A model based algorithm for perfusion estimation in interventional C-arm CT systems

Purpose: Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, “Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model,” AJNR Am. J. Neuroradiol. 30, - (2009) 10.3174/ajnr.A1586] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. Methods: The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. Results: The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 105 photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. Conclusions: In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. Furthermore, radiation dose could be reduced using their method to increase temporal resolution than using static reconstruction with a higher sampling frequency. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wagner, Martin - 1983- [verfasserIn] Deuerling-Zheng, Yu [verfasserIn] Möhlenbruch, Markus Alfred - 1979- [verfasserIn] Bendszus, Martin [verfasserIn] Boese, Jan [verfasserIn] Heiland, Sabine [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	1 March 2013

Schlagwörter:	backpropagation Computed tomography Computerised tomographs computerised tomography computerized tomography Digital computing or data processing equipment or methods dynamic reconstruction Flow visualization Fluid transport and rheology Haemodynamics haemorheology Image data processing or generation image reconstruction Image reconstruction image segmentation in general Interpolation iterative methods iterative reconstruction Medical image noise medical image processing Medical image quality Medical image reconstruction Medical imaging model function perfusion imaging Reconstruction Segmentation specially adapted for specific applications Tissues

Anmerkung:	Gesehen am 23.09.2021
Umfang:	11

Übergeordnetes Werk:	Enthalten in: Medical physics - Hoboken, NJ : Wiley, 1974, 40(2013), 3, Artikel-ID 031916, Seite 1-11
Übergeordnetes Werk:	volume:40 ; year:2013 ; number:3 ; elocationid:031916 ; pages:1-11 ; extent:11

Links:	Volltext Volltext

DOI / URN:	10.1118/1.4790467

Katalog-ID:	177169937X

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245	1	2	\|a A model based algorithm for perfusion estimation in interventional C-arm CT systems \|c Martin Wagner, Yu Deuerling-Zheng, Markus Möhlenbruch, Martin Bendszus, Jan Boese, Sabine Heiland
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520			\|a Purpose: Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, “Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model,” AJNR Am. J. Neuroradiol. 30, - (2009) 10.3174/ajnr.A1586] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. Methods: The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. Results: The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 105 photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. Conclusions: In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. Furthermore, radiation dose could be reduced using their method to increase temporal resolution than using static reconstruction with a higher sampling frequency.
650		4	\|a backpropagation
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650		4	\|a computerised tomography
650		4	\|a computerized tomography
650		4	\|a Digital computing or data processing equipment or methods
650		4	\|a dynamic reconstruction
650		4	\|a Flow visualization
650		4	\|a Fluid transport and rheology
650		4	\|a Haemodynamics
650		4	\|a haemorheology
650		4	\|a Image data processing or generation
650		4	\|a image reconstruction
650		4	\|a Image reconstruction
650		4	\|a image segmentation
650		4	\|a in general
650		4	\|a Interpolation
650		4	\|a iterative methods
650		4	\|a iterative reconstruction
650		4	\|a Medical image noise
650		4	\|a medical image processing
650		4	\|a Medical image quality
650		4	\|a Medical image reconstruction
650		4	\|a Medical imaging
650		4	\|a model function
650		4	\|a perfusion imaging
650		4	\|a Reconstruction
650		4	\|a Segmentation
650		4	\|a specially adapted for specific applications
650		4	\|a Tissues
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700	1		\|a Boese, Jan \|e verfasserin \|4 aut
700	1		\|a Heiland, Sabine \|e verfasserin \|0 (DE-588)106732626X \|0 (DE-627)818624450 \|0 (DE-576)426561368 \|4 aut
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Indexfelder

author_variant	m w mw y d z ydz m a m ma mam m b mb j b jb s h sh
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oclc_num	1341421263
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publishDate	2013
allfields	10.1118/1.4790467 doi (DE-627)177169937X (DE-599)KXP177169937X (OCoLC)1341421263 DE-627 ger DE-627 rda eng Wagner, Martin 1983- verfasserin (DE-588)1051422167 (DE-627)78617689X (DE-576)406446229 aut A model based algorithm for perfusion estimation in interventional C-arm CT systems Martin Wagner, Yu Deuerling-Zheng, Markus Möhlenbruch, Martin Bendszus, Jan Boese, Sabine Heiland 1 March 2013 11 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 23.09.2021 Purpose: Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, “Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model,” AJNR Am. J. Neuroradiol. 30, - (2009) 10.3174/ajnr.A1586] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. Methods: The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. Results: The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 105 photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. Conclusions: In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. Furthermore, radiation dose could be reduced using their method to increase temporal resolution than using static reconstruction with a higher sampling frequency. backpropagation Computed tomography Computerised tomographs computerised tomography computerized tomography Digital computing or data processing equipment or methods dynamic reconstruction Flow visualization Fluid transport and rheology Haemodynamics haemorheology Image data processing or generation image reconstruction Image reconstruction image segmentation in general Interpolation iterative methods iterative reconstruction Medical image noise medical image processing Medical image quality Medical image reconstruction Medical imaging model function perfusion imaging Reconstruction Segmentation specially adapted for specific applications Tissues Deuerling-Zheng, Yu verfasserin aut Möhlenbruch, Markus Alfred 1979- verfasserin (DE-588)137693591 (DE-627)594845041 (DE-576)304845655 aut Bendszus, Martin verfasserin (DE-588)1032676426 (DE-627)738634131 (DE-576)175567697 aut Boese, Jan verfasserin aut Heiland, Sabine verfasserin (DE-588)106732626X (DE-627)818624450 (DE-576)426561368 aut Enthalten in Medical physics Hoboken, NJ : Wiley, 1974 40(2013), 3, Artikel-ID 031916, Seite 1-11 Online-Ressource (DE-627)265784867 (DE-600)1466421-5 (DE-576)074891243 2473-4209 nnns volume:40 year:2013 number:3 elocationid:031916 pages:1-11 extent:11 https://doi.org/10.1118/1.4790467 Verlag Resolving-System lizenzpflichtig Volltext https://onlinelibrary.wiley.com/doi/abs/10.1118/1.4790467 Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 40 2013 3 031916 1-11 11 2013 01 DE-16-250 3980565645 00 --%%-- --%%-- --%%-- --%%-- l01 23-09-21 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_11 2013 01 DE-16-250 04 p (DE-627)1493467417 Wagner, Martin 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1501228323 Möhlenbruch, Markus Alfred 2013 01 DE-16-250 05 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3 2013 01 DE-16-250 06 p (DE-627)1450183360 Bendszus, Martin 2013 01 DE-16-250 06 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_4 2013 01 DE-16-250 07 p (DE-627)1496564405 Heiland, Sabine 2013 01 DE-16-250 07 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 07 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 07 s pos_6
spelling	10.1118/1.4790467 doi (DE-627)177169937X (DE-599)KXP177169937X (OCoLC)1341421263 DE-627 ger DE-627 rda eng Wagner, Martin 1983- verfasserin (DE-588)1051422167 (DE-627)78617689X (DE-576)406446229 aut A model based algorithm for perfusion estimation in interventional C-arm CT systems Martin Wagner, Yu Deuerling-Zheng, Markus Möhlenbruch, Martin Bendszus, Jan Boese, Sabine Heiland 1 March 2013 11 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 23.09.2021 Purpose: Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, “Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model,” AJNR Am. J. Neuroradiol. 30, - (2009) 10.3174/ajnr.A1586] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. Methods: The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. Results: The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 105 photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. Conclusions: In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. Furthermore, radiation dose could be reduced using their method to increase temporal resolution than using static reconstruction with a higher sampling frequency. backpropagation Computed tomography Computerised tomographs computerised tomography computerized tomography Digital computing or data processing equipment or methods dynamic reconstruction Flow visualization Fluid transport and rheology Haemodynamics haemorheology Image data processing or generation image reconstruction Image reconstruction image segmentation in general Interpolation iterative methods iterative reconstruction Medical image noise medical image processing Medical image quality Medical image reconstruction Medical imaging model function perfusion imaging Reconstruction Segmentation specially adapted for specific applications Tissues Deuerling-Zheng, Yu verfasserin aut Möhlenbruch, Markus Alfred 1979- verfasserin (DE-588)137693591 (DE-627)594845041 (DE-576)304845655 aut Bendszus, Martin verfasserin (DE-588)1032676426 (DE-627)738634131 (DE-576)175567697 aut Boese, Jan verfasserin aut Heiland, Sabine verfasserin (DE-588)106732626X (DE-627)818624450 (DE-576)426561368 aut Enthalten in Medical physics Hoboken, NJ : Wiley, 1974 40(2013), 3, Artikel-ID 031916, Seite 1-11 Online-Ressource (DE-627)265784867 (DE-600)1466421-5 (DE-576)074891243 2473-4209 nnns volume:40 year:2013 number:3 elocationid:031916 pages:1-11 extent:11 https://doi.org/10.1118/1.4790467 Verlag Resolving-System lizenzpflichtig Volltext https://onlinelibrary.wiley.com/doi/abs/10.1118/1.4790467 Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 40 2013 3 031916 1-11 11 2013 01 DE-16-250 3980565645 00 --%%-- --%%-- --%%-- --%%-- l01 23-09-21 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_11 2013 01 DE-16-250 04 p (DE-627)1493467417 Wagner, Martin 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1501228323 Möhlenbruch, Markus Alfred 2013 01 DE-16-250 05 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3 2013 01 DE-16-250 06 p (DE-627)1450183360 Bendszus, Martin 2013 01 DE-16-250 06 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_4 2013 01 DE-16-250 07 p (DE-627)1496564405 Heiland, Sabine 2013 01 DE-16-250 07 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 07 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 07 s pos_6
allfields_unstemmed	10.1118/1.4790467 doi (DE-627)177169937X (DE-599)KXP177169937X (OCoLC)1341421263 DE-627 ger DE-627 rda eng Wagner, Martin 1983- verfasserin (DE-588)1051422167 (DE-627)78617689X (DE-576)406446229 aut A model based algorithm for perfusion estimation in interventional C-arm CT systems Martin Wagner, Yu Deuerling-Zheng, Markus Möhlenbruch, Martin Bendszus, Jan Boese, Sabine Heiland 1 March 2013 11 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 23.09.2021 Purpose: Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, “Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model,” AJNR Am. J. Neuroradiol. 30, - (2009) 10.3174/ajnr.A1586] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. Methods: The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. Results: The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 105 photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. Conclusions: In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. Furthermore, radiation dose could be reduced using their method to increase temporal resolution than using static reconstruction with a higher sampling frequency. backpropagation Computed tomography Computerised tomographs computerised tomography computerized tomography Digital computing or data processing equipment or methods dynamic reconstruction Flow visualization Fluid transport and rheology Haemodynamics haemorheology Image data processing or generation image reconstruction Image reconstruction image segmentation in general Interpolation iterative methods iterative reconstruction Medical image noise medical image processing Medical image quality Medical image reconstruction Medical imaging model function perfusion imaging Reconstruction Segmentation specially adapted for specific applications Tissues Deuerling-Zheng, Yu verfasserin aut Möhlenbruch, Markus Alfred 1979- verfasserin (DE-588)137693591 (DE-627)594845041 (DE-576)304845655 aut Bendszus, Martin verfasserin (DE-588)1032676426 (DE-627)738634131 (DE-576)175567697 aut Boese, Jan verfasserin aut Heiland, Sabine verfasserin (DE-588)106732626X (DE-627)818624450 (DE-576)426561368 aut Enthalten in Medical physics Hoboken, NJ : Wiley, 1974 40(2013), 3, Artikel-ID 031916, Seite 1-11 Online-Ressource (DE-627)265784867 (DE-600)1466421-5 (DE-576)074891243 2473-4209 nnns volume:40 year:2013 number:3 elocationid:031916 pages:1-11 extent:11 https://doi.org/10.1118/1.4790467 Verlag Resolving-System lizenzpflichtig Volltext https://onlinelibrary.wiley.com/doi/abs/10.1118/1.4790467 Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 40 2013 3 031916 1-11 11 2013 01 DE-16-250 3980565645 00 --%%-- --%%-- --%%-- --%%-- l01 23-09-21 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_11 2013 01 DE-16-250 04 p (DE-627)1493467417 Wagner, Martin 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1501228323 Möhlenbruch, Markus Alfred 2013 01 DE-16-250 05 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3 2013 01 DE-16-250 06 p (DE-627)1450183360 Bendszus, Martin 2013 01 DE-16-250 06 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_4 2013 01 DE-16-250 07 p (DE-627)1496564405 Heiland, Sabine 2013 01 DE-16-250 07 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 07 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 07 s pos_6
allfieldsGer	10.1118/1.4790467 doi (DE-627)177169937X (DE-599)KXP177169937X (OCoLC)1341421263 DE-627 ger DE-627 rda eng Wagner, Martin 1983- verfasserin (DE-588)1051422167 (DE-627)78617689X (DE-576)406446229 aut A model based algorithm for perfusion estimation in interventional C-arm CT systems Martin Wagner, Yu Deuerling-Zheng, Markus Möhlenbruch, Martin Bendszus, Jan Boese, Sabine Heiland 1 March 2013 11 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 23.09.2021 Purpose: Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, “Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model,” AJNR Am. J. Neuroradiol. 30, - (2009) 10.3174/ajnr.A1586] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. Methods: The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. Results: The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 105 photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. Conclusions: In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. Furthermore, radiation dose could be reduced using their method to increase temporal resolution than using static reconstruction with a higher sampling frequency. backpropagation Computed tomography Computerised tomographs computerised tomography computerized tomography Digital computing or data processing equipment or methods dynamic reconstruction Flow visualization Fluid transport and rheology Haemodynamics haemorheology Image data processing or generation image reconstruction Image reconstruction image segmentation in general Interpolation iterative methods iterative reconstruction Medical image noise medical image processing Medical image quality Medical image reconstruction Medical imaging model function perfusion imaging Reconstruction Segmentation specially adapted for specific applications Tissues Deuerling-Zheng, Yu verfasserin aut Möhlenbruch, Markus Alfred 1979- verfasserin (DE-588)137693591 (DE-627)594845041 (DE-576)304845655 aut Bendszus, Martin verfasserin (DE-588)1032676426 (DE-627)738634131 (DE-576)175567697 aut Boese, Jan verfasserin aut Heiland, Sabine verfasserin (DE-588)106732626X (DE-627)818624450 (DE-576)426561368 aut Enthalten in Medical physics Hoboken, NJ : Wiley, 1974 40(2013), 3, Artikel-ID 031916, Seite 1-11 Online-Ressource (DE-627)265784867 (DE-600)1466421-5 (DE-576)074891243 2473-4209 nnns volume:40 year:2013 number:3 elocationid:031916 pages:1-11 extent:11 https://doi.org/10.1118/1.4790467 Verlag Resolving-System lizenzpflichtig Volltext https://onlinelibrary.wiley.com/doi/abs/10.1118/1.4790467 Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 40 2013 3 031916 1-11 11 2013 01 DE-16-250 3980565645 00 --%%-- --%%-- --%%-- --%%-- l01 23-09-21 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_11 2013 01 DE-16-250 04 p (DE-627)1493467417 Wagner, Martin 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1501228323 Möhlenbruch, Markus Alfred 2013 01 DE-16-250 05 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3 2013 01 DE-16-250 06 p (DE-627)1450183360 Bendszus, Martin 2013 01 DE-16-250 06 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_4 2013 01 DE-16-250 07 p (DE-627)1496564405 Heiland, Sabine 2013 01 DE-16-250 07 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 07 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 07 s pos_6
allfieldsSound	10.1118/1.4790467 doi (DE-627)177169937X (DE-599)KXP177169937X (OCoLC)1341421263 DE-627 ger DE-627 rda eng Wagner, Martin 1983- verfasserin (DE-588)1051422167 (DE-627)78617689X (DE-576)406446229 aut A model based algorithm for perfusion estimation in interventional C-arm CT systems Martin Wagner, Yu Deuerling-Zheng, Markus Möhlenbruch, Martin Bendszus, Jan Boese, Sabine Heiland 1 March 2013 11 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 23.09.2021 Purpose: Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, “Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model,” AJNR Am. J. Neuroradiol. 30, - (2009) 10.3174/ajnr.A1586] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. Methods: The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. Results: The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 105 photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. Conclusions: In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. Furthermore, radiation dose could be reduced using their method to increase temporal resolution than using static reconstruction with a higher sampling frequency. backpropagation Computed tomography Computerised tomographs computerised tomography computerized tomography Digital computing or data processing equipment or methods dynamic reconstruction Flow visualization Fluid transport and rheology Haemodynamics haemorheology Image data processing or generation image reconstruction Image reconstruction image segmentation in general Interpolation iterative methods iterative reconstruction Medical image noise medical image processing Medical image quality Medical image reconstruction Medical imaging model function perfusion imaging Reconstruction Segmentation specially adapted for specific applications Tissues Deuerling-Zheng, Yu verfasserin aut Möhlenbruch, Markus Alfred 1979- verfasserin (DE-588)137693591 (DE-627)594845041 (DE-576)304845655 aut Bendszus, Martin verfasserin (DE-588)1032676426 (DE-627)738634131 (DE-576)175567697 aut Boese, Jan verfasserin aut Heiland, Sabine verfasserin (DE-588)106732626X (DE-627)818624450 (DE-576)426561368 aut Enthalten in Medical physics Hoboken, NJ : Wiley, 1974 40(2013), 3, Artikel-ID 031916, Seite 1-11 Online-Ressource (DE-627)265784867 (DE-600)1466421-5 (DE-576)074891243 2473-4209 nnns volume:40 year:2013 number:3 elocationid:031916 pages:1-11 extent:11 https://doi.org/10.1118/1.4790467 Verlag Resolving-System lizenzpflichtig Volltext https://onlinelibrary.wiley.com/doi/abs/10.1118/1.4790467 Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 40 2013 3 031916 1-11 11 2013 01 DE-16-250 3980565645 00 --%%-- --%%-- --%%-- --%%-- l01 23-09-21 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_11 2013 01 DE-16-250 04 p (DE-627)1493467417 Wagner, Martin 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1501228323 Möhlenbruch, Markus Alfred 2013 01 DE-16-250 05 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3 2013 01 DE-16-250 06 p (DE-627)1450183360 Bendszus, Martin 2013 01 DE-16-250 06 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_4 2013 01 DE-16-250 07 p (DE-627)1496564405 Heiland, Sabine 2013 01 DE-16-250 07 k (DE-627)1416741267 Neurologische Universitätsklinik 2013 01 DE-16-250 07 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 07 s pos_6
language	English
source	Enthalten in Medical physics 40(2013), 3, Artikel-ID 031916, Seite 1-11 volume:40 year:2013 number:3 elocationid:031916 pages:1-11 extent:11
sourceStr	Enthalten in Medical physics 40(2013), 3, Artikel-ID 031916, Seite 1-11 volume:40 year:2013 number:3 elocationid:031916 pages:1-11 extent:11
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institution	findex.gbv.de
selectbib_iln_str_mv	2013@01
topic_facet	backpropagation Computed tomography Computerised tomographs computerised tomography computerized tomography Digital computing or data processing equipment or methods dynamic reconstruction Flow visualization Fluid transport and rheology Haemodynamics haemorheology Image data processing or generation image reconstruction Image reconstruction image segmentation in general Interpolation iterative methods iterative reconstruction Medical image noise medical image processing Medical image quality Medical image reconstruction Medical imaging model function perfusion imaging Reconstruction Segmentation specially adapted for specific applications Tissues
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container_title	Medical physics
authorswithroles_txt_mv	Wagner, Martin @@aut@@ Deuerling-Zheng, Yu @@aut@@ Möhlenbruch, Markus Alfred @@aut@@ Bendszus, Martin @@aut@@ Boese, Jan @@aut@@ Heiland, Sabine @@aut@@
publishDateDaySort_date	2013-01-01T00:00:00Z
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fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">177169937X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230427130224.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210923s2013 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1118/1.4790467</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)177169937X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP177169937X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1341421263</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wagner, Martin</subfield><subfield code="d">1983-</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1051422167</subfield><subfield code="0">(DE-627)78617689X</subfield><subfield code="0">(DE-576)406446229</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A model based algorithm for perfusion estimation in interventional C-arm CT systems</subfield><subfield code="c">Martin Wagner, Yu Deuerling-Zheng, Markus Möhlenbruch, Martin Bendszus, Jan Boese, Sabine Heiland</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">1 March 2013</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">11</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Gesehen am 23.09.2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose: Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, “Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model,” AJNR Am. J. Neuroradiol. 30, - (2009) 10.3174/ajnr.A1586] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. Methods: The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. Results: The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 105 photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. Conclusions: In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. 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title	A model based algorithm for perfusion estimation in interventional C-arm CT systems
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title_full	A model based algorithm for perfusion estimation in interventional C-arm CT systems Martin Wagner, Yu Deuerling-Zheng, Markus Möhlenbruch, Martin Bendszus, Jan Boese, Sabine Heiland
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title_sort	model based algorithm for perfusion estimation in interventional c-arm ct systems
title_auth	A model based algorithm for perfusion estimation in interventional C-arm CT systems
abstract	Purpose: Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, “Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model,” AJNR Am. J. Neuroradiol. 30, - (2009) 10.3174/ajnr.A1586] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. Methods: The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. Results: The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 105 photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. Conclusions: In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. Furthermore, radiation dose could be reduced using their method to increase temporal resolution than using static reconstruction with a higher sampling frequency. Gesehen am 23.09.2021
abstractGer	Purpose: Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, “Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model,” AJNR Am. J. Neuroradiol. 30, - (2009) 10.3174/ajnr.A1586] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. Methods: The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. Results: The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 105 photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. Conclusions: In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. Furthermore, radiation dose could be reduced using their method to increase temporal resolution than using static reconstruction with a higher sampling frequency. Gesehen am 23.09.2021
abstract_unstemmed	Purpose: Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, “Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model,” AJNR Am. J. Neuroradiol. 30, - (2009) 10.3174/ajnr.A1586] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. Methods: The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. Results: The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 105 photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. Conclusions: In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. Furthermore, radiation dose could be reduced using their method to increase temporal resolution than using static reconstruction with a higher sampling frequency. Gesehen am 23.09.2021
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title_short	A model based algorithm for perfusion estimation in interventional C-arm CT systems
url	https://doi.org/10.1118/1.4790467 https://onlinelibrary.wiley.com/doi/abs/10.1118/1.4790467
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">177169937X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230427130224.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210923s2013 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1118/1.4790467</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)177169937X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP177169937X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1341421263</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wagner, Martin</subfield><subfield code="d">1983-</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1051422167</subfield><subfield code="0">(DE-627)78617689X</subfield><subfield code="0">(DE-576)406446229</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A model based algorithm for perfusion estimation in interventional C-arm CT systems</subfield><subfield code="c">Martin Wagner, Yu Deuerling-Zheng, Markus Möhlenbruch, Martin Bendszus, Jan Boese, Sabine Heiland</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">1 March 2013</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">11</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Gesehen am 23.09.2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose: Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, “Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model,” AJNR Am. J. Neuroradiol. 30, - (2009) 10.3174/ajnr.A1586] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. Methods: The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. Results: The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 105 photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. Conclusions: In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. 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A model based algorithm for perfusion estimation in interventional C-arm CT systems

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