Quantification of Hypoxia in Human Glioblastoma using PET with 18F-FMISO
Purpose This study aimed to investigate the results of compartmental modeling (CM) and spectral analysis (SA) generated with dynamic 18F-FMISO tumor images. Besides, the regular tissue-to-blood ratio (TBR) images were derived and compared with the dynamic models. Methods Nine subjects with glioblast...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Abdo, Redha-alla [verfasserIn] Lamare, Frédéric [verfasserIn] Fernandez, Philippe [verfasserIn] Bentourkia, M’hamed [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© Korean Society of Nuclear Medicine 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Nuclear medicine and molecular imaging - Berlin : Springer, 2010, 55(2021), 3 vom: 25. März, Seite 107-115 |
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| Übergeordnetes Werk: |
volume:55 ; year:2021 ; number:3 ; day:25 ; month:03 ; pages:107-115 |
| Links: |
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| DOI / URN: |
10.1007/s13139-021-00693-8 |
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| Katalog-ID: |
SPR04410927X |
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| 520 | |a Purpose This study aimed to investigate the results of compartmental modeling (CM) and spectral analysis (SA) generated with dynamic 18F-FMISO tumor images. Besides, the regular tissue-to-blood ratio (TBR) images were derived and compared with the dynamic models. Methods Nine subjects with glioblastoma underwent PET/CT imaging with the 18F-FMISO tracer. The protocol for PET imaging began with 15 min in dynamic mode and two 10-min duration static images at 120 min and 180 min post-injection. We used the two-tissue compartmental model for CM at the voxel basis, and we conducted SA to estimate the 18F-FMISO accumulation within each voxel. We also investigated the usual tumor-to-blood ratio (TBR) for comparison. Results The images of the tumor showed different patterns of hypoxia and necrosis as a function of PET scanning times, while CM and SA methods based on dynamic PET imaging equally located tumor hypoxia. The mean correlation of $ K_{i} $ images of all subjects between CM and SA was 0.63 ± 0.19 (0.24-0.86). CM produced less noisy Ki images than SA, and, in the contrary, SA produced accumulation component images more clear than with CM. CM-Ki and SA-$ K_{i} $ images were correlated with TBR images (r = 0.72 ± 0.20 and 0.56 ± 0.26, respectively). In the only subject having a continuously increasing tumor time-activity curve, the k3 image showed a high uptake in the necrosis region which was not apparent in TBR or Ki images. Conclusion Based on these results, the combination of CM and SA approaches was found more appropriate in generating voxel-based hypoxia images. | ||
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| 650 | 4 | |a Hypoxia |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Compartmental modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Spectral analysis |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Lamare, Frédéric |e verfasserin |4 aut | |
| 700 | 1 | |a Fernandez, Philippe |e verfasserin |4 aut | |
| 700 | 1 | |a Bentourkia, M’hamed |e verfasserin |4 aut | |
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10.1007/s13139-021-00693-8 doi (DE-627)SPR04410927X (DE-599)SPRs13139-021-00693-8-e (SPR)s13139-021-00693-8-e DE-627 ger DE-627 rakwb eng 610 ASE Abdo, Redha-alla verfasserin aut Quantification of Hypoxia in Human Glioblastoma using PET with 18F-FMISO 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Nuclear Medicine 2021 Purpose This study aimed to investigate the results of compartmental modeling (CM) and spectral analysis (SA) generated with dynamic 18F-FMISO tumor images. Besides, the regular tissue-to-blood ratio (TBR) images were derived and compared with the dynamic models. Methods Nine subjects with glioblastoma underwent PET/CT imaging with the 18F-FMISO tracer. The protocol for PET imaging began with 15 min in dynamic mode and two 10-min duration static images at 120 min and 180 min post-injection. We used the two-tissue compartmental model for CM at the voxel basis, and we conducted SA to estimate the 18F-FMISO accumulation within each voxel. We also investigated the usual tumor-to-blood ratio (TBR) for comparison. Results The images of the tumor showed different patterns of hypoxia and necrosis as a function of PET scanning times, while CM and SA methods based on dynamic PET imaging equally located tumor hypoxia. The mean correlation of $ K_{i} $ images of all subjects between CM and SA was 0.63 ± 0.19 (0.24-0.86). CM produced less noisy Ki images than SA, and, in the contrary, SA produced accumulation component images more clear than with CM. CM-Ki and SA-$ K_{i} $ images were correlated with TBR images (r = 0.72 ± 0.20 and 0.56 ± 0.26, respectively). In the only subject having a continuously increasing tumor time-activity curve, the k3 image showed a high uptake in the necrosis region which was not apparent in TBR or Ki images. Conclusion Based on these results, the combination of CM and SA approaches was found more appropriate in generating voxel-based hypoxia images. FMISO (dpeaa)DE-He213 Hypoxia (dpeaa)DE-He213 Compartmental modeling (dpeaa)DE-He213 Spectral analysis (dpeaa)DE-He213 Lamare, Frédéric verfasserin aut Fernandez, Philippe verfasserin aut Bentourkia, M’hamed verfasserin aut Enthalten in Nuclear medicine and molecular imaging Berlin : Springer, 2010 55(2021), 3 vom: 25. März, Seite 107-115 (DE-627)620146869 (DE-600)2541855-5 1869-3482 nnns volume:55 year:2021 number:3 day:25 month:03 pages:107-115 https://dx.doi.org/10.1007/s13139-021-00693-8 lizenzpflichtig 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_65 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_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_4277 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 55 2021 3 25 03 107-115 |
| spelling |
10.1007/s13139-021-00693-8 doi (DE-627)SPR04410927X (DE-599)SPRs13139-021-00693-8-e (SPR)s13139-021-00693-8-e DE-627 ger DE-627 rakwb eng 610 ASE Abdo, Redha-alla verfasserin aut Quantification of Hypoxia in Human Glioblastoma using PET with 18F-FMISO 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Nuclear Medicine 2021 Purpose This study aimed to investigate the results of compartmental modeling (CM) and spectral analysis (SA) generated with dynamic 18F-FMISO tumor images. Besides, the regular tissue-to-blood ratio (TBR) images were derived and compared with the dynamic models. Methods Nine subjects with glioblastoma underwent PET/CT imaging with the 18F-FMISO tracer. The protocol for PET imaging began with 15 min in dynamic mode and two 10-min duration static images at 120 min and 180 min post-injection. We used the two-tissue compartmental model for CM at the voxel basis, and we conducted SA to estimate the 18F-FMISO accumulation within each voxel. We also investigated the usual tumor-to-blood ratio (TBR) for comparison. Results The images of the tumor showed different patterns of hypoxia and necrosis as a function of PET scanning times, while CM and SA methods based on dynamic PET imaging equally located tumor hypoxia. The mean correlation of $ K_{i} $ images of all subjects between CM and SA was 0.63 ± 0.19 (0.24-0.86). CM produced less noisy Ki images than SA, and, in the contrary, SA produced accumulation component images more clear than with CM. CM-Ki and SA-$ K_{i} $ images were correlated with TBR images (r = 0.72 ± 0.20 and 0.56 ± 0.26, respectively). In the only subject having a continuously increasing tumor time-activity curve, the k3 image showed a high uptake in the necrosis region which was not apparent in TBR or Ki images. Conclusion Based on these results, the combination of CM and SA approaches was found more appropriate in generating voxel-based hypoxia images. FMISO (dpeaa)DE-He213 Hypoxia (dpeaa)DE-He213 Compartmental modeling (dpeaa)DE-He213 Spectral analysis (dpeaa)DE-He213 Lamare, Frédéric verfasserin aut Fernandez, Philippe verfasserin aut Bentourkia, M’hamed verfasserin aut Enthalten in Nuclear medicine and molecular imaging Berlin : Springer, 2010 55(2021), 3 vom: 25. März, Seite 107-115 (DE-627)620146869 (DE-600)2541855-5 1869-3482 nnns volume:55 year:2021 number:3 day:25 month:03 pages:107-115 https://dx.doi.org/10.1007/s13139-021-00693-8 lizenzpflichtig 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_65 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_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_4277 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 55 2021 3 25 03 107-115 |
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10.1007/s13139-021-00693-8 doi (DE-627)SPR04410927X (DE-599)SPRs13139-021-00693-8-e (SPR)s13139-021-00693-8-e DE-627 ger DE-627 rakwb eng 610 ASE Abdo, Redha-alla verfasserin aut Quantification of Hypoxia in Human Glioblastoma using PET with 18F-FMISO 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Nuclear Medicine 2021 Purpose This study aimed to investigate the results of compartmental modeling (CM) and spectral analysis (SA) generated with dynamic 18F-FMISO tumor images. Besides, the regular tissue-to-blood ratio (TBR) images were derived and compared with the dynamic models. Methods Nine subjects with glioblastoma underwent PET/CT imaging with the 18F-FMISO tracer. The protocol for PET imaging began with 15 min in dynamic mode and two 10-min duration static images at 120 min and 180 min post-injection. We used the two-tissue compartmental model for CM at the voxel basis, and we conducted SA to estimate the 18F-FMISO accumulation within each voxel. We also investigated the usual tumor-to-blood ratio (TBR) for comparison. Results The images of the tumor showed different patterns of hypoxia and necrosis as a function of PET scanning times, while CM and SA methods based on dynamic PET imaging equally located tumor hypoxia. The mean correlation of $ K_{i} $ images of all subjects between CM and SA was 0.63 ± 0.19 (0.24-0.86). CM produced less noisy Ki images than SA, and, in the contrary, SA produced accumulation component images more clear than with CM. CM-Ki and SA-$ K_{i} $ images were correlated with TBR images (r = 0.72 ± 0.20 and 0.56 ± 0.26, respectively). In the only subject having a continuously increasing tumor time-activity curve, the k3 image showed a high uptake in the necrosis region which was not apparent in TBR or Ki images. Conclusion Based on these results, the combination of CM and SA approaches was found more appropriate in generating voxel-based hypoxia images. FMISO (dpeaa)DE-He213 Hypoxia (dpeaa)DE-He213 Compartmental modeling (dpeaa)DE-He213 Spectral analysis (dpeaa)DE-He213 Lamare, Frédéric verfasserin aut Fernandez, Philippe verfasserin aut Bentourkia, M’hamed verfasserin aut Enthalten in Nuclear medicine and molecular imaging Berlin : Springer, 2010 55(2021), 3 vom: 25. März, Seite 107-115 (DE-627)620146869 (DE-600)2541855-5 1869-3482 nnns volume:55 year:2021 number:3 day:25 month:03 pages:107-115 https://dx.doi.org/10.1007/s13139-021-00693-8 lizenzpflichtig 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_65 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_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_4277 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 55 2021 3 25 03 107-115 |
| allfieldsGer |
10.1007/s13139-021-00693-8 doi (DE-627)SPR04410927X (DE-599)SPRs13139-021-00693-8-e (SPR)s13139-021-00693-8-e DE-627 ger DE-627 rakwb eng 610 ASE Abdo, Redha-alla verfasserin aut Quantification of Hypoxia in Human Glioblastoma using PET with 18F-FMISO 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Nuclear Medicine 2021 Purpose This study aimed to investigate the results of compartmental modeling (CM) and spectral analysis (SA) generated with dynamic 18F-FMISO tumor images. Besides, the regular tissue-to-blood ratio (TBR) images were derived and compared with the dynamic models. Methods Nine subjects with glioblastoma underwent PET/CT imaging with the 18F-FMISO tracer. The protocol for PET imaging began with 15 min in dynamic mode and two 10-min duration static images at 120 min and 180 min post-injection. We used the two-tissue compartmental model for CM at the voxel basis, and we conducted SA to estimate the 18F-FMISO accumulation within each voxel. We also investigated the usual tumor-to-blood ratio (TBR) for comparison. Results The images of the tumor showed different patterns of hypoxia and necrosis as a function of PET scanning times, while CM and SA methods based on dynamic PET imaging equally located tumor hypoxia. The mean correlation of $ K_{i} $ images of all subjects between CM and SA was 0.63 ± 0.19 (0.24-0.86). CM produced less noisy Ki images than SA, and, in the contrary, SA produced accumulation component images more clear than with CM. CM-Ki and SA-$ K_{i} $ images were correlated with TBR images (r = 0.72 ± 0.20 and 0.56 ± 0.26, respectively). In the only subject having a continuously increasing tumor time-activity curve, the k3 image showed a high uptake in the necrosis region which was not apparent in TBR or Ki images. Conclusion Based on these results, the combination of CM and SA approaches was found more appropriate in generating voxel-based hypoxia images. FMISO (dpeaa)DE-He213 Hypoxia (dpeaa)DE-He213 Compartmental modeling (dpeaa)DE-He213 Spectral analysis (dpeaa)DE-He213 Lamare, Frédéric verfasserin aut Fernandez, Philippe verfasserin aut Bentourkia, M’hamed verfasserin aut Enthalten in Nuclear medicine and molecular imaging Berlin : Springer, 2010 55(2021), 3 vom: 25. März, Seite 107-115 (DE-627)620146869 (DE-600)2541855-5 1869-3482 nnns volume:55 year:2021 number:3 day:25 month:03 pages:107-115 https://dx.doi.org/10.1007/s13139-021-00693-8 lizenzpflichtig 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_65 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_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_4277 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 55 2021 3 25 03 107-115 |
| allfieldsSound |
10.1007/s13139-021-00693-8 doi (DE-627)SPR04410927X (DE-599)SPRs13139-021-00693-8-e (SPR)s13139-021-00693-8-e DE-627 ger DE-627 rakwb eng 610 ASE Abdo, Redha-alla verfasserin aut Quantification of Hypoxia in Human Glioblastoma using PET with 18F-FMISO 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Nuclear Medicine 2021 Purpose This study aimed to investigate the results of compartmental modeling (CM) and spectral analysis (SA) generated with dynamic 18F-FMISO tumor images. Besides, the regular tissue-to-blood ratio (TBR) images were derived and compared with the dynamic models. Methods Nine subjects with glioblastoma underwent PET/CT imaging with the 18F-FMISO tracer. The protocol for PET imaging began with 15 min in dynamic mode and two 10-min duration static images at 120 min and 180 min post-injection. We used the two-tissue compartmental model for CM at the voxel basis, and we conducted SA to estimate the 18F-FMISO accumulation within each voxel. We also investigated the usual tumor-to-blood ratio (TBR) for comparison. Results The images of the tumor showed different patterns of hypoxia and necrosis as a function of PET scanning times, while CM and SA methods based on dynamic PET imaging equally located tumor hypoxia. The mean correlation of $ K_{i} $ images of all subjects between CM and SA was 0.63 ± 0.19 (0.24-0.86). CM produced less noisy Ki images than SA, and, in the contrary, SA produced accumulation component images more clear than with CM. CM-Ki and SA-$ K_{i} $ images were correlated with TBR images (r = 0.72 ± 0.20 and 0.56 ± 0.26, respectively). In the only subject having a continuously increasing tumor time-activity curve, the k3 image showed a high uptake in the necrosis region which was not apparent in TBR or Ki images. Conclusion Based on these results, the combination of CM and SA approaches was found more appropriate in generating voxel-based hypoxia images. FMISO (dpeaa)DE-He213 Hypoxia (dpeaa)DE-He213 Compartmental modeling (dpeaa)DE-He213 Spectral analysis (dpeaa)DE-He213 Lamare, Frédéric verfasserin aut Fernandez, Philippe verfasserin aut Bentourkia, M’hamed verfasserin aut Enthalten in Nuclear medicine and molecular imaging Berlin : Springer, 2010 55(2021), 3 vom: 25. März, Seite 107-115 (DE-627)620146869 (DE-600)2541855-5 1869-3482 nnns volume:55 year:2021 number:3 day:25 month:03 pages:107-115 https://dx.doi.org/10.1007/s13139-021-00693-8 lizenzpflichtig 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_65 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_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_4277 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 55 2021 3 25 03 107-115 |
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English |
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Enthalten in Nuclear medicine and molecular imaging 55(2021), 3 vom: 25. März, Seite 107-115 volume:55 year:2021 number:3 day:25 month:03 pages:107-115 |
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Enthalten in Nuclear medicine and molecular imaging 55(2021), 3 vom: 25. März, Seite 107-115 volume:55 year:2021 number:3 day:25 month:03 pages:107-115 |
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FMISO Hypoxia Compartmental modeling Spectral analysis |
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Nuclear medicine and molecular imaging |
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Abdo, Redha-alla @@aut@@ Lamare, Frédéric @@aut@@ Fernandez, Philippe @@aut@@ Bentourkia, M’hamed @@aut@@ |
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2021-03-25T00:00:00Z |
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Besides, the regular tissue-to-blood ratio (TBR) images were derived and compared with the dynamic models. Methods Nine subjects with glioblastoma underwent PET/CT imaging with the 18F-FMISO tracer. The protocol for PET imaging began with 15 min in dynamic mode and two 10-min duration static images at 120 min and 180 min post-injection. We used the two-tissue compartmental model for CM at the voxel basis, and we conducted SA to estimate the 18F-FMISO accumulation within each voxel. We also investigated the usual tumor-to-blood ratio (TBR) for comparison. Results The images of the tumor showed different patterns of hypoxia and necrosis as a function of PET scanning times, while CM and SA methods based on dynamic PET imaging equally located tumor hypoxia. The mean correlation of $ K_{i} $ images of all subjects between CM and SA was 0.63 ± 0.19 (0.24-0.86). CM produced less noisy Ki images than SA, and, in the contrary, SA produced accumulation component images more clear than with CM. CM-Ki and SA-$ K_{i} $ images were correlated with TBR images (r = 0.72 ± 0.20 and 0.56 ± 0.26, respectively). In the only subject having a continuously increasing tumor time-activity curve, the k3 image showed a high uptake in the necrosis region which was not apparent in TBR or Ki images. 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|
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Abdo, Redha-alla |
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Abdo, Redha-alla ddc 610 misc FMISO misc Hypoxia misc Compartmental modeling misc Spectral analysis Quantification of Hypoxia in Human Glioblastoma using PET with 18F-FMISO |
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610 ASE Quantification of Hypoxia in Human Glioblastoma using PET with 18F-FMISO FMISO (dpeaa)DE-He213 Hypoxia (dpeaa)DE-He213 Compartmental modeling (dpeaa)DE-He213 Spectral analysis (dpeaa)DE-He213 |
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ddc 610 misc FMISO misc Hypoxia misc Compartmental modeling misc Spectral analysis |
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Quantification of Hypoxia in Human Glioblastoma using PET with 18F-FMISO |
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Quantification of Hypoxia in Human Glioblastoma using PET with 18F-FMISO |
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Abdo, Redha-alla |
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Nuclear medicine and molecular imaging |
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Abdo, Redha-alla Lamare, Frédéric Fernandez, Philippe Bentourkia, M’hamed |
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verfasserin |
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quantification of hypoxia in human glioblastoma using pet with 18f-fmiso |
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Quantification of Hypoxia in Human Glioblastoma using PET with 18F-FMISO |
| abstract |
Purpose This study aimed to investigate the results of compartmental modeling (CM) and spectral analysis (SA) generated with dynamic 18F-FMISO tumor images. Besides, the regular tissue-to-blood ratio (TBR) images were derived and compared with the dynamic models. Methods Nine subjects with glioblastoma underwent PET/CT imaging with the 18F-FMISO tracer. The protocol for PET imaging began with 15 min in dynamic mode and two 10-min duration static images at 120 min and 180 min post-injection. We used the two-tissue compartmental model for CM at the voxel basis, and we conducted SA to estimate the 18F-FMISO accumulation within each voxel. We also investigated the usual tumor-to-blood ratio (TBR) for comparison. Results The images of the tumor showed different patterns of hypoxia and necrosis as a function of PET scanning times, while CM and SA methods based on dynamic PET imaging equally located tumor hypoxia. The mean correlation of $ K_{i} $ images of all subjects between CM and SA was 0.63 ± 0.19 (0.24-0.86). CM produced less noisy Ki images than SA, and, in the contrary, SA produced accumulation component images more clear than with CM. CM-Ki and SA-$ K_{i} $ images were correlated with TBR images (r = 0.72 ± 0.20 and 0.56 ± 0.26, respectively). In the only subject having a continuously increasing tumor time-activity curve, the k3 image showed a high uptake in the necrosis region which was not apparent in TBR or Ki images. Conclusion Based on these results, the combination of CM and SA approaches was found more appropriate in generating voxel-based hypoxia images. © Korean Society of Nuclear Medicine 2021 |
| abstractGer |
Purpose This study aimed to investigate the results of compartmental modeling (CM) and spectral analysis (SA) generated with dynamic 18F-FMISO tumor images. Besides, the regular tissue-to-blood ratio (TBR) images were derived and compared with the dynamic models. Methods Nine subjects with glioblastoma underwent PET/CT imaging with the 18F-FMISO tracer. The protocol for PET imaging began with 15 min in dynamic mode and two 10-min duration static images at 120 min and 180 min post-injection. We used the two-tissue compartmental model for CM at the voxel basis, and we conducted SA to estimate the 18F-FMISO accumulation within each voxel. We also investigated the usual tumor-to-blood ratio (TBR) for comparison. Results The images of the tumor showed different patterns of hypoxia and necrosis as a function of PET scanning times, while CM and SA methods based on dynamic PET imaging equally located tumor hypoxia. The mean correlation of $ K_{i} $ images of all subjects between CM and SA was 0.63 ± 0.19 (0.24-0.86). CM produced less noisy Ki images than SA, and, in the contrary, SA produced accumulation component images more clear than with CM. CM-Ki and SA-$ K_{i} $ images were correlated with TBR images (r = 0.72 ± 0.20 and 0.56 ± 0.26, respectively). In the only subject having a continuously increasing tumor time-activity curve, the k3 image showed a high uptake in the necrosis region which was not apparent in TBR or Ki images. Conclusion Based on these results, the combination of CM and SA approaches was found more appropriate in generating voxel-based hypoxia images. © Korean Society of Nuclear Medicine 2021 |
| abstract_unstemmed |
Purpose This study aimed to investigate the results of compartmental modeling (CM) and spectral analysis (SA) generated with dynamic 18F-FMISO tumor images. Besides, the regular tissue-to-blood ratio (TBR) images were derived and compared with the dynamic models. Methods Nine subjects with glioblastoma underwent PET/CT imaging with the 18F-FMISO tracer. The protocol for PET imaging began with 15 min in dynamic mode and two 10-min duration static images at 120 min and 180 min post-injection. We used the two-tissue compartmental model for CM at the voxel basis, and we conducted SA to estimate the 18F-FMISO accumulation within each voxel. We also investigated the usual tumor-to-blood ratio (TBR) for comparison. Results The images of the tumor showed different patterns of hypoxia and necrosis as a function of PET scanning times, while CM and SA methods based on dynamic PET imaging equally located tumor hypoxia. The mean correlation of $ K_{i} $ images of all subjects between CM and SA was 0.63 ± 0.19 (0.24-0.86). CM produced less noisy Ki images than SA, and, in the contrary, SA produced accumulation component images more clear than with CM. CM-Ki and SA-$ K_{i} $ images were correlated with TBR images (r = 0.72 ± 0.20 and 0.56 ± 0.26, respectively). In the only subject having a continuously increasing tumor time-activity curve, the k3 image showed a high uptake in the necrosis region which was not apparent in TBR or Ki images. Conclusion Based on these results, the combination of CM and SA approaches was found more appropriate in generating voxel-based hypoxia images. © Korean Society of Nuclear Medicine 2021 |
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| title_short |
Quantification of Hypoxia in Human Glioblastoma using PET with 18F-FMISO |
| url |
https://dx.doi.org/10.1007/s13139-021-00693-8 |
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| author2 |
Lamare, Frédéric Fernandez, Philippe Bentourkia, M’hamed |
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Lamare, Frédéric Fernandez, Philippe Bentourkia, M’hamed |
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| doi_str |
10.1007/s13139-021-00693-8 |
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2024-07-03T22:57:10.216Z |
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| score |
7.399988 |