Feasibility exploration of myocardial blood flow synthesis from a simulated static myocardial computed tomography perfusion via a deep neural network.
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| Title: | Feasibility exploration of myocardial blood flow synthesis from a simulated static myocardial computed tomography perfusion via a deep neural network. |
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| Authors: | Dong, Jun1,2 (AUTHOR), Ling, Runjianya3 (AUTHOR), Huang, Zhenxing1 (AUTHOR), Xu, Yidan4 (AUTHOR), Wang, Haiyan1 (AUTHOR), Chen, Zixiang1 (AUTHOR), Huang, Meiyong1 (AUTHOR), Stankovic, Vladimir2 (AUTHOR), Zhang, Jiayin4 (AUTHOR) andrewssmu@msn.com, Hu, Zhanli1 (AUTHOR) zl.hu@siat.ac.cn |
| Source: | Journal of X-Ray Science & Technology. May2025, Vol. 33 Issue 3, p578-590. 13p. |
| Subjects: | Artificial neural networks, Myocardial perfusion imaging, Computed tomography, Coronary artery disease, Deep learning |
| Abstract: | Background: Myocardial blood flow (MBF) provides important diagnostic information for myocardial ischemia. However, dynamic computed tomography perfusion (CTP) needed for MBF involves multiple exposures, leading to high radiation doses. Objectives: This study investigated synthesizing MBF from simulated static myocardial CTP to explore dose reduction potential, bypassing the traditional dynamic input function. Methods: The study included 253 subjects with intermediate-to-high pretest probabilities of obstructive coronary artery disease (CAD). MBF was reconstructed from dynamic myocardial CTP. A deep neural network (DNN) converted simulated static CTP into synthetic MBF. Beyond the usual image quality evaluation, the synthetic MBF was segmented and a clinical functional assessment was conducted, with quantitative analysis for consistency and correlation. Results: Synthetic MBF closely matched the referenced MBF, with an average structure similarity (SSIM) of 0.87. ROC analysis of ischemic segments showed an area under curve (AUC) of 0.915 for synthetic MBF. This method can theoretically reduce the radiation dose for MBF significantly, provided satisfactory static CTP is obtained, reducing reliance on high time resolution of dynamic CTP. Conclusions: The proposed method is feasible, with satisfactory clinical functionality of synthetic MBF. Further investigation and validation are needed to confirm actual dose reduction in clinical settings. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of X-Ray Science & Technology is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 185066905 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Feasibility exploration of myocardial blood flow synthesis from a simulated static myocardial computed tomography perfusion via a deep neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dong%2C+Jun%22">Dong, Jun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ling%2C+Runjianya%22">Ling, Runjianya</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Zhenxing%22">Huang, Zhenxing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Yidan%22">Xu, Yidan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Haiyan%22">Wang, Haiyan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Zixiang%22">Chen, Zixiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Meiyong%22">Huang, Meiyong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Stankovic%2C+Vladimir%22">Stankovic, Vladimir</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jiayin%22">Zhang, Jiayin</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> andrewssmu@msn.com</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Zhanli%22">Hu, Zhanli</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zl.hu@siat.ac.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+X-Ray+Science+%26+Technology%22">Journal of X-Ray Science & Technology</searchLink>. May2025, Vol. 33 Issue 3, p578-590. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Myocardial+perfusion+imaging%22">Myocardial perfusion imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Coronary+artery+disease%22">Coronary artery disease</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Myocardial blood flow (MBF) provides important diagnostic information for myocardial ischemia. However, dynamic computed tomography perfusion (CTP) needed for MBF involves multiple exposures, leading to high radiation doses. Objectives: This study investigated synthesizing MBF from simulated static myocardial CTP to explore dose reduction potential, bypassing the traditional dynamic input function. Methods: The study included 253 subjects with intermediate-to-high pretest probabilities of obstructive coronary artery disease (CAD). MBF was reconstructed from dynamic myocardial CTP. A deep neural network (DNN) converted simulated static CTP into synthetic MBF. Beyond the usual image quality evaluation, the synthetic MBF was segmented and a clinical functional assessment was conducted, with quantitative analysis for consistency and correlation. Results: Synthetic MBF closely matched the referenced MBF, with an average structure similarity (SSIM) of 0.87. ROC analysis of ischemic segments showed an area under curve (AUC) of 0.915 for synthetic MBF. This method can theoretically reduce the radiation dose for MBF significantly, provided satisfactory static CTP is obtained, reducing reliance on high time resolution of dynamic CTP. Conclusions: The proposed method is feasible, with satisfactory clinical functionality of synthetic MBF. Further investigation and validation are needed to confirm actual dose reduction in clinical settings. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of X-Ray Science & Technology is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/08953996251317412 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 578 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Myocardial perfusion imaging Type: general – SubjectFull: Computed tomography Type: general – SubjectFull: Coronary artery disease Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Feasibility exploration of myocardial blood flow synthesis from a simulated static myocardial computed tomography perfusion via a deep neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dong, Jun – PersonEntity: Name: NameFull: Ling, Runjianya – PersonEntity: Name: NameFull: Huang, Zhenxing – PersonEntity: Name: NameFull: Xu, Yidan – PersonEntity: Name: NameFull: Wang, Haiyan – PersonEntity: Name: NameFull: Chen, Zixiang – PersonEntity: Name: NameFull: Huang, Meiyong – PersonEntity: Name: NameFull: Stankovic, Vladimir – PersonEntity: Name: NameFull: Zhang, Jiayin – PersonEntity: Name: NameFull: Hu, Zhanli IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08953996 Numbering: – Type: volume Value: 33 – Type: issue Value: 3 Titles: – TitleFull: Journal of X-Ray Science & Technology Type: main |
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