Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke

Purpose To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke. Methods We retrospectively enrolled 30 patients who had presented hy...
Ausführliche Beschreibung

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Autor*in:	Ichikawa, Shota [verfasserIn] Hamada, Misaki [verfasserIn] Watanabe, Daiki [verfasserIn] Ito, Osamu [verfasserIn] Moriya, Takafumi [verfasserIn] Yamamoto, Hiroyuki [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Computed tomography Hyperdense artery sign Acute ischemic stroke Slice thickness Hybrid iterative reconstruction algorithm

Übergeordnetes Werk:	Enthalten in: Emergency radiology - Heidelberg : Springer, 1994, 28(2020), 2 vom: 14. Okt., Seite 309-315
Übergeordnetes Werk:	volume:28 ; year:2020 ; number:2 ; day:14 ; month:10 ; pages:309-315

Links:	Volltext

DOI / URN:	10.1007/s10140-020-01864-4

Katalog-ID:	SPR043471501

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245	1	0	\|a Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke
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520			\|a Purpose To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke. Methods We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC). Results The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751). Conclusion Thin slice thickness of ≤ 1 mm has a better diagnostic performance for identifying hyperdense artery sign on brain non-contrast computed tomography with a hybrid iterative reconstruction algorithm in patients with acute ischemic stroke.
650		4	\|a Computed tomography \|7 (dpeaa)DE-He213
650		4	\|a Hyperdense artery sign \|7 (dpeaa)DE-He213
650		4	\|a Acute ischemic stroke \|7 (dpeaa)DE-He213
650		4	\|a Slice thickness \|7 (dpeaa)DE-He213
650		4	\|a Hybrid iterative reconstruction algorithm \|7 (dpeaa)DE-He213
700	1		\|a Hamada, Misaki \|e verfasserin \|4 aut
700	1		\|a Watanabe, Daiki \|e verfasserin \|4 aut
700	1		\|a Ito, Osamu \|e verfasserin \|4 aut
700	1		\|a Moriya, Takafumi \|e verfasserin \|4 aut
700	1		\|a Yamamoto, Hiroyuki \|e verfasserin \|4 aut
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author_variant	s i si m h mh d w dw o i oi t m tm h y hy
matchkey_str	article:14381435:2020----::piasiehcnsobanoptdoorpysnayrdtrtvrcntutoagrtmoietfigyednei
hierarchy_sort_str	2020
bklnumber	44.64
publishDate	2020
allfields	10.1007/s10140-020-01864-4 doi (DE-627)SPR043471501 (DE-599)SPRs10140-020-01864-4-e (SPR)s10140-020-01864-4-e DE-627 ger DE-627 rakwb eng 610 ASE 44.64 bkl Ichikawa, Shota verfasserin aut Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke. Methods We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC). Results The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751). Conclusion Thin slice thickness of ≤ 1 mm has a better diagnostic performance for identifying hyperdense artery sign on brain non-contrast computed tomography with a hybrid iterative reconstruction algorithm in patients with acute ischemic stroke. Computed tomography (dpeaa)DE-He213 Hyperdense artery sign (dpeaa)DE-He213 Acute ischemic stroke (dpeaa)DE-He213 Slice thickness (dpeaa)DE-He213 Hybrid iterative reconstruction algorithm (dpeaa)DE-He213 Hamada, Misaki verfasserin aut Watanabe, Daiki verfasserin aut Ito, Osamu verfasserin aut Moriya, Takafumi verfasserin aut Yamamoto, Hiroyuki verfasserin aut Enthalten in Emergency radiology Heidelberg : Springer, 1994 28(2020), 2 vom: 14. Okt., Seite 309-315 (DE-627)271599332 (DE-600)1480928-X 1438-1435 nnns volume:28 year:2020 number:2 day:14 month:10 pages:309-315 https://dx.doi.org/10.1007/s10140-020-01864-4 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_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_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_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 44.64 ASE AR 28 2020 2 14 10 309-315
spelling	10.1007/s10140-020-01864-4 doi (DE-627)SPR043471501 (DE-599)SPRs10140-020-01864-4-e (SPR)s10140-020-01864-4-e DE-627 ger DE-627 rakwb eng 610 ASE 44.64 bkl Ichikawa, Shota verfasserin aut Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke. Methods We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC). Results The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751). Conclusion Thin slice thickness of ≤ 1 mm has a better diagnostic performance for identifying hyperdense artery sign on brain non-contrast computed tomography with a hybrid iterative reconstruction algorithm in patients with acute ischemic stroke. Computed tomography (dpeaa)DE-He213 Hyperdense artery sign (dpeaa)DE-He213 Acute ischemic stroke (dpeaa)DE-He213 Slice thickness (dpeaa)DE-He213 Hybrid iterative reconstruction algorithm (dpeaa)DE-He213 Hamada, Misaki verfasserin aut Watanabe, Daiki verfasserin aut Ito, Osamu verfasserin aut Moriya, Takafumi verfasserin aut Yamamoto, Hiroyuki verfasserin aut Enthalten in Emergency radiology Heidelberg : Springer, 1994 28(2020), 2 vom: 14. Okt., Seite 309-315 (DE-627)271599332 (DE-600)1480928-X 1438-1435 nnns volume:28 year:2020 number:2 day:14 month:10 pages:309-315 https://dx.doi.org/10.1007/s10140-020-01864-4 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_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_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_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 44.64 ASE AR 28 2020 2 14 10 309-315
allfields_unstemmed	10.1007/s10140-020-01864-4 doi (DE-627)SPR043471501 (DE-599)SPRs10140-020-01864-4-e (SPR)s10140-020-01864-4-e DE-627 ger DE-627 rakwb eng 610 ASE 44.64 bkl Ichikawa, Shota verfasserin aut Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke. Methods We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC). Results The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751). Conclusion Thin slice thickness of ≤ 1 mm has a better diagnostic performance for identifying hyperdense artery sign on brain non-contrast computed tomography with a hybrid iterative reconstruction algorithm in patients with acute ischemic stroke. Computed tomography (dpeaa)DE-He213 Hyperdense artery sign (dpeaa)DE-He213 Acute ischemic stroke (dpeaa)DE-He213 Slice thickness (dpeaa)DE-He213 Hybrid iterative reconstruction algorithm (dpeaa)DE-He213 Hamada, Misaki verfasserin aut Watanabe, Daiki verfasserin aut Ito, Osamu verfasserin aut Moriya, Takafumi verfasserin aut Yamamoto, Hiroyuki verfasserin aut Enthalten in Emergency radiology Heidelberg : Springer, 1994 28(2020), 2 vom: 14. Okt., Seite 309-315 (DE-627)271599332 (DE-600)1480928-X 1438-1435 nnns volume:28 year:2020 number:2 day:14 month:10 pages:309-315 https://dx.doi.org/10.1007/s10140-020-01864-4 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_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_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_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 44.64 ASE AR 28 2020 2 14 10 309-315
allfieldsGer	10.1007/s10140-020-01864-4 doi (DE-627)SPR043471501 (DE-599)SPRs10140-020-01864-4-e (SPR)s10140-020-01864-4-e DE-627 ger DE-627 rakwb eng 610 ASE 44.64 bkl Ichikawa, Shota verfasserin aut Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke. Methods We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC). Results The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751). Conclusion Thin slice thickness of ≤ 1 mm has a better diagnostic performance for identifying hyperdense artery sign on brain non-contrast computed tomography with a hybrid iterative reconstruction algorithm in patients with acute ischemic stroke. Computed tomography (dpeaa)DE-He213 Hyperdense artery sign (dpeaa)DE-He213 Acute ischemic stroke (dpeaa)DE-He213 Slice thickness (dpeaa)DE-He213 Hybrid iterative reconstruction algorithm (dpeaa)DE-He213 Hamada, Misaki verfasserin aut Watanabe, Daiki verfasserin aut Ito, Osamu verfasserin aut Moriya, Takafumi verfasserin aut Yamamoto, Hiroyuki verfasserin aut Enthalten in Emergency radiology Heidelberg : Springer, 1994 28(2020), 2 vom: 14. Okt., Seite 309-315 (DE-627)271599332 (DE-600)1480928-X 1438-1435 nnns volume:28 year:2020 number:2 day:14 month:10 pages:309-315 https://dx.doi.org/10.1007/s10140-020-01864-4 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_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_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_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 44.64 ASE AR 28 2020 2 14 10 309-315
allfieldsSound	10.1007/s10140-020-01864-4 doi (DE-627)SPR043471501 (DE-599)SPRs10140-020-01864-4-e (SPR)s10140-020-01864-4-e DE-627 ger DE-627 rakwb eng 610 ASE 44.64 bkl Ichikawa, Shota verfasserin aut Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke. Methods We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC). Results The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751). Conclusion Thin slice thickness of ≤ 1 mm has a better diagnostic performance for identifying hyperdense artery sign on brain non-contrast computed tomography with a hybrid iterative reconstruction algorithm in patients with acute ischemic stroke. Computed tomography (dpeaa)DE-He213 Hyperdense artery sign (dpeaa)DE-He213 Acute ischemic stroke (dpeaa)DE-He213 Slice thickness (dpeaa)DE-He213 Hybrid iterative reconstruction algorithm (dpeaa)DE-He213 Hamada, Misaki verfasserin aut Watanabe, Daiki verfasserin aut Ito, Osamu verfasserin aut Moriya, Takafumi verfasserin aut Yamamoto, Hiroyuki verfasserin aut Enthalten in Emergency radiology Heidelberg : Springer, 1994 28(2020), 2 vom: 14. Okt., Seite 309-315 (DE-627)271599332 (DE-600)1480928-X 1438-1435 nnns volume:28 year:2020 number:2 day:14 month:10 pages:309-315 https://dx.doi.org/10.1007/s10140-020-01864-4 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_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_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_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 44.64 ASE AR 28 2020 2 14 10 309-315
language	English
source	Enthalten in Emergency radiology 28(2020), 2 vom: 14. Okt., Seite 309-315 volume:28 year:2020 number:2 day:14 month:10 pages:309-315
sourceStr	Enthalten in Emergency radiology 28(2020), 2 vom: 14. Okt., Seite 309-315 volume:28 year:2020 number:2 day:14 month:10 pages:309-315
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Computed tomography Hyperdense artery sign Acute ischemic stroke Slice thickness Hybrid iterative reconstruction algorithm
dewey-raw	610
isfreeaccess_bool	false
container_title	Emergency radiology
authorswithroles_txt_mv	Ichikawa, Shota @@aut@@ Hamada, Misaki @@aut@@ Watanabe, Daiki @@aut@@ Ito, Osamu @@aut@@ Moriya, Takafumi @@aut@@ Yamamoto, Hiroyuki @@aut@@
publishDateDaySort_date	2020-10-14T00:00:00Z
hierarchy_top_id	271599332
dewey-sort	3610
id	SPR043471501
language_de	englisch
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Methods We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC). Results The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751). 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author	Ichikawa, Shota
spellingShingle	Ichikawa, Shota ddc 610 bkl 44.64 misc Computed tomography misc Hyperdense artery sign misc Acute ischemic stroke misc Slice thickness misc Hybrid iterative reconstruction algorithm Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke
authorStr	Ichikawa, Shota
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topic_title	610 ASE 44.64 bkl Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke Computed tomography (dpeaa)DE-He213 Hyperdense artery sign (dpeaa)DE-He213 Acute ischemic stroke (dpeaa)DE-He213 Slice thickness (dpeaa)DE-He213 Hybrid iterative reconstruction algorithm (dpeaa)DE-He213
topic	ddc 610 bkl 44.64 misc Computed tomography misc Hyperdense artery sign misc Acute ischemic stroke misc Slice thickness misc Hybrid iterative reconstruction algorithm
topic_unstemmed	ddc 610 bkl 44.64 misc Computed tomography misc Hyperdense artery sign misc Acute ischemic stroke misc Slice thickness misc Hybrid iterative reconstruction algorithm
topic_browse	ddc 610 bkl 44.64 misc Computed tomography misc Hyperdense artery sign misc Acute ischemic stroke misc Slice thickness misc Hybrid iterative reconstruction algorithm
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title	Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke
ctrlnum	(DE-627)SPR043471501 (DE-599)SPRs10140-020-01864-4-e (SPR)s10140-020-01864-4-e
title_full	Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke
author_sort	Ichikawa, Shota
journal	Emergency radiology
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author_browse	Ichikawa, Shota Hamada, Misaki Watanabe, Daiki Ito, Osamu Moriya, Takafumi Yamamoto, Hiroyuki
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author-letter	Ichikawa, Shota
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title_sort	optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke
title_auth	Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke
abstract	Purpose To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke. Methods We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC). Results The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751). Conclusion Thin slice thickness of ≤ 1 mm has a better diagnostic performance for identifying hyperdense artery sign on brain non-contrast computed tomography with a hybrid iterative reconstruction algorithm in patients with acute ischemic stroke.
abstractGer	Purpose To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke. Methods We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC). Results The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751). Conclusion Thin slice thickness of ≤ 1 mm has a better diagnostic performance for identifying hyperdense artery sign on brain non-contrast computed tomography with a hybrid iterative reconstruction algorithm in patients with acute ischemic stroke.
abstract_unstemmed	Purpose To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke. Methods We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC). Results The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751). Conclusion Thin slice thickness of ≤ 1 mm has a better diagnostic performance for identifying hyperdense artery sign on brain non-contrast computed tomography with a hybrid iterative reconstruction algorithm in patients with acute ischemic stroke.
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title_short	Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke
url	https://dx.doi.org/10.1007/s10140-020-01864-4
remote_bool	true
author2	Hamada, Misaki Watanabe, Daiki Ito, Osamu Moriya, Takafumi Yamamoto, Hiroyuki
author2Str	Hamada, Misaki Watanabe, Daiki Ito, Osamu Moriya, Takafumi Yamamoto, Hiroyuki
ppnlink	271599332
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doi_str	10.1007/s10140-020-01864-4
up_date	2024-07-03T18:53:38.888Z
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Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke

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