduced variation in the multicenter data. $ AutoRef_{FH} $ pseudo-T2s were closest to experimentally measured T2s. | ||
| 650 | 4 | |a Prostate |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Prostatic neoplasms |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Medical image processing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Magnetic resonance imaging |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multicenter study |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Sunoqrot, Mohammed R. S. |4 aut | |
| 700 | 1 | |a Sandsmark, Elise |4 aut | |
| 700 | 1 | |a Langørgen, Sverre |4 aut | |
| 700 | 1 | |a Bertilsson, Helena |4 aut | |
| 700 | 1 | |a Trimble, Christopher G. |4 aut | |
| 700 | 1 | |a Lin, Gigin |4 aut | |
| 700 | 1 | |a Selnæs, Kirsten M. |4 aut | |
| 700 | 1 | |a Goa, Pål E. |4 aut | |
| 700 | 1 | |a Bathen, Tone F. |4 aut | |
| 700 | 1 | |a Elschot, Mattijs |4 aut | |
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10.1007/s10334-022-01003-9 doi (DE-627)SPR04780565X (SPR)s10334-022-01003-9-e DE-627 ger DE-627 rakwb eng Sørland, Kaia Ingerdatter verfasserin (orcid)0000-0001-9335-9968 aut Pseudo-T2 mapping for normalization of T2-weighted prostate MRI 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle ($ AutoRef_{F} $), femoral head/muscle ($ AutoRef_{FH} $) and pelvic bone/muscle ($ AutoRef_{PB} $). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results $ AutoRef_{FH} $ pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 ($ AutoRef_{FH} $), 0.739 ($ AutoRef_{F} $) and 0.726 ($ AutoRef_{PB} $). Discussion All AutoRef versions reduced variation in the multicenter data. $ AutoRef_{FH} $ pseudo-T2s were closest to experimentally measured T2s. Prostate (dpeaa)DE-He213 Prostatic neoplasms (dpeaa)DE-He213 Medical image processing (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Multicenter study (dpeaa)DE-He213 Sunoqrot, Mohammed R. S. aut Sandsmark, Elise aut Langørgen, Sverre aut Bertilsson, Helena aut Trimble, Christopher G. aut Lin, Gigin aut Selnæs, Kirsten M. aut Goa, Pål E. aut Bathen, Tone F. aut Elschot, Mattijs aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 35(2022), 4 vom: 12. Feb., Seite 573-585 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:35 year:2022 number:4 day:12 month:02 pages:573-585 https://dx.doi.org/10.1007/s10334-022-01003-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_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_2118 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_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 35 2022 4 12 02 573-585 |
| spelling |
10.1007/s10334-022-01003-9 doi (DE-627)SPR04780565X (SPR)s10334-022-01003-9-e DE-627 ger DE-627 rakwb eng Sørland, Kaia Ingerdatter verfasserin (orcid)0000-0001-9335-9968 aut Pseudo-T2 mapping for normalization of T2-weighted prostate MRI 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle ($ AutoRef_{F} $), femoral head/muscle ($ AutoRef_{FH} $) and pelvic bone/muscle ($ AutoRef_{PB} $). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results $ AutoRef_{FH} $ pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 ($ AutoRef_{FH} $), 0.739 ($ AutoRef_{F} $) and 0.726 ($ AutoRef_{PB} $). Discussion All AutoRef versions reduced variation in the multicenter data. $ AutoRef_{FH} $ pseudo-T2s were closest to experimentally measured T2s. Prostate (dpeaa)DE-He213 Prostatic neoplasms (dpeaa)DE-He213 Medical image processing (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Multicenter study (dpeaa)DE-He213 Sunoqrot, Mohammed R. S. aut Sandsmark, Elise aut Langørgen, Sverre aut Bertilsson, Helena aut Trimble, Christopher G. aut Lin, Gigin aut Selnæs, Kirsten M. aut Goa, Pål E. aut Bathen, Tone F. aut Elschot, Mattijs aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 35(2022), 4 vom: 12. Feb., Seite 573-585 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:35 year:2022 number:4 day:12 month:02 pages:573-585 https://dx.doi.org/10.1007/s10334-022-01003-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_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_2118 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_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 35 2022 4 12 02 573-585 |
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10.1007/s10334-022-01003-9 doi (DE-627)SPR04780565X (SPR)s10334-022-01003-9-e DE-627 ger DE-627 rakwb eng Sørland, Kaia Ingerdatter verfasserin (orcid)0000-0001-9335-9968 aut Pseudo-T2 mapping for normalization of T2-weighted prostate MRI 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle ($ AutoRef_{F} $), femoral head/muscle ($ AutoRef_{FH} $) and pelvic bone/muscle ($ AutoRef_{PB} $). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results $ AutoRef_{FH} $ pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 ($ AutoRef_{FH} $), 0.739 ($ AutoRef_{F} $) and 0.726 ($ AutoRef_{PB} $). Discussion All AutoRef versions reduced variation in the multicenter data. $ AutoRef_{FH} $ pseudo-T2s were closest to experimentally measured T2s. Prostate (dpeaa)DE-He213 Prostatic neoplasms (dpeaa)DE-He213 Medical image processing (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Multicenter study (dpeaa)DE-He213 Sunoqrot, Mohammed R. S. aut Sandsmark, Elise aut Langørgen, Sverre aut Bertilsson, Helena aut Trimble, Christopher G. aut Lin, Gigin aut Selnæs, Kirsten M. aut Goa, Pål E. aut Bathen, Tone F. aut Elschot, Mattijs aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 35(2022), 4 vom: 12. Feb., Seite 573-585 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:35 year:2022 number:4 day:12 month:02 pages:573-585 https://dx.doi.org/10.1007/s10334-022-01003-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_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_2118 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_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 35 2022 4 12 02 573-585 |
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10.1007/s10334-022-01003-9 doi (DE-627)SPR04780565X (SPR)s10334-022-01003-9-e DE-627 ger DE-627 rakwb eng Sørland, Kaia Ingerdatter verfasserin (orcid)0000-0001-9335-9968 aut Pseudo-T2 mapping for normalization of T2-weighted prostate MRI 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle ($ AutoRef_{F} $), femoral head/muscle ($ AutoRef_{FH} $) and pelvic bone/muscle ($ AutoRef_{PB} $). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results $ AutoRef_{FH} $ pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 ($ AutoRef_{FH} $), 0.739 ($ AutoRef_{F} $) and 0.726 ($ AutoRef_{PB} $). Discussion All AutoRef versions reduced variation in the multicenter data. $ AutoRef_{FH} $ pseudo-T2s were closest to experimentally measured T2s. Prostate (dpeaa)DE-He213 Prostatic neoplasms (dpeaa)DE-He213 Medical image processing (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Multicenter study (dpeaa)DE-He213 Sunoqrot, Mohammed R. S. aut Sandsmark, Elise aut Langørgen, Sverre aut Bertilsson, Helena aut Trimble, Christopher G. aut Lin, Gigin aut Selnæs, Kirsten M. aut Goa, Pål E. aut Bathen, Tone F. aut Elschot, Mattijs aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 35(2022), 4 vom: 12. Feb., Seite 573-585 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:35 year:2022 number:4 day:12 month:02 pages:573-585 https://dx.doi.org/10.1007/s10334-022-01003-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_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_2118 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_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 35 2022 4 12 02 573-585 |
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10.1007/s10334-022-01003-9 doi (DE-627)SPR04780565X (SPR)s10334-022-01003-9-e DE-627 ger DE-627 rakwb eng Sørland, Kaia Ingerdatter verfasserin (orcid)0000-0001-9335-9968 aut Pseudo-T2 mapping for normalization of T2-weighted prostate MRI 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle ($ AutoRef_{F} $), femoral head/muscle ($ AutoRef_{FH} $) and pelvic bone/muscle ($ AutoRef_{PB} $). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results $ AutoRef_{FH} $ pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 ($ AutoRef_{FH} $), 0.739 ($ AutoRef_{F} $) and 0.726 ($ AutoRef_{PB} $). Discussion All AutoRef versions reduced variation in the multicenter data. $ AutoRef_{FH} $ pseudo-T2s were closest to experimentally measured T2s. Prostate (dpeaa)DE-He213 Prostatic neoplasms (dpeaa)DE-He213 Medical image processing (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Multicenter study (dpeaa)DE-He213 Sunoqrot, Mohammed R. S. aut Sandsmark, Elise aut Langørgen, Sverre aut Bertilsson, Helena aut Trimble, Christopher G. aut Lin, Gigin aut Selnæs, Kirsten M. aut Goa, Pål E. aut Bathen, Tone F. aut Elschot, Mattijs aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 35(2022), 4 vom: 12. Feb., Seite 573-585 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:35 year:2022 number:4 day:12 month:02 pages:573-585 https://dx.doi.org/10.1007/s10334-022-01003-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_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_2118 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_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 35 2022 4 12 02 573-585 |
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Enthalten in Magnetic resonance materials in physics, biology and medicine 35(2022), 4 vom: 12. Feb., Seite 573-585 volume:35 year:2022 number:4 day:12 month:02 pages:573-585 |
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Enthalten in Magnetic resonance materials in physics, biology and medicine 35(2022), 4 vom: 12. Feb., Seite 573-585 volume:35 year:2022 number:4 day:12 month:02 pages:573-585 |
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Prostate Prostatic neoplasms Medical image processing Magnetic resonance imaging Multicenter study |
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Sørland, Kaia Ingerdatter @@aut@@ Sunoqrot, Mohammed R. S. @@aut@@ Sandsmark, Elise @@aut@@ Langørgen, Sverre @@aut@@ Bertilsson, Helena @@aut@@ Trimble, Christopher G. @@aut@@ Lin, Gigin @@aut@@ Selnæs, Kirsten M. @@aut@@ Goa, Pål E. @@aut@@ Bathen, Tone F. @@aut@@ Elschot, Mattijs @@aut@@ |
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AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle ($ AutoRef_{F} $), femoral head/muscle ($ AutoRef_{FH} $) and pelvic bone/muscle ($ AutoRef_{PB} $). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results $ AutoRef_{FH} $ pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 ($ AutoRef_{FH} $), 0.739 ($ AutoRef_{F} $) and 0.726 ($ AutoRef_{PB} $). 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Sørland, Kaia Ingerdatter |
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Sørland, Kaia Ingerdatter misc Prostate misc Prostatic neoplasms misc Medical image processing misc Magnetic resonance imaging misc Multicenter study Pseudo-T2 mapping for normalization of T2-weighted prostate MRI |
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Pseudo-T2 mapping for normalization of T2-weighted prostate MRI Prostate (dpeaa)DE-He213 Prostatic neoplasms (dpeaa)DE-He213 Medical image processing (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Multicenter study (dpeaa)DE-He213 |
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misc Prostate misc Prostatic neoplasms misc Medical image processing misc Magnetic resonance imaging misc Multicenter study |
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Pseudo-T2 mapping for normalization of T2-weighted prostate MRI |
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Sørland, Kaia Ingerdatter |
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Magnetic resonance materials in physics, biology and medicine |
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Sørland, Kaia Ingerdatter Sunoqrot, Mohammed R. S. Sandsmark, Elise Langørgen, Sverre Bertilsson, Helena Trimble, Christopher G. Lin, Gigin Selnæs, Kirsten M. Goa, Pål E. Bathen, Tone F. Elschot, Mattijs |
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pseudo-t2 mapping for normalization of t2-weighted prostate mri |
| title_auth |
Pseudo-T2 mapping for normalization of T2-weighted prostate MRI |
| abstract |
Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle ($ AutoRef_{F} $), femoral head/muscle ($ AutoRef_{FH} $) and pelvic bone/muscle ($ AutoRef_{PB} $). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results $ AutoRef_{FH} $ pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 ($ AutoRef_{FH} $), 0.739 ($ AutoRef_{F} $) and 0.726 ($ AutoRef_{PB} $). Discussion All AutoRef versions reduced variation in the multicenter data. $ AutoRef_{FH} $ pseudo-T2s were closest to experimentally measured T2s. © The Author(s) 2022 |
| abstractGer |
Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle ($ AutoRef_{F} $), femoral head/muscle ($ AutoRef_{FH} $) and pelvic bone/muscle ($ AutoRef_{PB} $). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results $ AutoRef_{FH} $ pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 ($ AutoRef_{FH} $), 0.739 ($ AutoRef_{F} $) and 0.726 ($ AutoRef_{PB} $). Discussion All AutoRef versions reduced variation in the multicenter data. $ AutoRef_{FH} $ pseudo-T2s were closest to experimentally measured T2s. © The Author(s) 2022 |
| abstract_unstemmed |
Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle ($ AutoRef_{F} $), femoral head/muscle ($ AutoRef_{FH} $) and pelvic bone/muscle ($ AutoRef_{PB} $). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results $ AutoRef_{FH} $ pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 ($ AutoRef_{FH} $), 0.739 ($ AutoRef_{F} $) and 0.726 ($ AutoRef_{PB} $). Discussion All AutoRef versions reduced variation in the multicenter data. $ AutoRef_{FH} $ pseudo-T2s were closest to experimentally measured T2s. © The Author(s) 2022 |
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Pseudo-T2 mapping for normalization of T2-weighted prostate MRI |
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Sunoqrot, Mohammed R. S. Sandsmark, Elise Langørgen, Sverre Bertilsson, Helena Trimble, Christopher G. Lin, Gigin Selnæs, Kirsten M. Goa, Pål E. Bathen, Tone F. Elschot, Mattijs |
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Sunoqrot, Mohammed R. S. Sandsmark, Elise Langørgen, Sverre Bertilsson, Helena Trimble, Christopher G. Lin, Gigin Selnæs, Kirsten M. Goa, Pål E. Bathen, Tone F. Elschot, Mattijs |
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| score |
7.4020147 |