Using the coefficient of determination to identify injury regions after stroke in pre-clinical FDG-PET images
In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of the most appropriate normalization method in stroke studies remains unclear, as demonstrated by inconsist...
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| Published in | Computers in biology and medicine Vol. 184; p. 109401 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.01.2025
Elsevier Limited |
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| Abstract | In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of the most appropriate normalization method in stroke studies remains unclear, as demonstrated by inconsistent findings in the literature.
Here, we propose a regression- and single-subject-based model for analyzing FDG-PET images without intensity normalization. Two independent data sets were collected before and after middle cerebral artery occlusion (MCAO), with one comprising 120 rats and the other 96 rats. After data preprocessing, voxel intensities in the same region and hemisphere were paired before and after the MCAO scan. A linear regression model was applied to the paired data, and the coefficient of determination R2 was calculated to measure the linearity. The R2 values between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those with reduced linearity. Our method was compared with voxel-wise analysis under different intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data.
The significant regions identified by the proposed method showed a large degree of overlap with the infarcted regions identified by TTC data, as measured by the Dice similarity coefficient (DSC). The average DSC of the proposed method was 59.7%, whereas the DSCs of the existing approaches ranged from 41.4%∼51.3%. Additional validation using receiver operating characteristic (ROC) demonstrated that the area under the curve (AUC) of the average ROC curves reached 0.84 using the proposed method, whereas existing methods achieved AUCs ranging from 0.77∼0.79. The identified regions were consistent across the two independent data sets, and some findings were corroborated by other publications.
The proposed model presents a novel quantitative approach for identifying injury regions post-stroke using FDG-PET images. The calculation does not require intensity normalization and can be applied to individual subjects. The method yields more sensitive results compared to existing identification methods.
•Identify injured regions after cerebral ischemia using a regression model.•Provide better sensitivity than voxel-wise analysis with intensity normalization.•Use TTC staining-based ground truth to validate the performance.•Significant regions can be derived from a single subject. |
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| AbstractList | In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of the most appropriate normalization method in stroke studies remains unclear, as demonstrated by inconsistent findings in the literature.BACKGROUNDIn the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of the most appropriate normalization method in stroke studies remains unclear, as demonstrated by inconsistent findings in the literature.Here, we propose a regression- and single-subject-based model for analyzing FDG-PET images without intensity normalization. Two independent data sets were collected before and after middle cerebral artery occlusion (MCAO), with one comprising 120 rats and the other 96 rats. After data preprocessing, voxel intensities in the same region and hemisphere were paired before and after the MCAO scan. A linear regression model was applied to the paired data, and the coefficient of determination R2 was calculated to measure the linearity. The R2 values between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those with reduced linearity. Our method was compared with voxel-wise analysis under different intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data.MATERIALS AND METHODSHere, we propose a regression- and single-subject-based model for analyzing FDG-PET images without intensity normalization. Two independent data sets were collected before and after middle cerebral artery occlusion (MCAO), with one comprising 120 rats and the other 96 rats. After data preprocessing, voxel intensities in the same region and hemisphere were paired before and after the MCAO scan. A linear regression model was applied to the paired data, and the coefficient of determination R2 was calculated to measure the linearity. The R2 values between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those with reduced linearity. Our method was compared with voxel-wise analysis under different intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data.The significant regions identified by the proposed method showed a large degree of overlap with the infarcted regions identified by TTC data, as measured by the Dice similarity coefficient (DSC). The average DSC of the proposed method was 59.7%, whereas the DSCs of the existing approaches ranged from 41.4%∼51.3%. Additional validation using receiver operating characteristic (ROC) demonstrated that the area under the curve (AUC) of the average ROC curves reached 0.84 using the proposed method, whereas existing methods achieved AUCs ranging from 0.77∼0.79. The identified regions were consistent across the two independent data sets, and some findings were corroborated by other publications.RESULTSThe significant regions identified by the proposed method showed a large degree of overlap with the infarcted regions identified by TTC data, as measured by the Dice similarity coefficient (DSC). The average DSC of the proposed method was 59.7%, whereas the DSCs of the existing approaches ranged from 41.4%∼51.3%. Additional validation using receiver operating characteristic (ROC) demonstrated that the area under the curve (AUC) of the average ROC curves reached 0.84 using the proposed method, whereas existing methods achieved AUCs ranging from 0.77∼0.79. The identified regions were consistent across the two independent data sets, and some findings were corroborated by other publications.The proposed model presents a novel quantitative approach for identifying injury regions post-stroke using FDG-PET images. The calculation does not require intensity normalization and can be applied to individual subjects. The method yields more sensitive results compared to existing identification methods.CONCLUSIONSThe proposed model presents a novel quantitative approach for identifying injury regions post-stroke using FDG-PET images. The calculation does not require intensity normalization and can be applied to individual subjects. The method yields more sensitive results compared to existing identification methods. In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of the most appropriate normalization method in stroke studies remains unclear, as demonstrated by inconsistent findings in the literature. Here, we propose a regression- and single-subject-based model for analyzing FDG-PET images without intensity normalization. Two independent data sets were collected before and after middle cerebral artery occlusion (MCAO), with one comprising 120 rats and the other 96 rats. After data preprocessing, voxel intensities in the same region and hemisphere were paired before and after the MCAO scan. A linear regression model was applied to the paired data, and the coefficient of determination R2 was calculated to measure the linearity. The R2 values between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those with reduced linearity. Our method was compared with voxel-wise analysis under different intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data. The significant regions identified by the proposed method showed a large degree of overlap with the infarcted regions identified by TTC data, as measured by the Dice similarity coefficient (DSC). The average DSC of the proposed method was 59.7%, whereas the DSCs of the existing approaches ranged from 41.4%∼51.3%. Additional validation using receiver operating characteristic (ROC) demonstrated that the area under the curve (AUC) of the average ROC curves reached 0.84 using the proposed method, whereas existing methods achieved AUCs ranging from 0.77∼0.79. The identified regions were consistent across the two independent data sets, and some findings were corroborated by other publications. The proposed model presents a novel quantitative approach for identifying injury regions post-stroke using FDG-PET images. The calculation does not require intensity normalization and can be applied to individual subjects. The method yields more sensitive results compared to existing identification methods. •Identify injured regions after cerebral ischemia using a regression model.•Provide better sensitivity than voxel-wise analysis with intensity normalization.•Use TTC staining-based ground truth to validate the performance.•Significant regions can be derived from a single subject. AbstractBackground:In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of the most appropriate normalization method in stroke studies remains unclear, as demonstrated by inconsistent findings in the literature. Materials and methods:Here, we propose a regression- and single-subject-based model for analyzing FDG-PET images without intensity normalization. Two independent data sets were collected before and after middle cerebral artery occlusion (MCAO), with one comprising 120 rats and the other 96 rats. After data preprocessing, voxel intensities in the same region and hemisphere were paired before and after the MCAO scan. A linear regression model was applied to the paired data, and the coefficient of determination R2 was calculated to measure the linearity. The R2 values between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those with reduced linearity. Our method was compared with voxel-wise analysis under different intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data. Results:The significant regions identified by the proposed method showed a large degree of overlap with the infarcted regions identified by TTC data, as measured by the Dice similarity coefficient (DSC). The average DSC of the proposed method was 59.7%, whereas the DSCs of the existing approaches ranged from 41.4%∼51.3%. Additional validation using receiver operating characteristic (ROC) demonstrated that the area under the curve (AUC) of the average ROC curves reached 0.84 using the proposed method, whereas existing methods achieved AUCs ranging from 0.77∼0.79. The identified regions were consistent across the two independent data sets, and some findings were corroborated by other publications. Conclusions:The proposed model presents a novel quantitative approach for identifying injury regions post-stroke using FDG-PET images. The calculation does not require intensity normalization and can be applied to individual subjects. The method yields more sensitive results compared to existing identification methods. Background:In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of the most appropriate normalization method in stroke studies remains unclear, as demonstrated by inconsistent findings in the literature.Materials and methods:Here, we propose a regression- and single-subject-based model for analyzing FDG-PET images without intensity normalization. Two independent data sets were collected before and after middle cerebral artery occlusion (MCAO), with one comprising 120 rats and the other 96 rats. After data preprocessing, voxel intensities in the same region and hemisphere were paired before and after the MCAO scan. A linear regression model was applied to the paired data, and the coefficient of determination R2 was calculated to measure the linearity. The R2 values between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those with reduced linearity. Our method was compared with voxel-wise analysis under different intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data.Results:The significant regions identified by the proposed method showed a large degree of overlap with the infarcted regions identified by TTC data, as measured by the Dice similarity coefficient (DSC). The average DSC of the proposed method was 59.7%, whereas the DSCs of the existing approaches ranged from 41.4%∼51.3%. Additional validation using receiver operating characteristic (ROC) demonstrated that the area under the curve (AUC) of the average ROC curves reached 0.84 using the proposed method, whereas existing methods achieved AUCs ranging from 0.77∼0.79. The identified regions were consistent across the two independent data sets, and some findings were corroborated by other publications.Conclusions:The proposed model presents a novel quantitative approach for identifying injury regions post-stroke using FDG-PET images. The calculation does not require intensity normalization and can be applied to individual subjects. The method yields more sensitive results compared to existing identification methods. In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of the most appropriate normalization method in stroke studies remains unclear, as demonstrated by inconsistent findings in the literature. Here, we propose a regression- and single-subject-based model for analyzing FDG-PET images without intensity normalization. Two independent data sets were collected before and after middle cerebral artery occlusion (MCAO), with one comprising 120 rats and the other 96 rats. After data preprocessing, voxel intensities in the same region and hemisphere were paired before and after the MCAO scan. A linear regression model was applied to the paired data, and the coefficient of determination R was calculated to measure the linearity. The R values between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those with reduced linearity. Our method was compared with voxel-wise analysis under different intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data. The significant regions identified by the proposed method showed a large degree of overlap with the infarcted regions identified by TTC data, as measured by the Dice similarity coefficient (DSC). The average DSC of the proposed method was 59.7%, whereas the DSCs of the existing approaches ranged from 41.4%∼51.3%. Additional validation using receiver operating characteristic (ROC) demonstrated that the area under the curve (AUC) of the average ROC curves reached 0.84 using the proposed method, whereas existing methods achieved AUCs ranging from 0.77∼0.79. The identified regions were consistent across the two independent data sets, and some findings were corroborated by other publications. The proposed model presents a novel quantitative approach for identifying injury regions post-stroke using FDG-PET images. The calculation does not require intensity normalization and can be applied to individual subjects. The method yields more sensitive results compared to existing identification methods. |
| ArticleNumber | 109401 |
| Author | Zhang, Xuechen Yu, Weichuan Shen, Xiaoyan Liu, Huafeng Li, Jia Tang, Hongtu He, Wuxian Li, Chenrui |
| Author_xml | – sequence: 1 givenname: Wuxian surname: He fullname: He, Wuxian organization: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China – sequence: 2 givenname: Hongtu surname: Tang fullname: Tang, Hongtu organization: Department of Acupuncture and Moxibustion, Hubei University of Chinese Medicine, Wuhan, 430065, Hubei, China – sequence: 3 givenname: Jia surname: Li fullname: Li, Jia organization: Xianning Hospital of Traditional Chinese Medicine, Xianning, 437100, Hubei, China – sequence: 4 givenname: Xiaoyan surname: Shen fullname: Shen, Xiaoyan organization: College of Science, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, China – sequence: 5 givenname: Xuechen surname: Zhang fullname: Zhang, Xuechen organization: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China – sequence: 6 givenname: Chenrui surname: Li fullname: Li, Chenrui organization: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China – sequence: 7 givenname: Huafeng surname: Liu fullname: Liu, Huafeng organization: State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, 310027, Zhejiang, China – sequence: 8 givenname: Weichuan orcidid: 0000-0002-5510-6916 surname: Yu fullname: Yu, Weichuan email: eeyu@ust.hk organization: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China |
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| Cites_doi | 10.1016/j.patrec.2005.10.010 10.1038/s41592-023-02034-3 10.1371/journal.pone.0229345 10.1023/A:1007979827043 10.1080/000164800750000207 10.1161/STROKEAHA.114.008407 10.1097/ANA.0b013e31802c7e33 10.1016/0006-8993(95)00182-P 10.1016/j.neuroimage.2011.04.045 10.1007/s13139-019-00612-y 10.1016/j.arr.2016.02.003 10.3389/fninf.2018.00094 10.1186/s13550-021-00771-0 10.1006/nimg.1997.0302 10.1155/2018/2483078 10.1161/STROKEAHA.115.008905 10.3389/fmed.2021.744157 10.1523/JNEUROSCI.3437-09.2009 10.2967/jnumed.108.057182 10.1371/journal.pone.0108295 10.1016/j.compbiomed.2022.105873 10.1371/journal.pone.0004764 10.1016/j.expneurol.2012.08.011 10.1007/s00429-010-0246-0 10.1016/j.neuroimage.2005.03.012 10.1016/S0010-4825(97)00031-0 10.1016/j.neuroimage.2016.09.031 10.1155/2013/634598 10.1007/s12264-018-0240-8 10.2214/AJR.13.12363 10.1016/j.neulet.2012.11.026 10.3389/fneur.2019.00154 10.1161/STROKEAHA.111.624551 10.1016/j.neuroimage.2009.03.021 10.1016/j.nicl.2018.08.006 10.1002/hbm.20690 10.1016/j.neuropharm.2020.108202 10.1016/j.neuroimage.2008.07.015 10.1161/STROKEAHA.107.511261 10.1016/S1474-4422(20)30314-8 10.1137/040615286 10.1016/j.neuroimage.2020.117229 10.1007/s12975-019-00765-0 10.1038/nrneurol.2017.129 10.1161/CIRCRESAHA.122.319951 10.1016/j.apradiso.2009.03.002 10.1007/s12264-022-00865-2 10.1016/j.jstrokecerebrovasdis.2021.105630 10.1016/j.neuroimage.2011.12.037 10.1016/j.neuroimage.2009.09.017 10.1007/s00259-008-1039-z 10.1161/01.STR.17.3.472 10.1016/j.neuroscience.2007.01.046 10.1007/s00259-022-05752-6 10.1007/s12149-010-0359-9 10.1161/STROKEAHA.113.000903 10.1038/jcbfm.1988.111 10.1016/j.radcr.2019.09.005 10.1038/s41598-020-65957-3 |
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| Keywords | FDG-PET MCAO Linear regression Intensity normalization |
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| References | Chételat, Arbizu, Barthel, Garibotto, Law, Morbelli, van de Giessen, Agosta, Barkhof, Brooks (b4) 2020; 19 Arnberg, Grafström, Lundberg, Nikkhou-Aski, Little, Damberg, Mitsios, Mulder, Lu, Söderman (b34) 2015; 46 Mosconi, Mistur, Switalski, Tsui, Glodzik, Li, Pirraglia, De Santi, Reisberg, Wisniewski (b3) 2009; 36 He, Tang, Li, Hou, Shen, Li, Liu, Yu (b39) 2022; 38 Nugent, Croteau, Potvin, Castellano, Dieumegarde, Cunnane, Duchesne (b42) 2020; 10 Conchou, Loubinoux, Castel-Lacanal, Le Tinnier, Gerdelat-Mas, Faure-Marie, Gros, Thalamas, Calvas, Berry (b15) 2009; 30 Proesmans, Raedt, Germonpré, Christiaen, Descamps, Boon, De Herdt, Vanhove (b30) 2021; 8 Zhang, Nie, Liu, Shan, Initiative (b45) 2022; 49 Wang, Snyder, Bilbro, Santago (b38) 1998; 28 Joya, Padro, Gomez-Vallejo, Plaza-García, Llop, Martín (b65) 2018; 2018 Walberer, Backes, Rueger, Neumaier, Endepols, Hoehn, Fink, Schroeter, Graf (b17) 2012; 43 Berti, Polito, Borghammer, Ramat, Mosconi, Vanzi, De Cristofaro, De Leon, Sorbi, Pupi (b64) 2012; 56 Lourbopoulos, Chatzigeorgiou, Mavridis, Kokkinakis, Tascos, Simeonidou (b52) 2012; 16 Frackowiak, Friston, Frith, Dolan, Price, Zeki, Ashburner, Penny (b23) 2004 Jeong, Jeong, Park, Kang (b25) 2019; 53 Shivamurthy, Tahari, Marcus, Subramaniam (b7) 2015; 204 Nie, Liang, Jiang, Duan, Huang, Zhang, Li, Liu, Shan (b28) 2018; 34 Bunevicius, Yuan, Lin (b9) 2013; 2013 Claeys, Mertens, D’Asseler, Goethals (b22) 2010; 24 Haque, Gabr, Hasan, George, Arevalo, Zha, Alderman, Jeevarajan, Mas, Zhang (b54) 2019; 10 Häusler, Levine (b57) 2000; 120 Fu, Chang, Chen, Hwang, Wu, Chang, Jan, Chen, Chang (b2) 2009; 67 Walberer, Blaes, Stolz, Müller, Schoenburg, Tschernatsch, Bachmann, Gerriets (b51) 2007; 19 Bederson, Pitts, Tsuji, Nishimura, Davis, Bartkowski (b37) 1986; 17 Yuan, Frank, Hong, An, Eldeniz, Nie, Bunevicius, Shen, Lin (b32) 2013; 44 Rost, Brodtmann, Pase, van Veluw, Biffi, Duering, Hinman, Dichgans (b61) 2022; 130 Fox, Mintun, Reiman, Raichle (b41) 1988; 8 Lou, Zhang, Wang, Wen, Tang, Chen, Yan, Ding (b49) 2007; 146 Zhang, Wu, Ziegler, Guan, Wang, Ge, Schwaiger, Huang, Zuo, Förster (b62) 2017; 146 Ayubcha, Amanullah, Patel, Teichner, Gokhale, Marquez-Valenzuela, Werner, Alavi (b13) 2023; 13 Evans, Tarkin, Buscombe, Markus, Rudd, Warburton (b47) 2017; 13 Dundar, Bold, Agac, Kendi, Friedman (b14) 2019; 14 Shafaat, Sotoudeh (b12) 2023 Yakushev, Hammers, Fellgiebel, Schmidtmann, Scheurich, Buchholz, Peters, Bartenstein, Lieb, Schreckenberger (b26) 2009; 44 Truong, Venna, McCullough, Fitch (b58) 2012; 238 Borghammer, Aanerud, Gjedde (b35) 2009; 46 Penny, Friston, Ashburner, Kiebel, Nichols (b24) 2011 Kleven, Bjerke, Clascá, Groenewegen, Bjaalie, Leergaard (b40) 2023; 20 Borghammer, Chakravarty, Jonsdottir, Sato, Matsuda, Ito, Arahata, Kato, Gjedde (b63) 2010; 214 Gu, Huang, Cheng, Yang, Wu, Sun, Liu, Li, Zhao, Ye (b36) 2022; 148 Barth, Goldberg, Brett, Di (b55) 1995; 678 Balsara, Chapman, Sander, Donahue, Liepert, Castellino, Leevy (b19) 2014 Nie, Liu, Chen, Jiang, Shan (b31) 2014; 9 Küntzelmann, Guenther, Haberkorn, Essig, Giesel, Henze, Schroeter, Schröder, Schönknecht (b27) 2013; 534 Caselles, Kimmel, Sapiro (b67) 1997; 22 Chan, Esedoglu, Nikolova (b66) 2006; 66 Lee, Choi, Park, Kim, Seok (b10) 2021 Lopez-Gonzalez, Silva-Rodríguez, Paredes-Pacheco, Ninerola-Baizan, Efthimiou, Martín-Martín, Moscoso, Ruibal, Roe-Vellve, Aguiar (b33) 2020; 222 Sobrado, Delgado, Fernández-Valle, García-García, Torres, Sánchez-Prieto, Vivancos, Manzanares, Moro, Pozo (b16) 2011; 57 Owjfard, Bigdeli, Safari, Haghani, Namavar (b56) 2021; 30 Dukart, Mueller, Horstmann, Vogt, Frisch, Barthel, Becker, Möller, Villringer, Sabri (b20) 2010; 49 Verger, Doyen, Campion, Guedj (b44) 2021; 11 Dahl, Logothetis, Kayser (b59) 2009; 29 Wang, Mascarenhas, Jia (b60) 2020; 11 Li, Zhao, Zhan, Yang, Feng, Lu, Lei, Zhao, Wang, Zhao (b18) 2020; 176 Boellaard (b21) 2009; 50 Kato, Inui, Nakamura, Ito (b8) 2016; 30 Schmidt, Diederich, Strecker, Geng, Hoppen, Duning, Schäbitz, Minnerup (b53) 2015; 46 Fawcett (b48) 2006; 27 Zimmer, Luxen (b1) 2012; 61 Matthews, Lerman, Lukic, Andrews, Mirelman, Wernick, Giladi, Strother, Evans, Cedarbaum (b6) 2018; 20 Popp, Jaenisch, Witte, Frahm (b46) 2009; 4 Eckert, Barnes, Dhawan, Frucht, Gordon, Feigin, Eidelberg (b5) 2005; 26 Jenkins, Quintana-Ascencio (b50) 2020; 15 Marcoux, Burgos, Bertrand, Teichmann, Routier, Wen, Samper-González, Bottani, Durrleman, Habert (b43) 2018; 12 Andersson (b29) 1997; 6 Whiteley, Tseng, Sandercock (b11) 2008; 39 Marcoux (10.1016/j.compbiomed.2024.109401_b43) 2018; 12 Lee (10.1016/j.compbiomed.2024.109401_b10) 2021 Jenkins (10.1016/j.compbiomed.2024.109401_b50) 2020; 15 Bunevicius (10.1016/j.compbiomed.2024.109401_b9) 2013; 2013 Fox (10.1016/j.compbiomed.2024.109401_b41) 1988; 8 Lourbopoulos (10.1016/j.compbiomed.2024.109401_b52) 2012; 16 Nie (10.1016/j.compbiomed.2024.109401_b31) 2014; 9 Shafaat (10.1016/j.compbiomed.2024.109401_b12) 2023 Dundar (10.1016/j.compbiomed.2024.109401_b14) 2019; 14 Borghammer (10.1016/j.compbiomed.2024.109401_b63) 2010; 214 Mosconi (10.1016/j.compbiomed.2024.109401_b3) 2009; 36 Bederson (10.1016/j.compbiomed.2024.109401_b37) 1986; 17 Haque (10.1016/j.compbiomed.2024.109401_b54) 2019; 10 Truong (10.1016/j.compbiomed.2024.109401_b58) 2012; 238 Rost (10.1016/j.compbiomed.2024.109401_b61) 2022; 130 Dahl (10.1016/j.compbiomed.2024.109401_b59) 2009; 29 Ayubcha (10.1016/j.compbiomed.2024.109401_b13) 2023; 13 Zimmer (10.1016/j.compbiomed.2024.109401_b1) 2012; 61 Popp (10.1016/j.compbiomed.2024.109401_b46) 2009; 4 Berti (10.1016/j.compbiomed.2024.109401_b64) 2012; 56 Chételat (10.1016/j.compbiomed.2024.109401_b4) 2020; 19 Lou (10.1016/j.compbiomed.2024.109401_b49) 2007; 146 Fawcett (10.1016/j.compbiomed.2024.109401_b48) 2006; 27 Evans (10.1016/j.compbiomed.2024.109401_b47) 2017; 13 Schmidt (10.1016/j.compbiomed.2024.109401_b53) 2015; 46 Owjfard (10.1016/j.compbiomed.2024.109401_b56) 2021; 30 Whiteley (10.1016/j.compbiomed.2024.109401_b11) 2008; 39 Borghammer (10.1016/j.compbiomed.2024.109401_b35) 2009; 46 Yakushev (10.1016/j.compbiomed.2024.109401_b26) 2009; 44 Andersson (10.1016/j.compbiomed.2024.109401_b29) 1997; 6 Penny (10.1016/j.compbiomed.2024.109401_b24) 2011 Sobrado (10.1016/j.compbiomed.2024.109401_b16) 2011; 57 Küntzelmann (10.1016/j.compbiomed.2024.109401_b27) 2013; 534 Yuan (10.1016/j.compbiomed.2024.109401_b32) 2013; 44 Claeys (10.1016/j.compbiomed.2024.109401_b22) 2010; 24 Nie (10.1016/j.compbiomed.2024.109401_b28) 2018; 34 Gu (10.1016/j.compbiomed.2024.109401_b36) 2022; 148 Verger (10.1016/j.compbiomed.2024.109401_b44) 2021; 11 Zhang (10.1016/j.compbiomed.2024.109401_b45) 2022; 49 Caselles (10.1016/j.compbiomed.2024.109401_b67) 1997; 22 Kato (10.1016/j.compbiomed.2024.109401_b8) 2016; 30 Balsara (10.1016/j.compbiomed.2024.109401_b19) 2014 Walberer (10.1016/j.compbiomed.2024.109401_b51) 2007; 19 Walberer (10.1016/j.compbiomed.2024.109401_b17) 2012; 43 Nugent (10.1016/j.compbiomed.2024.109401_b42) 2020; 10 Conchou (10.1016/j.compbiomed.2024.109401_b15) 2009; 30 Shivamurthy (10.1016/j.compbiomed.2024.109401_b7) 2015; 204 Häusler (10.1016/j.compbiomed.2024.109401_b57) 2000; 120 Boellaard (10.1016/j.compbiomed.2024.109401_b21) 2009; 50 Jeong (10.1016/j.compbiomed.2024.109401_b25) 2019; 53 Matthews (10.1016/j.compbiomed.2024.109401_b6) 2018; 20 Li (10.1016/j.compbiomed.2024.109401_b18) 2020; 176 He (10.1016/j.compbiomed.2024.109401_b39) 2022; 38 Eckert (10.1016/j.compbiomed.2024.109401_b5) 2005; 26 Dukart (10.1016/j.compbiomed.2024.109401_b20) 2010; 49 Proesmans (10.1016/j.compbiomed.2024.109401_b30) 2021; 8 Wang (10.1016/j.compbiomed.2024.109401_b60) 2020; 11 Fu (10.1016/j.compbiomed.2024.109401_b2) 2009; 67 Lopez-Gonzalez (10.1016/j.compbiomed.2024.109401_b33) 2020; 222 Chan (10.1016/j.compbiomed.2024.109401_b66) 2006; 66 Arnberg (10.1016/j.compbiomed.2024.109401_b34) 2015; 46 Joya (10.1016/j.compbiomed.2024.109401_b65) 2018; 2018 Frackowiak (10.1016/j.compbiomed.2024.109401_b23) 2004 Kleven (10.1016/j.compbiomed.2024.109401_b40) 2023; 20 Barth (10.1016/j.compbiomed.2024.109401_b55) 1995; 678 Wang (10.1016/j.compbiomed.2024.109401_b38) 1998; 28 Zhang (10.1016/j.compbiomed.2024.109401_b62) 2017; 146 |
| References_xml | – start-page: 1 year: 2021 end-page: 9 ident: b10 article-title: Prediction of post-stroke cognitive impairment using brain FDG PET: ?MakeLowercasedeep learning-based approach publication-title: Eur. J. Nuclear Med. Molecular Imaging – volume: 13 start-page: 51 year: 2023 ident: b13 article-title: Stroke and molecular imaging: a focus on FDG-PET publication-title: Am. J. Nuclear Med. Molecular Imaging – volume: 11 start-page: 1 year: 2021 end-page: 7 ident: b44 article-title: The pons as reference region for intensity normalization in semi-quantitative analysis of brain publication-title: EJNMMI Res. – volume: 39 start-page: 2902 year: 2008 end-page: 2909 ident: b11 article-title: Blood biomarkers in the diagnosis of ischemic stroke: A systematic review publication-title: Stroke – volume: 49 start-page: 3073 year: 2022 end-page: 3085 ident: b45 article-title: Unified spatial normalization method of brain PET images using adaptive probabilistic brain atlas publication-title: Eur. J. Nuclear Med. Molecular Imaging – volume: 146 start-page: 589 year: 2017 end-page: 599 ident: b62 article-title: Data-driven identification of intensity normalization region based on longitudinal coherency of 18f-FDG metabolism in the healthy brain publication-title: NeuroImage – volume: 29 start-page: 11924 year: 2009 end-page: 11932 ident: b59 article-title: Spatial organization of multisensory responses in temporal association cortex publication-title: J. Neurosci. – volume: 26 start-page: 912 year: 2005 end-page: 921 ident: b5 article-title: FDG PET in the differential diagnosis of parkinsonian disorders publication-title: NeuroImage – volume: 2013 year: 2013 ident: b9 article-title: The potential roles of 18f-FDG-PET in management of acute stroke patients publication-title: BioMed Res. Int. – volume: 46 start-page: 981 year: 2009 end-page: 988 ident: b35 article-title: Data-driven intensity normalization of PET group comparison studies is superior to global mean normalization publication-title: NeuroImage – volume: 24 start-page: 501 year: 2010 end-page: 505 ident: b22 article-title: Normoglycemic plasma glucose levels affect F-18 FDG uptake in the brain publication-title: Ann. Nucl. Med. – volume: 9 year: 2014 ident: b31 article-title: A statistical parametric mapping toolbox used for voxel-wise analysis of FDG-PET images of rat brain publication-title: PLoS One – volume: 14 start-page: 1447 year: 2019 end-page: 1451 ident: b14 article-title: Stroke detection with 3 different PET tracers publication-title: Radiol. Case Rep. – volume: 11 start-page: 628 year: 2020 end-page: 642 ident: b60 article-title: Positron emission tomography after ischemic brain injury: MakeLowercasecurrent challenges and future developments publication-title: Translat. Stroke Res. – volume: 30 start-page: 2542 year: 2009 end-page: 2557 ident: b15 article-title: Neural substrates of low-frequency repetitive transcranial magnetic stimulation during movement in healthy subjects and acute stroke patients. a PET study publication-title: Hum. Brain Mapp. – volume: 28 start-page: 13 year: 1998 end-page: 25 ident: b38 article-title: Performance evaluation of filtered backprojection reconstruction and iterative reconstruction methods for PET images publication-title: Comput. Biol. Med. – volume: 4 year: 2009 ident: b46 article-title: Identification of ischemic regions in a rat model of stroke publication-title: PLoS One – year: 2023 ident: b12 article-title: Stroke imaging publication-title: StatPearls [Internet] – volume: 222 year: 2020 ident: b33 article-title: Intensity normalization methods in brain FDG-PET quantification publication-title: NeuroImage – volume: 6 start-page: 237 year: 1997 end-page: 244 ident: b29 article-title: How to estimate global activity independent of changes in local activity publication-title: NeuroImage – volume: 38 start-page: 1057 year: 2022 end-page: 1068 ident: b39 article-title: Feature-based quality assessment of middle cerebral artery occlusion using 18f-fluorodeoxyglucose positron emission tomography publication-title: Neurosci. Bull. – volume: 20 start-page: 1822 year: 2023 end-page: 1829 ident: b40 article-title: Waxholm space atlas of the rat brain: A 3D atlas supporting data analysis and integration publication-title: Nat. Mthods – volume: 2018 year: 2018 ident: b65 article-title: Pet imaging of crossed cerebellar diaschisis after long-term cerebral ischemia in rats publication-title: Contrast Media & Molecular Imaging – volume: 8 start-page: 642 year: 1988 end-page: 653 ident: b41 article-title: Enhanced detection of focal brain responses using intersubject averaging and change-distribution analysis of subtracted [pet] images publication-title: J. Cerebral Blood Flow Metabolism – volume: 534 start-page: 12 year: 2013 end-page: 17 ident: b27 article-title: Impaired cerebral glucose metabolism in prodromal alzheimer’s disease differs by regional intensity normalization publication-title: Neurosci. Lett. – volume: 56 start-page: 299 year: 2012 ident: b64 article-title: Alternative normalization methods demonstrate widespread cortical hypometabolism in untreated de novo parkinson’s disease publication-title: Q. J. Nuclear Med. Molecular Imaging – volume: 66 start-page: 1632 year: 2006 end-page: 1648 ident: b66 article-title: Algorithms for finding global minimizers of image segmentation and denoising models publication-title: SIAM J. Appl. Math. – volume: 148 year: 2022 ident: b36 article-title: A multiomics and network pharmacological study reveals the neuroprotective efficacy of Fu-Fang-Dan-Zhi tablets against glutamate-induced oxidative cell death publication-title: Comput. Biol. Med. – volume: 15 year: 2020 ident: b50 article-title: A solution to minimum sample size for regressions publication-title: PloS One – volume: 44 start-page: 43 year: 2009 end-page: 50 ident: b26 article-title: SPM-based count normalization provides excellent discrimination of mild alzheimer’s disease and amnestic mild cognitive impairment from healthy aging publication-title: NeuroImage – volume: 16 start-page: 51 year: 2012 ident: b52 article-title: Stereotactic coordinates for intracerebroventricular infusion after permanent focal cerebral ischemia in wistar rats publication-title: Hippokratia – volume: 120 start-page: 689 year: 2000 end-page: 703 ident: b57 article-title: Auditory dysfunction in stroke publication-title: Acta Oto-laryngol. – volume: 34 start-page: 833 year: 2018 end-page: 841 ident: b28 article-title: An automatic method for generating an unbiased intensity normalizing factor in positron emission tomography image analysis after stroke publication-title: Neurosci. Bull. – volume: 17 start-page: 472 year: 1986 end-page: 476 ident: b37 article-title: Rat middle cerebral artery occlusion: evaluation of the model and development of a neurologic examination publication-title: Stroke – volume: 57 start-page: 45 year: 2011 end-page: 54 ident: b16 article-title: Longitudinal studies of ischemic penumbra by using 18f-FDG PET and MRI techniques in permanent and transient focal cerebral ischemia in rats publication-title: NeuroImage – volume: 20 start-page: 572 year: 2018 end-page: 579 ident: b6 article-title: FDG PET Parkinson’s disease-related pattern as a biomarker for clinical trials in early stage disease publication-title: NeuroImage: Clinical – volume: 49 start-page: 1490 year: 2010 end-page: 1495 ident: b20 article-title: Differential effects of global and cerebellar normalization on detection and differentiation of dementia in FDG-PET studies publication-title: NeuroImage – volume: 146 start-page: 555 year: 2007 end-page: 561 ident: b49 article-title: Hyperbaric oxygen treatment attenuated the decrease in regional glucose metabolism of rats subjected to focal cerebral ischemia: MakeLowercasea high resolution positron emission tomography study publication-title: Neuroscience – volume: 10 start-page: 154 year: 2019 ident: b54 article-title: Ongoing secondary degeneration of the limbic system in patients with ischemic stroke: MakeLowercasea longitudinal MRI study publication-title: Front. Neurol. – volume: 130 start-page: 1252 year: 2022 end-page: 1271 ident: b61 article-title: Post-stroke cognitive impairment and dementia publication-title: Circ. Res. – volume: 30 start-page: 73 year: 2016 end-page: 84 ident: b8 article-title: Brain fluorodeoxyglucose (FDG) PET in dementia publication-title: Ageing Res. Rev. – year: 2014 ident: b19 article-title: Non-invasive imaging and analysis of cerebral ischemia in living rats using positron emission tomography with 18f-FDG publication-title: JoVE (J. Visualiz. Exper.) – volume: 238 start-page: 114 year: 2012 end-page: 121 ident: b58 article-title: Deficits in auditory, cognitive, and motor processing following reversible middle cerebral artery occlusion in mice publication-title: Exp. Neurol. – volume: 46 start-page: 1127 year: 2015 end-page: 1131 ident: b53 article-title: Progressive cognitive deficits in a mouse model of recurrent photothrombotic stroke publication-title: Stroke – volume: 43 start-page: 193 year: 2012 end-page: 198 ident: b17 article-title: Potential of early [18F]-2-fluoro-2-deoxy-d-glucose positron emission tomography for identifying hypoperfusion and predicting fate of tissue in a rat embolic stroke model publication-title: Stroke – volume: 44 start-page: 2292 year: 2013 end-page: 2299 ident: b32 article-title: Spatiotemporal uptake characteristics of [18] F-2-fluoro-2-deoxy-d-glucose in a rat middle cerebral artery occlusion model publication-title: Stroke – volume: 27 start-page: 861 year: 2006 end-page: 874 ident: b48 article-title: An introduction to ROC analysis publication-title: Pattern Recognit. Lett. – volume: 22 start-page: 61 year: 1997 end-page: 79 ident: b67 article-title: Geodesic active contours publication-title: Int. J. Comput. Vis. – volume: 678 start-page: 177 year: 1995 end-page: 190 ident: b55 article-title: The spatiotemporal organization of auditory, visual, and auditory-visual evoked potentials in rat cortex publication-title: Brain Res. – volume: 36 start-page: 811 year: 2009 end-page: 822 ident: b3 article-title: FDG-PET changes in brain glucose metabolism from normal cognition to pathologically verified alzheimer’s disease publication-title: Eur. J. Nuclear Med. Molecular Imaging – volume: 214 start-page: 303 year: 2010 end-page: 317 ident: b63 article-title: Cortical hypometabolism and hypoperfusion in parkinson’s disease is extensive: MakeLowercaseprobably even at early disease stages publication-title: Brain Struct. Function – volume: 19 start-page: 105 year: 2007 end-page: 110 ident: b51 article-title: Midline-shift corresponds to the amount of brain edema early after hemispheric stroke—an MRI study in rats publication-title: J. Neurosurg. Anesthesiol. – volume: 176 year: 2020 ident: b18 article-title: Enhanced white matter reorganization and activated brain glucose metabolism by enriched environment following ischemic stroke: Micro PET/CT and MRI study publication-title: Neuropharmacology – volume: 204 start-page: W76 year: 2015 end-page: W85 ident: b7 article-title: Brain FDG PET and the diagnosis of dementia publication-title: Am. J. Roentgenol. – volume: 19 start-page: 951 year: 2020 end-page: 962 ident: b4 article-title: Amyloid-PET and 18f-FDG-PET in the diagnostic investigation of alzheimer’s disease and other dementias publication-title: Lancet Neurol. – volume: 12 start-page: 94 year: 2018 ident: b43 article-title: An automated pipeline for the analysis of PET data on the cortical surface publication-title: Front. Neuroinform. – volume: 8 year: 2021 ident: b30 article-title: Voxel-based analysis of [18f]-FDG brain PET in rats using data-driven normalization publication-title: Front. Med. – start-page: 725 year: 2004 end-page: 760 ident: b23 article-title: Chapter 37 - the general linear model publication-title: Human Brain Function – volume: 13 start-page: 676 year: 2017 end-page: 688 ident: b47 article-title: PET imaging of the neurovascular interface in cerebrovascular disease publication-title: Nat. Rev. Neurol. – volume: 30 year: 2021 ident: b56 article-title: Effect of dimethyl fumarate on the motor function and spatial arrangement of primary motor cortical neurons in the sub-acute phase of stroke in a rat model publication-title: J. Stroke Cerebrovascular Diseases – volume: 53 start-page: 328 year: 2019 end-page: 333 ident: b25 article-title: Correlation of early-phase F-18 florapronal PET with F-18 FDG PET in alzheimer’s disease and normal brain publication-title: Nuclear Med. Molecular Imaging – volume: 46 start-page: 835 year: 2015 end-page: 842 ident: b34 article-title: Imaging of a clinically relevant stroke model: ?MakeLowercaseglucose hypermetabolism revisited publication-title: Stroke – volume: 61 start-page: 363 year: 2012 end-page: 370 ident: b1 article-title: PET radiotracers for molecular imaging in the brain: past, present and future publication-title: NeuroImage – volume: 50 start-page: 11S year: 2009 end-page: 20S ident: b21 article-title: Standards for PET image acquisition and quantitative data analysis publication-title: J. Nucl. Med. – year: 2011 ident: b24 article-title: Statistical parametric mapping: the analysis of functional brain images – volume: 67 start-page: 1743 year: 2009 end-page: 1747 ident: b2 article-title: Imaging of regional metabolic activity by 18F-FDG/PET in rats with transient cerebral ischemia publication-title: Appl. Radiat. Isot. – volume: 10 start-page: 9261 year: 2020 ident: b42 article-title: Selection of the optimal intensity normalization region for FDG-PET studies of normal aging and alzheimer’s disease publication-title: Sci. Rep. – volume: 27 start-page: 861 issue: 8 year: 2006 ident: 10.1016/j.compbiomed.2024.109401_b48 article-title: An introduction to ROC analysis publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2005.10.010 – volume: 20 start-page: 1822 issue: 11 year: 2023 ident: 10.1016/j.compbiomed.2024.109401_b40 article-title: Waxholm space atlas of the rat brain: A 3D atlas supporting data analysis and integration publication-title: Nat. Mthods doi: 10.1038/s41592-023-02034-3 – volume: 15 issue: 2 year: 2020 ident: 10.1016/j.compbiomed.2024.109401_b50 article-title: A solution to minimum sample size for regressions publication-title: PloS One doi: 10.1371/journal.pone.0229345 – volume: 22 start-page: 61 issue: 1 year: 1997 ident: 10.1016/j.compbiomed.2024.109401_b67 article-title: Geodesic active contours publication-title: Int. J. Comput. Vis. doi: 10.1023/A:1007979827043 – volume: 120 start-page: 689 issue: 6 year: 2000 ident: 10.1016/j.compbiomed.2024.109401_b57 article-title: Auditory dysfunction in stroke publication-title: Acta Oto-laryngol. doi: 10.1080/000164800750000207 – volume: 46 start-page: 835 issue: 3 year: 2015 ident: 10.1016/j.compbiomed.2024.109401_b34 article-title: Imaging of a clinically relevant stroke model: ?MakeLowercaseglucose hypermetabolism revisited publication-title: Stroke doi: 10.1161/STROKEAHA.114.008407 – volume: 19 start-page: 105 issue: 2 year: 2007 ident: 10.1016/j.compbiomed.2024.109401_b51 article-title: Midline-shift corresponds to the amount of brain edema early after hemispheric stroke—an MRI study in rats publication-title: J. Neurosurg. Anesthesiol. doi: 10.1097/ANA.0b013e31802c7e33 – volume: 678 start-page: 177 issue: 1–2 year: 1995 ident: 10.1016/j.compbiomed.2024.109401_b55 article-title: The spatiotemporal organization of auditory, visual, and auditory-visual evoked potentials in rat cortex publication-title: Brain Res. doi: 10.1016/0006-8993(95)00182-P – volume: 56 start-page: 299 issue: 3 year: 2012 ident: 10.1016/j.compbiomed.2024.109401_b64 article-title: Alternative normalization methods demonstrate widespread cortical hypometabolism in untreated de novo parkinson’s disease publication-title: Q. J. Nuclear Med. Molecular Imaging – volume: 57 start-page: 45 issue: 1 year: 2011 ident: 10.1016/j.compbiomed.2024.109401_b16 article-title: Longitudinal studies of ischemic penumbra by using 18f-FDG PET and MRI techniques in permanent and transient focal cerebral ischemia in rats publication-title: NeuroImage doi: 10.1016/j.neuroimage.2011.04.045 – volume: 53 start-page: 328 year: 2019 ident: 10.1016/j.compbiomed.2024.109401_b25 article-title: Correlation of early-phase F-18 florapronal PET with F-18 FDG PET in alzheimer’s disease and normal brain publication-title: Nuclear Med. Molecular Imaging doi: 10.1007/s13139-019-00612-y – volume: 30 start-page: 73 year: 2016 ident: 10.1016/j.compbiomed.2024.109401_b8 article-title: Brain fluorodeoxyglucose (FDG) PET in dementia publication-title: Ageing Res. Rev. doi: 10.1016/j.arr.2016.02.003 – volume: 12 start-page: 94 year: 2018 ident: 10.1016/j.compbiomed.2024.109401_b43 article-title: An automated pipeline for the analysis of PET data on the cortical surface publication-title: Front. Neuroinform. doi: 10.3389/fninf.2018.00094 – volume: 11 start-page: 1 year: 2021 ident: 10.1016/j.compbiomed.2024.109401_b44 article-title: The pons as reference region for intensity normalization in semi-quantitative analysis of brain 18FDG PET: application to metabolic changes related to ageing in conventional and digital control databases publication-title: EJNMMI Res. doi: 10.1186/s13550-021-00771-0 – volume: 6 start-page: 237 issue: 4 year: 1997 ident: 10.1016/j.compbiomed.2024.109401_b29 article-title: How to estimate global activity independent of changes in local activity publication-title: NeuroImage doi: 10.1006/nimg.1997.0302 – volume: 2018 year: 2018 ident: 10.1016/j.compbiomed.2024.109401_b65 article-title: Pet imaging of crossed cerebellar diaschisis after long-term cerebral ischemia in rats publication-title: Contrast Media & Molecular Imaging doi: 10.1155/2018/2483078 – volume: 46 start-page: 1127 issue: 4 year: 2015 ident: 10.1016/j.compbiomed.2024.109401_b53 article-title: Progressive cognitive deficits in a mouse model of recurrent photothrombotic stroke publication-title: Stroke doi: 10.1161/STROKEAHA.115.008905 – volume: 8 year: 2021 ident: 10.1016/j.compbiomed.2024.109401_b30 article-title: Voxel-based analysis of [18f]-FDG brain PET in rats using data-driven normalization publication-title: Front. Med. doi: 10.3389/fmed.2021.744157 – volume: 29 start-page: 11924 issue: 38 year: 2009 ident: 10.1016/j.compbiomed.2024.109401_b59 article-title: Spatial organization of multisensory responses in temporal association cortex publication-title: J. Neurosci. doi: 10.1523/JNEUROSCI.3437-09.2009 – volume: 50 start-page: 11S issue: Suppl 1 year: 2009 ident: 10.1016/j.compbiomed.2024.109401_b21 article-title: Standards for PET image acquisition and quantitative data analysis publication-title: J. Nucl. Med. doi: 10.2967/jnumed.108.057182 – volume: 9 issue: 9 year: 2014 ident: 10.1016/j.compbiomed.2024.109401_b31 article-title: A statistical parametric mapping toolbox used for voxel-wise analysis of FDG-PET images of rat brain publication-title: PLoS One doi: 10.1371/journal.pone.0108295 – volume: 148 year: 2022 ident: 10.1016/j.compbiomed.2024.109401_b36 article-title: A multiomics and network pharmacological study reveals the neuroprotective efficacy of Fu-Fang-Dan-Zhi tablets against glutamate-induced oxidative cell death publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2022.105873 – volume: 4 issue: 3 year: 2009 ident: 10.1016/j.compbiomed.2024.109401_b46 article-title: Identification of ischemic regions in a rat model of stroke publication-title: PLoS One doi: 10.1371/journal.pone.0004764 – volume: 238 start-page: 114 issue: 2 year: 2012 ident: 10.1016/j.compbiomed.2024.109401_b58 article-title: Deficits in auditory, cognitive, and motor processing following reversible middle cerebral artery occlusion in mice publication-title: Exp. Neurol. doi: 10.1016/j.expneurol.2012.08.011 – volume: 214 start-page: 303 year: 2010 ident: 10.1016/j.compbiomed.2024.109401_b63 article-title: Cortical hypometabolism and hypoperfusion in parkinson’s disease is extensive: MakeLowercaseprobably even at early disease stages publication-title: Brain Struct. Function doi: 10.1007/s00429-010-0246-0 – volume: 26 start-page: 912 issue: 3 year: 2005 ident: 10.1016/j.compbiomed.2024.109401_b5 article-title: FDG PET in the differential diagnosis of parkinsonian disorders publication-title: NeuroImage doi: 10.1016/j.neuroimage.2005.03.012 – volume: 28 start-page: 13 issue: 1 year: 1998 ident: 10.1016/j.compbiomed.2024.109401_b38 article-title: Performance evaluation of filtered backprojection reconstruction and iterative reconstruction methods for PET images publication-title: Comput. Biol. Med. doi: 10.1016/S0010-4825(97)00031-0 – volume: 146 start-page: 589 year: 2017 ident: 10.1016/j.compbiomed.2024.109401_b62 article-title: Data-driven identification of intensity normalization region based on longitudinal coherency of 18f-FDG metabolism in the healthy brain publication-title: NeuroImage doi: 10.1016/j.neuroimage.2016.09.031 – volume: 2013 year: 2013 ident: 10.1016/j.compbiomed.2024.109401_b9 article-title: The potential roles of 18f-FDG-PET in management of acute stroke patients publication-title: BioMed Res. Int. doi: 10.1155/2013/634598 – volume: 34 start-page: 833 year: 2018 ident: 10.1016/j.compbiomed.2024.109401_b28 article-title: An automatic method for generating an unbiased intensity normalizing factor in positron emission tomography image analysis after stroke publication-title: Neurosci. Bull. doi: 10.1007/s12264-018-0240-8 – volume: 204 start-page: W76 issue: 1 year: 2015 ident: 10.1016/j.compbiomed.2024.109401_b7 article-title: Brain FDG PET and the diagnosis of dementia publication-title: Am. J. Roentgenol. doi: 10.2214/AJR.13.12363 – volume: 534 start-page: 12 year: 2013 ident: 10.1016/j.compbiomed.2024.109401_b27 article-title: Impaired cerebral glucose metabolism in prodromal alzheimer’s disease differs by regional intensity normalization publication-title: Neurosci. Lett. doi: 10.1016/j.neulet.2012.11.026 – volume: 10 start-page: 154 year: 2019 ident: 10.1016/j.compbiomed.2024.109401_b54 article-title: Ongoing secondary degeneration of the limbic system in patients with ischemic stroke: MakeLowercasea longitudinal MRI study publication-title: Front. Neurol. doi: 10.3389/fneur.2019.00154 – volume: 43 start-page: 193 issue: 1 year: 2012 ident: 10.1016/j.compbiomed.2024.109401_b17 article-title: Potential of early [18F]-2-fluoro-2-deoxy-d-glucose positron emission tomography for identifying hypoperfusion and predicting fate of tissue in a rat embolic stroke model publication-title: Stroke doi: 10.1161/STROKEAHA.111.624551 – volume: 16 start-page: 51 issue: 1 year: 2012 ident: 10.1016/j.compbiomed.2024.109401_b52 article-title: Stereotactic coordinates for intracerebroventricular infusion after permanent focal cerebral ischemia in wistar rats publication-title: Hippokratia – volume: 46 start-page: 981 issue: 4 year: 2009 ident: 10.1016/j.compbiomed.2024.109401_b35 article-title: Data-driven intensity normalization of PET group comparison studies is superior to global mean normalization publication-title: NeuroImage doi: 10.1016/j.neuroimage.2009.03.021 – volume: 20 start-page: 572 year: 2018 ident: 10.1016/j.compbiomed.2024.109401_b6 article-title: FDG PET Parkinson’s disease-related pattern as a biomarker for clinical trials in early stage disease publication-title: NeuroImage: Clinical doi: 10.1016/j.nicl.2018.08.006 – volume: 30 start-page: 2542 issue: 8 year: 2009 ident: 10.1016/j.compbiomed.2024.109401_b15 article-title: Neural substrates of low-frequency repetitive transcranial magnetic stimulation during movement in healthy subjects and acute stroke patients. a PET study publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.20690 – volume: 176 year: 2020 ident: 10.1016/j.compbiomed.2024.109401_b18 article-title: Enhanced white matter reorganization and activated brain glucose metabolism by enriched environment following ischemic stroke: Micro PET/CT and MRI study publication-title: Neuropharmacology doi: 10.1016/j.neuropharm.2020.108202 – volume: 44 start-page: 43 issue: 1 year: 2009 ident: 10.1016/j.compbiomed.2024.109401_b26 article-title: SPM-based count normalization provides excellent discrimination of mild alzheimer’s disease and amnestic mild cognitive impairment from healthy aging publication-title: NeuroImage doi: 10.1016/j.neuroimage.2008.07.015 – volume: 39 start-page: 2902 issue: 10 year: 2008 ident: 10.1016/j.compbiomed.2024.109401_b11 article-title: Blood biomarkers in the diagnosis of ischemic stroke: A systematic review publication-title: Stroke doi: 10.1161/STROKEAHA.107.511261 – volume: 19 start-page: 951 issue: 11 year: 2020 ident: 10.1016/j.compbiomed.2024.109401_b4 article-title: Amyloid-PET and 18f-FDG-PET in the diagnostic investigation of alzheimer’s disease and other dementias publication-title: Lancet Neurol. doi: 10.1016/S1474-4422(20)30314-8 – volume: 66 start-page: 1632 issue: 5 year: 2006 ident: 10.1016/j.compbiomed.2024.109401_b66 article-title: Algorithms for finding global minimizers of image segmentation and denoising models publication-title: SIAM J. Appl. Math. doi: 10.1137/040615286 – volume: 222 year: 2020 ident: 10.1016/j.compbiomed.2024.109401_b33 article-title: Intensity normalization methods in brain FDG-PET quantification publication-title: NeuroImage doi: 10.1016/j.neuroimage.2020.117229 – issue: 94 year: 2014 ident: 10.1016/j.compbiomed.2024.109401_b19 article-title: Non-invasive imaging and analysis of cerebral ischemia in living rats using positron emission tomography with 18f-FDG publication-title: JoVE (J. Visualiz. Exper.) – volume: 11 start-page: 628 year: 2020 ident: 10.1016/j.compbiomed.2024.109401_b60 article-title: Positron emission tomography after ischemic brain injury: MakeLowercasecurrent challenges and future developments publication-title: Translat. Stroke Res. doi: 10.1007/s12975-019-00765-0 – start-page: 1 year: 2021 ident: 10.1016/j.compbiomed.2024.109401_b10 article-title: Prediction of post-stroke cognitive impairment using brain FDG PET: ?MakeLowercasedeep learning-based approach publication-title: Eur. J. Nuclear Med. Molecular Imaging – volume: 13 start-page: 676 issue: 11 year: 2017 ident: 10.1016/j.compbiomed.2024.109401_b47 article-title: PET imaging of the neurovascular interface in cerebrovascular disease publication-title: Nat. Rev. Neurol. doi: 10.1038/nrneurol.2017.129 – start-page: 725 year: 2004 ident: 10.1016/j.compbiomed.2024.109401_b23 article-title: Chapter 37 - the general linear model – volume: 13 start-page: 51 issue: 2 year: 2023 ident: 10.1016/j.compbiomed.2024.109401_b13 article-title: Stroke and molecular imaging: a focus on FDG-PET publication-title: Am. J. Nuclear Med. Molecular Imaging – volume: 130 start-page: 1252 issue: 8 year: 2022 ident: 10.1016/j.compbiomed.2024.109401_b61 article-title: Post-stroke cognitive impairment and dementia publication-title: Circ. Res. doi: 10.1161/CIRCRESAHA.122.319951 – volume: 67 start-page: 1743 issue: 10 year: 2009 ident: 10.1016/j.compbiomed.2024.109401_b2 article-title: Imaging of regional metabolic activity by 18F-FDG/PET in rats with transient cerebral ischemia publication-title: Appl. Radiat. Isot. doi: 10.1016/j.apradiso.2009.03.002 – volume: 38 start-page: 1057 issue: 9 year: 2022 ident: 10.1016/j.compbiomed.2024.109401_b39 article-title: Feature-based quality assessment of middle cerebral artery occlusion using 18f-fluorodeoxyglucose positron emission tomography publication-title: Neurosci. Bull. doi: 10.1007/s12264-022-00865-2 – volume: 30 issue: 4 year: 2021 ident: 10.1016/j.compbiomed.2024.109401_b56 article-title: Effect of dimethyl fumarate on the motor function and spatial arrangement of primary motor cortical neurons in the sub-acute phase of stroke in a rat model publication-title: J. Stroke Cerebrovascular Diseases doi: 10.1016/j.jstrokecerebrovasdis.2021.105630 – volume: 61 start-page: 363 issue: 2 year: 2012 ident: 10.1016/j.compbiomed.2024.109401_b1 article-title: PET radiotracers for molecular imaging in the brain: past, present and future publication-title: NeuroImage doi: 10.1016/j.neuroimage.2011.12.037 – year: 2011 ident: 10.1016/j.compbiomed.2024.109401_b24 – volume: 49 start-page: 1490 issue: 2 year: 2010 ident: 10.1016/j.compbiomed.2024.109401_b20 article-title: Differential effects of global and cerebellar normalization on detection and differentiation of dementia in FDG-PET studies publication-title: NeuroImage doi: 10.1016/j.neuroimage.2009.09.017 – volume: 36 start-page: 811 year: 2009 ident: 10.1016/j.compbiomed.2024.109401_b3 article-title: FDG-PET changes in brain glucose metabolism from normal cognition to pathologically verified alzheimer’s disease publication-title: Eur. J. Nuclear Med. Molecular Imaging doi: 10.1007/s00259-008-1039-z – volume: 17 start-page: 472 issue: 3 year: 1986 ident: 10.1016/j.compbiomed.2024.109401_b37 article-title: Rat middle cerebral artery occlusion: evaluation of the model and development of a neurologic examination publication-title: Stroke doi: 10.1161/01.STR.17.3.472 – volume: 146 start-page: 555 issue: 2 year: 2007 ident: 10.1016/j.compbiomed.2024.109401_b49 article-title: Hyperbaric oxygen treatment attenuated the decrease in regional glucose metabolism of rats subjected to focal cerebral ischemia: MakeLowercasea high resolution positron emission tomography study publication-title: Neuroscience doi: 10.1016/j.neuroscience.2007.01.046 – volume: 49 start-page: 3073 issue: 9 year: 2022 ident: 10.1016/j.compbiomed.2024.109401_b45 article-title: Unified spatial normalization method of brain PET images using adaptive probabilistic brain atlas publication-title: Eur. J. Nuclear Med. Molecular Imaging doi: 10.1007/s00259-022-05752-6 – volume: 24 start-page: 501 year: 2010 ident: 10.1016/j.compbiomed.2024.109401_b22 article-title: Normoglycemic plasma glucose levels affect F-18 FDG uptake in the brain publication-title: Ann. Nucl. Med. doi: 10.1007/s12149-010-0359-9 – volume: 44 start-page: 2292 issue: 8 year: 2013 ident: 10.1016/j.compbiomed.2024.109401_b32 article-title: Spatiotemporal uptake characteristics of [18] F-2-fluoro-2-deoxy-d-glucose in a rat middle cerebral artery occlusion model publication-title: Stroke doi: 10.1161/STROKEAHA.113.000903 – volume: 8 start-page: 642 issue: 5 year: 1988 ident: 10.1016/j.compbiomed.2024.109401_b41 article-title: Enhanced detection of focal brain responses using intersubject averaging and change-distribution analysis of subtracted [pet] images publication-title: J. Cerebral Blood Flow Metabolism doi: 10.1038/jcbfm.1988.111 – volume: 14 start-page: 1447 issue: 11 year: 2019 ident: 10.1016/j.compbiomed.2024.109401_b14 article-title: Stroke detection with 3 different PET tracers publication-title: Radiol. Case Rep. doi: 10.1016/j.radcr.2019.09.005 – year: 2023 ident: 10.1016/j.compbiomed.2024.109401_b12 article-title: Stroke imaging – volume: 10 start-page: 9261 issue: 1 year: 2020 ident: 10.1016/j.compbiomed.2024.109401_b42 article-title: Selection of the optimal intensity normalization region for FDG-PET studies of normal aging and alzheimer’s disease publication-title: Sci. Rep. doi: 10.1038/s41598-020-65957-3 |
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| Snippet | In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce inter-subject... AbstractBackground:In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to... Background:In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce... |
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| SubjectTerms | Animals Brain - diagnostic imaging Brain injury Cerebral blood flow Data analysis Datasets Emission analysis FDG-PET Fluorodeoxyglucose F18 Hemispheres Identification methods Image Processing, Computer-Assisted - methods Infarction, Middle Cerebral Artery - diagnostic imaging Injury analysis Intensity normalization Internal Medicine Ischemia Linear regression Linearity Male MCAO Medical imaging Methods Occlusion Other Positron emission Positron emission tomography Positron-Emission Tomography - methods Rats Rats, Sprague-Dawley Regression models Similarity coefficient method Stroke Stroke - diagnostic imaging Triphenyltetrazolium chloride |
| Title | Using the coefficient of determination to identify injury regions after stroke in pre-clinical FDG-PET images |
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