BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis
•A model of synthesizing expiratory CT images from inspiratory images is developed.•A CNN-Transformer network with a global context injection module is introduced.•GOLD stage is incorporated to guide the model in capturing the disease severity.•The model achieves a mean absolute error of 78.207 HU a...
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| Vydané v: | Computer methods and programs in biomedicine Ročník 259; s. 108516 |
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| Hlavní autori: | , , , , , , , , , |
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Ireland
Elsevier B.V
01.02.2025
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| ISSN: | 0169-2607, 1872-7565, 1872-7565 |
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| Abstract | •A model of synthesizing expiratory CT images from inspiratory images is developed.•A CNN-Transformer network with a global context injection module is introduced.•GOLD stage is incorporated to guide the model in capturing the disease severity.•The model achieves a mean absolute error of 78.207 HU and outperforms other models.•Predicted parametric response mapping can quantify functional small airway disease.•Predicted voxel distribution maps can aid in COPD phenotyping and classification.
Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, due to concerns about radiation exposure and cost, expiratory CT is not routinely performed. Recent work on synthesizing expiratory CT has primarily focused on imaging features while neglecting patient-specific pulmonary function.
To address these issues, we developed a novel model named BreathVisionNet that incorporates pulmonary function data to guide the synthesis of expiratory CT from inspiratory CT. An architecture combining a convolutional neural network and transformer is introduced to leverage the irregular phenotypic distribution in COPD patients. The model can better understand the long-range and global contexts by incorporating global information into the encoder. The utilization of edge information and multi-view data further enhances the quality of the synthesized CT. Parametric response mapping (PRM) can be estimated by using synthesized expiratory CT and inspiratory CT to quantify COPD phenotypes of the normal, emphysema, and functional small airway disease (fSAD), including their percentages, spatial distributions, and voxel distribution maps.
BreathVisionNet outperforms other generative models in terms of synthesized image quality. It achieves a mean absolute error, normalized mean square error, structural similarity index and peak signal-to-noise ratio of 78.207 HU, 0.643, 0.847 and 25.828 dB, respectively. Comparing the predicted and real PRM, the Dice coefficient can reach 0.732 (emphysema) and 0.560 (fSAD). The mean of differences between true and predicted fSAD percentage is 4.42 for the development dataset (low radiation dose CT scans), and 9.05 for an independent external validation dataset (routine dose), indicating that model has great generalizability. A classifier trained on voxel distribution maps can achieve an accuracy of 0.891 in predicting the presence of COPD.
BreathVisionNet can accurately synthesize expiratory CT images from inspiratory CT and predict their voxel distribution. The estimated PRM can help to quantify COPD phenotypes of the normal, emphysema, and fSAD. This capability provides additional insights into COPD diversity while only inspiratory CT images are available. |
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| AbstractList | •A model of synthesizing expiratory CT images from inspiratory images is developed.•A CNN-Transformer network with a global context injection module is introduced.•GOLD stage is incorporated to guide the model in capturing the disease severity.•The model achieves a mean absolute error of 78.207 HU and outperforms other models.•Predicted parametric response mapping can quantify functional small airway disease.•Predicted voxel distribution maps can aid in COPD phenotyping and classification.
Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, due to concerns about radiation exposure and cost, expiratory CT is not routinely performed. Recent work on synthesizing expiratory CT has primarily focused on imaging features while neglecting patient-specific pulmonary function.
To address these issues, we developed a novel model named BreathVisionNet that incorporates pulmonary function data to guide the synthesis of expiratory CT from inspiratory CT. An architecture combining a convolutional neural network and transformer is introduced to leverage the irregular phenotypic distribution in COPD patients. The model can better understand the long-range and global contexts by incorporating global information into the encoder. The utilization of edge information and multi-view data further enhances the quality of the synthesized CT. Parametric response mapping (PRM) can be estimated by using synthesized expiratory CT and inspiratory CT to quantify COPD phenotypes of the normal, emphysema, and functional small airway disease (fSAD), including their percentages, spatial distributions, and voxel distribution maps.
BreathVisionNet outperforms other generative models in terms of synthesized image quality. It achieves a mean absolute error, normalized mean square error, structural similarity index and peak signal-to-noise ratio of 78.207 HU, 0.643, 0.847 and 25.828 dB, respectively. Comparing the predicted and real PRM, the Dice coefficient can reach 0.732 (emphysema) and 0.560 (fSAD). The mean of differences between true and predicted fSAD percentage is 4.42 for the development dataset (low radiation dose CT scans), and 9.05 for an independent external validation dataset (routine dose), indicating that model has great generalizability. A classifier trained on voxel distribution maps can achieve an accuracy of 0.891 in predicting the presence of COPD.
BreathVisionNet can accurately synthesize expiratory CT images from inspiratory CT and predict their voxel distribution. The estimated PRM can help to quantify COPD phenotypes of the normal, emphysema, and fSAD. This capability provides additional insights into COPD diversity while only inspiratory CT images are available. Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, due to concerns about radiation exposure and cost, expiratory CT is not routinely performed. Recent work on synthesizing expiratory CT has primarily focused on imaging features while neglecting patient-specific pulmonary function. To address these issues, we developed a novel model named BreathVisionNet that incorporates pulmonary function data to guide the synthesis of expiratory CT from inspiratory CT. An architecture combining a convolutional neural network and transformer is introduced to leverage the irregular phenotypic distribution in COPD patients. The model can better understand the long-range and global contexts by incorporating global information into the encoder. The utilization of edge information and multi-view data further enhances the quality of the synthesized CT. Parametric response mapping (PRM) can be estimated by using synthesized expiratory CT and inspiratory CT to quantify COPD phenotypes of the normal, emphysema, and functional small airway disease (fSAD), including their percentages, spatial distributions, and voxel distribution maps. BreathVisionNet outperforms other generative models in terms of synthesized image quality. It achieves a mean absolute error, normalized mean square error, structural similarity index and peak signal-to-noise ratio of 78.207 HU, 0.643, 0.847 and 25.828 dB, respectively. Comparing the predicted and real PRM, the Dice coefficient can reach 0.732 (emphysema) and 0.560 (fSAD). The mean of differences between true and predicted fSAD percentage is 4.42 for the development dataset (low radiation dose CT scans), and 9.05 for an independent external validation dataset (routine dose), indicating that model has great generalizability. A classifier trained on voxel distribution maps can achieve an accuracy of 0.891 in predicting the presence of COPD. BreathVisionNet can accurately synthesize expiratory CT images from inspiratory CT and predict their voxel distribution. The estimated PRM can help to quantify COPD phenotypes of the normal, emphysema, and fSAD. This capability provides additional insights into COPD diversity while only inspiratory CT images are available. Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, due to concerns about radiation exposure and cost, expiratory CT is not routinely performed. Recent work on synthesizing expiratory CT has primarily focused on imaging features while neglecting patient-specific pulmonary function.BACKGROUND AND OBJECTIVEChronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, due to concerns about radiation exposure and cost, expiratory CT is not routinely performed. Recent work on synthesizing expiratory CT has primarily focused on imaging features while neglecting patient-specific pulmonary function.To address these issues, we developed a novel model named BreathVisionNet that incorporates pulmonary function data to guide the synthesis of expiratory CT from inspiratory CT. An architecture combining a convolutional neural network and transformer is introduced to leverage the irregular phenotypic distribution in COPD patients. The model can better understand the long-range and global contexts by incorporating global information into the encoder. The utilization of edge information and multi-view data further enhances the quality of the synthesized CT. Parametric response mapping (PRM) can be estimated by using synthesized expiratory CT and inspiratory CT to quantify COPD phenotypes of the normal, emphysema, and functional small airway disease (fSAD), including their percentages, spatial distributions, and voxel distribution maps.METHODSTo address these issues, we developed a novel model named BreathVisionNet that incorporates pulmonary function data to guide the synthesis of expiratory CT from inspiratory CT. An architecture combining a convolutional neural network and transformer is introduced to leverage the irregular phenotypic distribution in COPD patients. The model can better understand the long-range and global contexts by incorporating global information into the encoder. The utilization of edge information and multi-view data further enhances the quality of the synthesized CT. Parametric response mapping (PRM) can be estimated by using synthesized expiratory CT and inspiratory CT to quantify COPD phenotypes of the normal, emphysema, and functional small airway disease (fSAD), including their percentages, spatial distributions, and voxel distribution maps.BreathVisionNet outperforms other generative models in terms of synthesized image quality. It achieves a mean absolute error, normalized mean square error, structural similarity index and peak signal-to-noise ratio of 78.207 HU, 0.643, 0.847 and 25.828 dB, respectively. Comparing the predicted and real PRM, the Dice coefficient can reach 0.732 (emphysema) and 0.560 (fSAD). The mean of differences between true and predicted fSAD percentage is 4.42 for the development dataset (low radiation dose CT scans), and 9.05 for an independent external validation dataset (routine dose), indicating that model has great generalizability. A classifier trained on voxel distribution maps can achieve an accuracy of 0.891 in predicting the presence of COPD.RESULTSBreathVisionNet outperforms other generative models in terms of synthesized image quality. It achieves a mean absolute error, normalized mean square error, structural similarity index and peak signal-to-noise ratio of 78.207 HU, 0.643, 0.847 and 25.828 dB, respectively. Comparing the predicted and real PRM, the Dice coefficient can reach 0.732 (emphysema) and 0.560 (fSAD). The mean of differences between true and predicted fSAD percentage is 4.42 for the development dataset (low radiation dose CT scans), and 9.05 for an independent external validation dataset (routine dose), indicating that model has great generalizability. A classifier trained on voxel distribution maps can achieve an accuracy of 0.891 in predicting the presence of COPD.BreathVisionNet can accurately synthesize expiratory CT images from inspiratory CT and predict their voxel distribution. The estimated PRM can help to quantify COPD phenotypes of the normal, emphysema, and fSAD. This capability provides additional insights into COPD diversity while only inspiratory CT images are available.CONCLUSIONSBreathVisionNet can accurately synthesize expiratory CT images from inspiratory CT and predict their voxel distribution. The estimated PRM can help to quantify COPD phenotypes of the normal, emphysema, and fSAD. This capability provides additional insights into COPD diversity while only inspiratory CT images are available. |
| ArticleNumber | 108516 |
| Author | Qi, Shouliang Zhang, Tiande Chen, Rongchang Xia, Shuyue Liu, Lingkai Xu, Jiaxuan Pang, Haowen Wu, Yanan Liang, Zhenyu Li, Shang |
| Author_xml | – sequence: 1 givenname: Tiande orcidid: 0009-0001-7617-6235 surname: Zhang fullname: Zhang, Tiande organization: College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China – sequence: 2 givenname: Haowen surname: Pang fullname: Pang, Haowen organization: School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China – sequence: 3 givenname: Yanan surname: Wu fullname: Wu, Yanan organization: College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China – sequence: 4 givenname: Jiaxuan surname: Xu fullname: Xu, Jiaxuan organization: State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China – sequence: 5 givenname: Lingkai surname: Liu fullname: Liu, Lingkai organization: College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China – sequence: 6 givenname: Shang surname: Li fullname: Li, Shang organization: College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China – sequence: 7 givenname: Shuyue surname: Xia fullname: Xia, Shuyue organization: Department of Respiratory and Critical Care Medicine, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China – sequence: 8 givenname: Rongchang surname: Chen fullname: Chen, Rongchang organization: State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China – sequence: 9 givenname: Zhenyu orcidid: 0000-0002-8746-315X surname: Liang fullname: Liang, Zhenyu email: 490458234@qq.com organization: State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China – sequence: 10 givenname: Shouliang orcidid: 0000-0003-0977-1939 surname: Qi fullname: Qi, Shouliang email: qisl@bmie.neu.edu.cn organization: College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China |
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| Cites_doi | 10.2307/1932409 10.1109/TIP.2003.819861 10.1148/radiol.2019191022 10.1148/radiol.2423060196 10.1109/TMI.2019.2895894 10.1109/TMI.2024.3382043 10.1016/j.acra.2011.01.011 10.1109/TMI.2022.3167808 10.1016/j.cmpb.2023.107389 10.1016/j.media.2017.06.015 10.1016/j.media.2020.101840 10.5815/ijigsp.2021.04.03 10.1186/s12931-023-02611-2 10.1145/3422622 10.2214/ajr.170.5.9574614 10.1109/TMI.2024.3367321 10.1109/TMI.2023.3290149 10.4081/mrm.2013.542 10.1007/s11548-023-02946-7 10.1016/j.compbiomed.2022.105792 10.1109/TMI.2009.2035616 10.1109/ACCESS.2023.3246762 10.1038/s41598-021-00058-3 10.1109/TMI.2017.2785879 10.1155/2021/5624909 10.1038/nm.2971 10.1016/j.jcm.2016.02.012 10.1109/TMI.2019.2901750 10.1364/BOE.8.000679 10.1016/j.media.2023.102983 10.1016/j.bspc.2022.104162 10.1016/j.artmed.2023.102637 10.1109/TMI.2022.3174827 10.1109/4.996 10.1148/radiol.2015141579 10.1016/j.media.2022.102614 |
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| Keywords | Deep learning Parametric response mapping Chronic obstructive pulmonary disease Transformer Generative adversarial network Image translation |
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| References | Weninger, Rippel, Koppers, Merhof (bib0049) 2019 Ho, Jain, Abbeel (bib0016) 2020; 33 M.F. Chaudhary, S.E. Gerard, G.E. Christensen, C.B. Cooper, J.D. Schroeder, E.A. Hoffman, J.M. Reinhardt, Lung2Lung: volumetric style transfer with self-ensembling for high-resolution cross-volume computed tomography, ArXiv. org, (2022). Zhao, Balakrishnan, Durand, Guttag, Dalca (bib0027) 2019 Hatamizadeh, Nath, Tang, Yang, Roth, Xu (bib0066) 2021 Xu (bib0047) 2021; 13 He, Chen, Xie, Li, Dollár, Girshick (bib0046) 2022 Pang, Wu, Qi, Li, Shen, Yue, Qian, Wu (bib0053) 2022; 147 Arakawa, Webb (bib0006) 1998; 170 Shamonin, Bron, Lelieveldt, Smits, Klein, Staring, Initiative (bib0051) 2014; 7 K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint, (2014). Dar, Yurt, Karacan, Erdem, Erdem, Cukur (bib0021) 2019; 38 Wang, Bovik, Sheikh, Simoncelli (bib0061) 2004; 13 H.-C. Shin, A. Ihsani, S. Mandava, S.T. Sreenivas, C. Forster, J. Cha, A.s.D.N. Initiative, Ganbert: generative adversarial networks with bidirectional encoder representations from transformers for mri to pet synthesis, arXiv preprint, (2020). Zhong, Chen, Shu, Zheng, Li, Chen, Wu, Ma, Feng, Yang (bib0024) 2023 Ronneberger, Fischer, Brox (bib0064) 2015 X. Zhang, X. He, J. Guo, N. Ettehadi, N. Aw, D. Semanek, J. Posner, A. Laine, Y. Wang, PTNet: a high-resolution infant MRI synthesizer based on transformer, arXiv preprint, (2021). Kim, Park (bib0022) 2024 Isola, Zhu, Zhou, Efros (bib0018) 2017 N.-C. Ristea, A.-I. Miron, O. Savencu, M.-I. Georgescu, N. Verga, F.S. Khan, R.T. Ionescu, Cytran: cycle-consistent transformers for non-contrast to contrast ct translation, arXiv preprint (2021). Z. Liu, Q. Lv, Y. Li, Z. Yang, L. Shen, Medaugment: universal automatic data augmentation plug-in for medical image analysis, arXiv preprint, (2023). Yu, Tang, Lin, Han, Tang, Chen (bib0056) 2019 Chaudhary, Gerard, Christensen, Cooper, Schroeder, Hoffman, Reinhardt (bib0011) 2024 M.J. Cardoso, W. Li, R. Brown, N. Ma, E. Kerfoot, Y. Wang, B. Murrey, A. Myronenko, C. Zhao, D. Yang, Monai: an open-source framework for deep learning in healthcare, arXiv preprint, (2022). Tiago, Snare, Šprem, McLeod (bib0070) 2023; 11 Hatamizadeh, Tang, Nath, Yang, Myronenko, Landman, Roth, Xu (bib0065) 2022 Yu, Zhou, Wang, Shi, Fripp, Bourgeat (bib0013) 2019; 38 Kearney, Ziemer, Perry, Wang, Chan, Ma, Morin, Yom, Solberg (bib0023) 2020; 2 Zhang, Zhang, Gu, Yang (bib0030) 2023; 18 D.P. Kingma, M. Welling, Auto-encoding variational bayes, arXiv preprint, (2013). Zha, Zhang, Li (bib0029) 2022 Kong, Lian, Huang, Hu, Zhou (bib0034) 2021; 34 Gatys, Ecker, Bethge (bib0059) 2016 Yang, Yu, Dong, Slabaugh, Dragotti, Ye, Liu, Arridge, Keegan, Guo (bib0033) 2017; 37 Lynch, Austin, Hogg, Grenier, Kauczor, Bankier, Barr, Colby, Galvin, Gevenois (bib0003) 2015; 277 Klein, Staring, Murphy, Viergever, Pluim (bib0052) 2009; 29 Dalmaz, Yurt, Çukur (bib0020) 2022; 41 Hu, Liu, Li, Yu (bib0040) 2021; 2021 Wu, Zhao, Qi, Feng, Pang, Chang, Bai, Li, Xia, Qian (bib0054) 2023; 143 L. Qi, J. Lu, Y. Lu, H. Cui, C.-C. Fu, W. Zhang, Q. Fang, C. He, S. Zhang, Y. Yang, Evaluation of Gas Trapping in Chronic Obstructive Pulmonary Disease: prediction of Parametric Response Mapping from Solo Inspiratory Chest CT Scan by Deep Learning, Available at SSRN 4016494. M.O. Topal, A. Bas, I. van Heerden, Exploring transformers in natural language generation: gpt, bert, and xlnet, arXiv preprint, (2021). Chen, Liu, Lu, Li, Kuang, Yang, Wang, Sun, Du, Qi (bib0012) 2023; 24 Pang, Qi, Wu, Wang, Li, Sun, Qian, Tang, Xu, Liang (bib0032) 2023; 231 Koo, Li (bib0063) 2016; 15 Zhou, Sodha, Pang, Gotway, Liang (bib0050) 2021; 67 Wu, Du, Feng, Qi, Pang, Xia, Qian (bib0001) 2023; 79 Choi, Cho, Ha, Lee, Lee, Choi, Cheon, Kim (bib0031) 2021; 11 Luo, Wang, Zu, Zhan, Wu, Zhou, Shen, Zhou (bib0045) 2021 Zhang, He, Guo, Ettehadi, Aw, Semanek, Posner, Laine, Wang (bib0067) 2022; 41 Galbán, Han, Boes, Chughtai, Meyer, Johnson, Galbán, Rehemtulla, Kazerooni, Martinez (bib0007) 2012; 18 Xia, Yang, Qu, Guo, Zhou, Zhang, Wang (bib0057) 2022; 82 Zhu, Park, Isola, Efros (bib0019) 2017 Gaeta, Minutoli, Girbino, Murabito, Benedetto, Contiguglia, Ruggeri, Privitera (bib0005) 2013; 8 M. Mirza, S. Osindero, Conditional generative adversarial nets, arXiv preprint, (2014). Wang, Yang, Chen, Yuan, Sermesant, Delingette, Wu (bib0071) 2024 Liu, Lin, Cao, Hu, Wei, Zhang, Lin, Guo (bib0039) 2021 Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio (bib0017) 2020; 63 Özbey, Dalmaz, Dar, Bedel, Özturk, Güngör, Çukur (bib0069) 2023 Dice (bib0062) 1945; 26 Kanopoulos, Vasanthavada, Baker (bib0060) 1988; 23 Gietema, Müller, Fauerbach, Sharma, Edwards, Camp, Coxson (bib0004) 2011; 18 Chen, Zhang, Zhang, Liao, Li, Zhou, Wang (bib0026) 2017; 8 Bankier, Schaefer-Prokop, De Maertelaer, Tack, Jaksch, Klepetko, Gevenois (bib0008) 2007; 242 Wang, Luo, Zu, Zhan, Jiao, Wu, Zhou, Shen, Zhou (bib0044) 2024; 91 Vila, Escolano, Fonollosa, Costa-Jussa (bib0037) 2018 J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A.L. Yuille, Y. Zhou, Transunet: transformers make strong encoders for medical image segmentation, arXiv preprint, (2021). Q. Yang, P. Yan, M.K. Kalra, G. Wang, CT image denoising with perceptive deep neural networks, arXiv preprint, (2017). Setio, Traverso, De Bel, Berens, Van Den Bogaard, Cerello, Chen, Dou, Fantacci, Geurts (bib0048) 2017; 42 A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, An image is worth 16×16 words: transformers for image recognition at scale, arXiv preprint, (2020). Humphries, Notary, Centeno, Strand, Crapo, Silverman, Lynch (bib0002) 2020; 294 Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin (bib0035) 2017; 30 10.1016/j.cmpb.2024.108516_bib0036 10.1016/j.cmpb.2024.108516_bib0038 Wu (10.1016/j.cmpb.2024.108516_bib0001) 2023; 79 Wu (10.1016/j.cmpb.2024.108516_bib0054) 2023; 143 Wang (10.1016/j.cmpb.2024.108516_bib0061) 2004; 13 Arakawa (10.1016/j.cmpb.2024.108516_bib0006) 1998; 170 Zha (10.1016/j.cmpb.2024.108516_bib0029) 2022 Luo (10.1016/j.cmpb.2024.108516_bib0045) 2021 Hu (10.1016/j.cmpb.2024.108516_bib0040) 2021; 2021 Gietema (10.1016/j.cmpb.2024.108516_bib0004) 2011; 18 Gaeta (10.1016/j.cmpb.2024.108516_bib0005) 2013; 8 Wang (10.1016/j.cmpb.2024.108516_bib0044) 2024; 91 Goodfellow (10.1016/j.cmpb.2024.108516_bib0017) 2020; 63 Isola (10.1016/j.cmpb.2024.108516_bib0018) 2017 Zhao (10.1016/j.cmpb.2024.108516_bib0027) 2019 Kanopoulos (10.1016/j.cmpb.2024.108516_bib0060) 1988; 23 Pang (10.1016/j.cmpb.2024.108516_bib0053) 2022; 147 10.1016/j.cmpb.2024.108516_bib0043 Xia (10.1016/j.cmpb.2024.108516_bib0057) 2022; 82 Ronneberger (10.1016/j.cmpb.2024.108516_bib0064) 2015 Vaswani (10.1016/j.cmpb.2024.108516_bib0035) 2017; 30 10.1016/j.cmpb.2024.108516_bib0041 10.1016/j.cmpb.2024.108516_bib0042 Liu (10.1016/j.cmpb.2024.108516_bib0039) 2021 Yu (10.1016/j.cmpb.2024.108516_bib0013) 2019; 38 Dice (10.1016/j.cmpb.2024.108516_bib0062) 1945; 26 Zhu (10.1016/j.cmpb.2024.108516_bib0019) 2017 Choi (10.1016/j.cmpb.2024.108516_bib0031) 2021; 11 He (10.1016/j.cmpb.2024.108516_bib0046) 2022 Zhong (10.1016/j.cmpb.2024.108516_bib0024) 2023 Lynch (10.1016/j.cmpb.2024.108516_bib0003) 2015; 277 Ho (10.1016/j.cmpb.2024.108516_bib0016) 2020; 33 10.1016/j.cmpb.2024.108516_bib0014 10.1016/j.cmpb.2024.108516_bib0058 10.1016/j.cmpb.2024.108516_bib0015 Kim (10.1016/j.cmpb.2024.108516_bib0022) 2024 10.1016/j.cmpb.2024.108516_bib0010 Hatamizadeh (10.1016/j.cmpb.2024.108516_bib0066) 2021 10.1016/j.cmpb.2024.108516_bib0055 Chaudhary (10.1016/j.cmpb.2024.108516_bib0011) 2024 Zhang (10.1016/j.cmpb.2024.108516_bib0030) 2023; 18 Zhang (10.1016/j.cmpb.2024.108516_bib0067) 2022; 41 Kearney (10.1016/j.cmpb.2024.108516_bib0023) 2020; 2 Zhou (10.1016/j.cmpb.2024.108516_bib0050) 2021; 67 Xu (10.1016/j.cmpb.2024.108516_bib0047) 2021; 13 Dar (10.1016/j.cmpb.2024.108516_bib0021) 2019; 38 Koo (10.1016/j.cmpb.2024.108516_bib0063) 2016; 15 Humphries (10.1016/j.cmpb.2024.108516_bib0002) 2020; 294 Bankier (10.1016/j.cmpb.2024.108516_bib0008) 2007; 242 Chen (10.1016/j.cmpb.2024.108516_bib0026) 2017; 8 Yang (10.1016/j.cmpb.2024.108516_bib0033) 2017; 37 Shamonin (10.1016/j.cmpb.2024.108516_bib0051) 2014; 7 Kong (10.1016/j.cmpb.2024.108516_bib0034) 2021; 34 Pang (10.1016/j.cmpb.2024.108516_bib0032) 2023; 231 Gatys (10.1016/j.cmpb.2024.108516_bib0059) 2016 10.1016/j.cmpb.2024.108516_bib0009 Wang (10.1016/j.cmpb.2024.108516_bib0071) 2024 10.1016/j.cmpb.2024.108516_bib0025 Setio (10.1016/j.cmpb.2024.108516_bib0048) 2017; 42 Hatamizadeh (10.1016/j.cmpb.2024.108516_bib0065) 2022 10.1016/j.cmpb.2024.108516_bib0028 10.1016/j.cmpb.2024.108516_bib0068 Weninger (10.1016/j.cmpb.2024.108516_bib0049) 2019 Vila (10.1016/j.cmpb.2024.108516_bib0037) 2018 Tiago (10.1016/j.cmpb.2024.108516_bib0070) 2023; 11 Dalmaz (10.1016/j.cmpb.2024.108516_bib0020) 2022; 41 Özbey (10.1016/j.cmpb.2024.108516_bib0069) 2023 Chen (10.1016/j.cmpb.2024.108516_bib0012) 2023; 24 Klein (10.1016/j.cmpb.2024.108516_bib0052) 2009; 29 Yu (10.1016/j.cmpb.2024.108516_bib0056) 2019 Galbán (10.1016/j.cmpb.2024.108516_bib0007) 2012; 18 |
| References_xml | – volume: 38 start-page: 2375 year: 2019 end-page: 2388 ident: bib0021 article-title: Image synthesis in multi-contrast MRI with conditional generative adversarial networks publication-title: IEEe Trans. Med. ImAging – volume: 294 start-page: 434 year: 2020 end-page: 444 ident: bib0002 article-title: GEoC Investigators, Deep learning enables automatic classification of emphysema pattern at CT publication-title: Radiology – volume: 18 start-page: 661 year: 2011 end-page: 671 ident: bib0004 article-title: Eocltipse investigators, Quantifying the extent of emphysema: factors associated with Radiologists’ Estimations and quantitative indices of emphysema severity using the ECLIPSE cohort publication-title: Acad. Radiol. – reference: H.-C. Shin, A. Ihsani, S. Mandava, S.T. Sreenivas, C. Forster, J. Cha, A.s.D.N. Initiative, Ganbert: generative adversarial networks with bidirectional encoder representations from transformers for mri to pet synthesis, arXiv preprint, (2020). – volume: 38 start-page: 1750 year: 2019 end-page: 1762 ident: bib0013 article-title: Ea-GANs: edge-aware generative adversarial networks for cross-modality MR image synthesis publication-title: IEEe Trans. Med. ImAging – start-page: 10012 year: 2021 end-page: 10022 ident: bib0039 article-title: Swin transformer: hierarchical vision transformer using shifted windows publication-title: Proceedings of the IEEE/CVF international conference on computer vision – volume: 91 year: 2024 ident: bib0044 article-title: 3D multi-modality Transformer-GAN for high-quality PET reconstruction publication-title: Med. Image Anal. – start-page: 8543 year: 2019 end-page: 8553 ident: bib0027 article-title: Data augmentation using learned transformations for one-shot medical image segmentation publication-title: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition – volume: 277 start-page: 192 year: 2015 end-page: 205 ident: bib0003 article-title: CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner Society publication-title: Radiology – reference: D.P. Kingma, M. Welling, Auto-encoding variational bayes, arXiv preprint, (2013). – volume: 18 start-page: 1287 year: 2023 end-page: 1294 ident: bib0030 article-title: Deep anatomy learning for lung airway and artery-vein modeling with contrast-enhanced CT synthesis publication-title: Int. J. Comput. Assist. Radiol. Surg. – volume: 41 start-page: 2925 year: 2022 end-page: 2940 ident: bib0067 article-title: PTNet3D: a 3D high-resolution longitudinal infant brain MRI synthesizer based on transformers publication-title: IEEe Trans. Med. ImAging – year: 2024 ident: bib0071 article-title: Mutual information guided diffusion for zero-shot cross-modality medical image translation publication-title: IEEe Trans. Med. ImAging – volume: 11 start-page: 20403 year: 2021 ident: bib0031 article-title: Generating synthetic contrast enhancement from non-contrast chest computed tomography using a generative adversarial network publication-title: Sci. Rep. – reference: X. Zhang, X. He, J. Guo, N. Ettehadi, N. Aw, D. Semanek, J. Posner, A. Laine, Y. Wang, PTNet: a high-resolution infant MRI synthesizer based on transformer, arXiv preprint, (2021). – volume: 8 start-page: 679 year: 2017 end-page: 694 ident: bib0026 article-title: Low-dose CT via convolutional neural network publication-title: Biomed. Opt. Express. – volume: 8 start-page: 1 year: 2013 end-page: 8 ident: bib0005 article-title: Expiratory CT scan in patients with normal inspiratory CT scan: a finding of obliterative bronchiolitis and other causes of bronchiolar obstruction publication-title: Multidiscip. Respir. Med. – reference: N.-C. Ristea, A.-I. Miron, O. Savencu, M.-I. Georgescu, N. Verga, F.S. Khan, R.T. Ionescu, Cytran: cycle-consistent transformers for non-contrast to contrast ct translation, arXiv preprint (2021). – volume: 23 start-page: 358 year: 1988 end-page: 367 ident: bib0060 article-title: Design of an image edge detection filter using the Sobel operator publication-title: IEEe J. Solid-State Circuits. – reference: A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, An image is worth 16×16 words: transformers for image recognition at scale, arXiv preprint, (2020). – volume: 82 year: 2022 ident: bib0057 article-title: Multilevel structure-preserved GAN for domain adaptation in intravascular ultrasound analysis publication-title: Med. Image Anal. – volume: 13 start-page: 600 year: 2004 end-page: 612 ident: bib0061 article-title: Image quality assessment: from error visibility to structural similarity publication-title: IEEE Trans. Image Process. – volume: 41 start-page: 2598 year: 2022 end-page: 2614 ident: bib0020 article-title: ResViT: residual vision transformers for multimodal medical image synthesis publication-title: IEEe Trans. Med. ImAging – volume: 26 start-page: 297 year: 1945 end-page: 302 ident: bib0062 article-title: Measures of the amount of ecologic association between species publication-title: Ecology – volume: 42 start-page: 1 year: 2017 end-page: 13 ident: bib0048 article-title: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge publication-title: Med. Image Anal. – volume: 170 start-page: 1349 year: 1998 end-page: 1353 ident: bib0006 article-title: Air trapping on expiratory high-resolution CT scans in the absence of inspiratory scan abnormalities: correlation with pulmonary function tests and differential diagnosis publication-title: AJR Am. J. Roentgenol. – start-page: 3 year: 2019 end-page: 12 ident: bib0049 article-title: Segmentation of brain tumors and patient survival prediction: methods for the brats 2018 challenge publication-title: Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4 – year: 2023 ident: bib0069 article-title: Unsupervised medical image translation with adversarial diffusion models publication-title: IEEe Trans. Med. ImAging – reference: L. Qi, J. Lu, Y. Lu, H. Cui, C.-C. Fu, W. Zhang, Q. Fang, C. He, S. Zhang, Y. Yang, Evaluation of Gas Trapping in Chronic Obstructive Pulmonary Disease: prediction of Parametric Response Mapping from Solo Inspiratory Chest CT Scan by Deep Learning, Available at SSRN 4016494. – volume: 63 start-page: 139 year: 2020 end-page: 144 ident: bib0017 article-title: Generative adversarial networks publication-title: Commun. ACM – volume: 79 year: 2023 ident: bib0001 article-title: Deep CNN for COPD identification by multi-view snapshot integration of 3D airway tree and lung field publication-title: Biomed. Signal. Process. Control – volume: 2 year: 2020 ident: bib0023 article-title: Attention-aware discrimination for MR-to-CT image translation using cycle-consistent generative adversarial networks publication-title: Radiology – volume: 7 start-page: 50 year: 2014 ident: bib0051 article-title: Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease publication-title: Front. Neuroinform. – start-page: 2414 year: 2016 end-page: 2423 ident: bib0059 article-title: Image style transfer using convolutional neural networks publication-title: Proceedings of the IEEE conference on computer vision and pattern recognition – reference: Z. Liu, Q. Lv, Y. Li, Z. Yang, L. Shen, Medaugment: universal automatic data augmentation plug-in for medical image analysis, arXiv preprint, (2023). – volume: 147 year: 2022 ident: bib0053 article-title: A fully automatic segmentation pipeline of pulmonary lobes before and after lobectomy from computed tomography images publication-title: Comput. Biol. Med. – start-page: 60 year: 2018 end-page: 63 ident: bib0037 article-title: End-to-End Speech Translation With the Transformer – volume: 15 start-page: 155 year: 2016 end-page: 163 ident: bib0063 article-title: A guideline of selecting and reporting intraclass correlation coefficients for reliability research publication-title: J. Chiropr. Med. – volume: 18 start-page: 1711 year: 2012 end-page: 1715 ident: bib0007 article-title: Computed tomography–based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression publication-title: Nat. Med. – volume: 242 start-page: 898 year: 2007 end-page: 906 ident: bib0008 article-title: Air trapping: comparison of standard-dose and simulated low-dose thin-section CT techniques publication-title: Radiology – reference: M. Mirza, S. Osindero, Conditional generative adversarial nets, arXiv preprint, (2014). – start-page: 442 year: 2022 end-page: 452 ident: bib0029 article-title: Naf: neural attenuation fields for sparse-view cbct reconstruction publication-title: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention – volume: 33 start-page: 6840 year: 2020 end-page: 6851 ident: bib0016 article-title: Denoising diffusion probabilistic models publication-title: Adv. Neural Inf. Process. Syst. – reference: M.F. Chaudhary, S.E. Gerard, G.E. Christensen, C.B. Cooper, J.D. Schroeder, E.A. Hoffman, J.M. Reinhardt, Lung2Lung: volumetric style transfer with self-ensembling for high-resolution cross-volume computed tomography, ArXiv. org, (2022). – start-page: 16000 year: 2022 end-page: 16009 ident: bib0046 article-title: Masked autoencoders are scalable vision learners publication-title: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition – start-page: 276 year: 2021 end-page: 285 ident: bib0045 article-title: 3D transformer-GAN for high-quality PET reconstruction publication-title: Proceedings of the Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24 – volume: 11 start-page: 17594 year: 2023 end-page: 17602 ident: bib0070 article-title: A domain translation framework with an adversarial denoising diffusion model to generate synthetic datasets of echocardiography images publication-title: IEEe Access. – start-page: 2223 year: 2017 end-page: 2232 ident: bib0019 article-title: Unpaired image-to-image translation using cycle-consistent adversarial networks publication-title: Proceedings of the IEEE international conference on computer vision – start-page: 1125 year: 2017 end-page: 1134 ident: bib0018 article-title: Image-to-image translation with conditional adversarial networks publication-title: Proceedings of the IEEE conference on computer vision and pattern recognition – start-page: 2713 year: 2019 end-page: 2718 ident: bib0056 article-title: CWGAN: conditional wasserstein generative adversarial nets for fault data generation publication-title: Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) – volume: 143 year: 2023 ident: bib0054 article-title: Two-stage contextual transformer-based convolutional neural network for airway extraction from ct images publication-title: Artif. Intell. Med. – reference: K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint, (2014). – reference: Q. Yang, P. Yan, M.K. Kalra, G. Wang, CT image denoising with perceptive deep neural networks, arXiv preprint, (2017). – reference: M.J. Cardoso, W. Li, R. Brown, N. Ma, E. Kerfoot, Y. Wang, B. Murrey, A. Myronenko, C. Zhao, D. Yang, Monai: an open-source framework for deep learning in healthcare, arXiv preprint, (2022). – volume: 2021 start-page: 1 year: 2021 end-page: 7 ident: bib0040 article-title: Data-enabled intelligence in complex industrial systems cross-model transformer method for medical image synthesis publication-title: Complexity – volume: 37 start-page: 1310 year: 2017 end-page: 1321 ident: bib0033 article-title: DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction publication-title: IEEe Trans. Med. ImAging – reference: M.O. Topal, A. Bas, I. van Heerden, Exploring transformers in natural language generation: gpt, bert, and xlnet, arXiv preprint, (2021). – volume: 24 start-page: 299 year: 2023 ident: bib0012 article-title: Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening publication-title: Respir. Res. – volume: 13 start-page: 33 year: 2021 end-page: 46 ident: bib0047 article-title: A review of self-supervised learning methods in the field of medical image analysis publication-title: IJIGSP – start-page: 272 year: 2021 end-page: 284 ident: bib0066 article-title: Swin unetr: Swin transformers For Semantic Segmentation of Brain Tumors in MRI images, International MICCAI Brainlesion Workshop – volume: 231 year: 2023 ident: bib0032 article-title: NCCT-CECT image synthesizers and their application to pulmonary vessel segmentation publication-title: Comput. Methods Programs Biomed. – volume: 30 year: 2017 ident: bib0035 article-title: Attention is all you need publication-title: Adv. Neural Inf. Process. Syst. – volume: 34 start-page: 1964 year: 2021 end-page: 1978 ident: bib0034 article-title: Breaking the dilemma of medical image-to-image translation publication-title: Adv. Neural Inf. Process. Syst. – start-page: 7604 year: 2024 end-page: 7613 ident: bib0022 article-title: Adaptive latent diffusion model for 3D medical image to image translation: multi-modal magnetic resonance imaging study publication-title: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision – reference: J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A.L. Yuille, Y. Zhou, Transunet: transformers make strong encoders for medical image segmentation, arXiv preprint, (2021). – start-page: 574 year: 2022 end-page: 584 ident: bib0065 article-title: Unetr: transformers for 3D medical image segmentation publication-title: Proceedings of the IEEE/CVF winter conference on applications of computer vision – year: 2024 ident: bib0011 article-title: LungViT: ensembling cascade of texture sensitive hierarchical vision transformers for cross-volume chest CT image-to-image translation publication-title: IEEe Trans. Med. ImAging – volume: 67 year: 2021 ident: bib0050 article-title: Models genesis publication-title: Med. Image Anal. – year: 2023 ident: bib0024 article-title: Multi-scale tokens-aware transformer network for multi-region and multi-sequence MR-to-CT synthesis in a single model publication-title: IEEe Trans. Med. ImAging – volume: 29 start-page: 196 year: 2009 end-page: 205 ident: bib0052 article-title: Elastix: a toolbox for intensity-based medical image registration publication-title: IEEe Trans. Med. ImAging – start-page: 234 year: 2015 end-page: 241 ident: bib0064 article-title: U-net: convolutional networks for biomedical image segmentation publication-title: Proceedings of the Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 – volume: 26 start-page: 297 year: 1945 ident: 10.1016/j.cmpb.2024.108516_bib0062 article-title: Measures of the amount of ecologic association between species publication-title: Ecology doi: 10.2307/1932409 – volume: 13 start-page: 600 year: 2004 ident: 10.1016/j.cmpb.2024.108516_bib0061 article-title: Image quality assessment: from error visibility to structural similarity publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2003.819861 – volume: 294 start-page: 434 year: 2020 ident: 10.1016/j.cmpb.2024.108516_bib0002 article-title: GEoC Investigators, Deep learning enables automatic classification of emphysema pattern at CT publication-title: Radiology doi: 10.1148/radiol.2019191022 – volume: 242 start-page: 898 year: 2007 ident: 10.1016/j.cmpb.2024.108516_bib0008 article-title: Air trapping: comparison of standard-dose and simulated low-dose thin-section CT techniques publication-title: Radiology doi: 10.1148/radiol.2423060196 – start-page: 60 year: 2018 ident: 10.1016/j.cmpb.2024.108516_bib0037 – year: 2023 ident: 10.1016/j.cmpb.2024.108516_bib0024 article-title: Multi-scale tokens-aware transformer network for multi-region and multi-sequence MR-to-CT synthesis in a single model publication-title: IEEe Trans. Med. ImAging – volume: 38 start-page: 1750 year: 2019 ident: 10.1016/j.cmpb.2024.108516_bib0013 article-title: Ea-GANs: edge-aware generative adversarial networks for cross-modality MR image synthesis publication-title: IEEe Trans. Med. ImAging doi: 10.1109/TMI.2019.2895894 – start-page: 2713 year: 2019 ident: 10.1016/j.cmpb.2024.108516_bib0056 article-title: CWGAN: conditional wasserstein generative adversarial nets for fault data generation – year: 2024 ident: 10.1016/j.cmpb.2024.108516_bib0071 article-title: Mutual information guided diffusion for zero-shot cross-modality medical image translation publication-title: IEEe Trans. Med. ImAging doi: 10.1109/TMI.2024.3382043 – volume: 18 start-page: 661 year: 2011 ident: 10.1016/j.cmpb.2024.108516_bib0004 article-title: Eocltipse investigators, Quantifying the extent of emphysema: factors associated with Radiologists’ Estimations and quantitative indices of emphysema severity using the ECLIPSE cohort publication-title: Acad. Radiol. doi: 10.1016/j.acra.2011.01.011 – ident: 10.1016/j.cmpb.2024.108516_bib0036 – ident: 10.1016/j.cmpb.2024.108516_bib0042 – volume: 41 start-page: 2598 year: 2022 ident: 10.1016/j.cmpb.2024.108516_bib0020 article-title: ResViT: residual vision transformers for multimodal medical image synthesis publication-title: IEEe Trans. Med. ImAging doi: 10.1109/TMI.2022.3167808 – start-page: 272 year: 2021 ident: 10.1016/j.cmpb.2024.108516_bib0066 – volume: 231 year: 2023 ident: 10.1016/j.cmpb.2024.108516_bib0032 article-title: NCCT-CECT image synthesizers and their application to pulmonary vessel segmentation publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2023.107389 – volume: 42 start-page: 1 year: 2017 ident: 10.1016/j.cmpb.2024.108516_bib0048 article-title: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge publication-title: Med. Image Anal. doi: 10.1016/j.media.2017.06.015 – volume: 67 year: 2021 ident: 10.1016/j.cmpb.2024.108516_bib0050 article-title: Models genesis publication-title: Med. Image Anal. doi: 10.1016/j.media.2020.101840 – volume: 13 start-page: 33 year: 2021 ident: 10.1016/j.cmpb.2024.108516_bib0047 article-title: A review of self-supervised learning methods in the field of medical image analysis publication-title: IJIGSP doi: 10.5815/ijigsp.2021.04.03 – volume: 24 start-page: 299 year: 2023 ident: 10.1016/j.cmpb.2024.108516_bib0012 article-title: Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening publication-title: Respir. Res. doi: 10.1186/s12931-023-02611-2 – volume: 63 start-page: 139 year: 2020 ident: 10.1016/j.cmpb.2024.108516_bib0017 article-title: Generative adversarial networks publication-title: Commun. ACM doi: 10.1145/3422622 – volume: 170 start-page: 1349 year: 1998 ident: 10.1016/j.cmpb.2024.108516_bib0006 article-title: Air trapping on expiratory high-resolution CT scans in the absence of inspiratory scan abnormalities: correlation with pulmonary function tests and differential diagnosis publication-title: AJR Am. J. Roentgenol. doi: 10.2214/ajr.170.5.9574614 – ident: 10.1016/j.cmpb.2024.108516_bib0055 – start-page: 8543 year: 2019 ident: 10.1016/j.cmpb.2024.108516_bib0027 article-title: Data augmentation using learned transformations for one-shot medical image segmentation – start-page: 574 year: 2022 ident: 10.1016/j.cmpb.2024.108516_bib0065 article-title: Unetr: transformers for 3D medical image segmentation – year: 2024 ident: 10.1016/j.cmpb.2024.108516_bib0011 article-title: LungViT: ensembling cascade of texture sensitive hierarchical vision transformers for cross-volume chest CT image-to-image translation publication-title: IEEe Trans. Med. ImAging doi: 10.1109/TMI.2024.3367321 – year: 2023 ident: 10.1016/j.cmpb.2024.108516_bib0069 article-title: Unsupervised medical image translation with adversarial diffusion models publication-title: IEEe Trans. Med. ImAging doi: 10.1109/TMI.2023.3290149 – volume: 8 start-page: 1 year: 2013 ident: 10.1016/j.cmpb.2024.108516_bib0005 article-title: Expiratory CT scan in patients with normal inspiratory CT scan: a finding of obliterative bronchiolitis and other causes of bronchiolar obstruction publication-title: Multidiscip. Respir. Med. doi: 10.4081/mrm.2013.542 – volume: 18 start-page: 1287 year: 2023 ident: 10.1016/j.cmpb.2024.108516_bib0030 article-title: Deep anatomy learning for lung airway and artery-vein modeling with contrast-enhanced CT synthesis publication-title: Int. J. Comput. Assist. Radiol. Surg. doi: 10.1007/s11548-023-02946-7 – start-page: 2414 year: 2016 ident: 10.1016/j.cmpb.2024.108516_bib0059 article-title: Image style transfer using convolutional neural networks – ident: 10.1016/j.cmpb.2024.108516_bib0014 – volume: 147 year: 2022 ident: 10.1016/j.cmpb.2024.108516_bib0053 article-title: A fully automatic segmentation pipeline of pulmonary lobes before and after lobectomy from computed tomography images publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2022.105792 – ident: 10.1016/j.cmpb.2024.108516_bib0010 – volume: 30 year: 2017 ident: 10.1016/j.cmpb.2024.108516_bib0035 article-title: Attention is all you need publication-title: Adv. Neural Inf. Process. Syst. – start-page: 7604 year: 2024 ident: 10.1016/j.cmpb.2024.108516_bib0022 article-title: Adaptive latent diffusion model for 3D medical image to image translation: multi-modal magnetic resonance imaging study – start-page: 442 year: 2022 ident: 10.1016/j.cmpb.2024.108516_bib0029 article-title: Naf: neural attenuation fields for sparse-view cbct reconstruction – start-page: 10012 year: 2021 ident: 10.1016/j.cmpb.2024.108516_bib0039 article-title: Swin transformer: hierarchical vision transformer using shifted windows – start-page: 234 year: 2015 ident: 10.1016/j.cmpb.2024.108516_bib0064 article-title: U-net: convolutional networks for biomedical image segmentation – ident: 10.1016/j.cmpb.2024.108516_bib0041 – volume: 29 start-page: 196 year: 2009 ident: 10.1016/j.cmpb.2024.108516_bib0052 article-title: Elastix: a toolbox for intensity-based medical image registration publication-title: IEEe Trans. Med. ImAging doi: 10.1109/TMI.2009.2035616 – ident: 10.1016/j.cmpb.2024.108516_bib0028 – volume: 11 start-page: 17594 year: 2023 ident: 10.1016/j.cmpb.2024.108516_bib0070 article-title: A domain translation framework with an adversarial denoising diffusion model to generate synthetic datasets of echocardiography images publication-title: IEEe Access. doi: 10.1109/ACCESS.2023.3246762 – ident: 10.1016/j.cmpb.2024.108516_bib0025 – volume: 11 start-page: 20403 year: 2021 ident: 10.1016/j.cmpb.2024.108516_bib0031 article-title: Generating synthetic contrast enhancement from non-contrast chest computed tomography using a generative adversarial network publication-title: Sci. Rep. doi: 10.1038/s41598-021-00058-3 – volume: 37 start-page: 1310 year: 2017 ident: 10.1016/j.cmpb.2024.108516_bib0033 article-title: DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction publication-title: IEEe Trans. Med. ImAging doi: 10.1109/TMI.2017.2785879 – volume: 7 start-page: 50 year: 2014 ident: 10.1016/j.cmpb.2024.108516_bib0051 article-title: Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease publication-title: Front. Neuroinform. – volume: 2021 start-page: 1 year: 2021 ident: 10.1016/j.cmpb.2024.108516_bib0040 article-title: Data-enabled intelligence in complex industrial systems cross-model transformer method for medical image synthesis publication-title: Complexity doi: 10.1155/2021/5624909 – volume: 18 start-page: 1711 year: 2012 ident: 10.1016/j.cmpb.2024.108516_bib0007 article-title: Computed tomography–based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression publication-title: Nat. Med. doi: 10.1038/nm.2971 – ident: 10.1016/j.cmpb.2024.108516_bib0058 – ident: 10.1016/j.cmpb.2024.108516_bib0038 – volume: 15 start-page: 155 year: 2016 ident: 10.1016/j.cmpb.2024.108516_bib0063 article-title: A guideline of selecting and reporting intraclass correlation coefficients for reliability research publication-title: J. Chiropr. Med. doi: 10.1016/j.jcm.2016.02.012 – start-page: 276 year: 2021 ident: 10.1016/j.cmpb.2024.108516_bib0045 article-title: 3D transformer-GAN for high-quality PET reconstruction – ident: 10.1016/j.cmpb.2024.108516_bib0015 – volume: 38 start-page: 2375 year: 2019 ident: 10.1016/j.cmpb.2024.108516_bib0021 article-title: Image synthesis in multi-contrast MRI with conditional generative adversarial networks publication-title: IEEe Trans. Med. ImAging doi: 10.1109/TMI.2019.2901750 – start-page: 3 year: 2019 ident: 10.1016/j.cmpb.2024.108516_bib0049 article-title: Segmentation of brain tumors and patient survival prediction: methods for the brats 2018 challenge – volume: 8 start-page: 679 year: 2017 ident: 10.1016/j.cmpb.2024.108516_bib0026 article-title: Low-dose CT via convolutional neural network publication-title: Biomed. Opt. Express. doi: 10.1364/BOE.8.000679 – volume: 91 year: 2024 ident: 10.1016/j.cmpb.2024.108516_bib0044 article-title: 3D multi-modality Transformer-GAN for high-quality PET reconstruction publication-title: Med. Image Anal. doi: 10.1016/j.media.2023.102983 – volume: 2 year: 2020 ident: 10.1016/j.cmpb.2024.108516_bib0023 article-title: Attention-aware discrimination for MR-to-CT image translation using cycle-consistent generative adversarial networks publication-title: Radiology – volume: 34 start-page: 1964 year: 2021 ident: 10.1016/j.cmpb.2024.108516_bib0034 article-title: Breaking the dilemma of medical image-to-image translation publication-title: Adv. Neural Inf. Process. Syst. – volume: 79 year: 2023 ident: 10.1016/j.cmpb.2024.108516_bib0001 article-title: Deep CNN for COPD identification by multi-view snapshot integration of 3D airway tree and lung field publication-title: Biomed. Signal. Process. Control doi: 10.1016/j.bspc.2022.104162 – volume: 33 start-page: 6840 year: 2020 ident: 10.1016/j.cmpb.2024.108516_bib0016 article-title: Denoising diffusion probabilistic models publication-title: Adv. Neural Inf. Process. Syst. – start-page: 16000 year: 2022 ident: 10.1016/j.cmpb.2024.108516_bib0046 article-title: Masked autoencoders are scalable vision learners – volume: 143 year: 2023 ident: 10.1016/j.cmpb.2024.108516_bib0054 article-title: Two-stage contextual transformer-based convolutional neural network for airway extraction from ct images publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2023.102637 – volume: 41 start-page: 2925 year: 2022 ident: 10.1016/j.cmpb.2024.108516_bib0067 article-title: PTNet3D: a 3D high-resolution longitudinal infant brain MRI synthesizer based on transformers publication-title: IEEe Trans. Med. ImAging doi: 10.1109/TMI.2022.3174827 – ident: 10.1016/j.cmpb.2024.108516_bib0043 – volume: 23 start-page: 358 year: 1988 ident: 10.1016/j.cmpb.2024.108516_bib0060 article-title: Design of an image edge detection filter using the Sobel operator publication-title: IEEe J. Solid-State Circuits. doi: 10.1109/4.996 – ident: 10.1016/j.cmpb.2024.108516_bib0068 – volume: 277 start-page: 192 year: 2015 ident: 10.1016/j.cmpb.2024.108516_bib0003 article-title: CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner Society publication-title: Radiology doi: 10.1148/radiol.2015141579 – start-page: 2223 year: 2017 ident: 10.1016/j.cmpb.2024.108516_bib0019 article-title: Unpaired image-to-image translation using cycle-consistent adversarial networks – ident: 10.1016/j.cmpb.2024.108516_bib0009 – start-page: 1125 year: 2017 ident: 10.1016/j.cmpb.2024.108516_bib0018 article-title: Image-to-image translation with conditional adversarial networks – volume: 82 year: 2022 ident: 10.1016/j.cmpb.2024.108516_bib0057 article-title: Multilevel structure-preserved GAN for domain adaptation in intravascular ultrasound analysis publication-title: Med. Image Anal. doi: 10.1016/j.media.2022.102614 |
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| Snippet | •A model of synthesizing expiratory CT images from inspiratory images is developed.•A CNN-Transformer network with a global context injection module is... Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can... |
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| SubjectTerms | Algorithms Chronic obstructive pulmonary disease Deep learning Exhalation Generative adversarial network Humans Image Processing, Computer-Assisted - methods Image translation Lung - diagnostic imaging Neural Networks, Computer Parametric response mapping Pulmonary Disease, Chronic Obstructive - diagnostic imaging Pulmonary Disease, Chronic Obstructive - physiopathology Respiratory Function Tests Tomography, X-Ray Computed - methods Transformer |
| Title | BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis |
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