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Ji Won Kim, Tae Hoon Kim, Seung Hyun Lee, Ji Eun Nam, Chul Hwan Park
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Global burden of cardiovascular diseases and risks, 1990-2022: a systematic analysis for the Global Burden of Disease Study
10.1016/j.jacc.2023.11.007 · 2023
1990-2019: a systematic analysis for the Global Burden of Disease Study 2019
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Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017
10.1016/s0140-6736(18)32203-7 · 2018
Coronary artery calcium score and risk classification for coronary heart disease prediction
10.1001/jama.2010.461 · 2010
Quantification of coronary artery calcium using ultrafast computed tomography
10.1016/0735-1097(90)90282-t · 1990
Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT: A validation study
10.1016/j.ejrad.2020.109428 · 2021
Evaluating the use of coronary artery calcium scoring as a tool for coronary artery disease (CAD) risk stratification and its association with coronary stenosis and CAD risk factors: a single-centre, retrospective, cross-sectional study at a tertiary centre in Pakistan
10.1136/bmjopen-2021-057703 · 2022
Feasibility of coronary artery calcium scoring on dual-energy chest computed tomography: a prospective comparison with electrocardiogram-gated calcium score computed tomography
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Long-term prognostic value of coronary calcification detected by electron-beam computed tomography in patients undergoing coronary angiography
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Physics-informed Fourier neural network with self-adaptive loss weight for modeling transient wave propagation
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Chest computed tomography using iterative reconstruction vs filtered back projection (Part 1): evaluation of image noise reduction in 32 patients
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Chest computed tomography using iterative reconstruction vs filtered back projection (Part 2): image quality of low-dose CT examinations in 80 patients
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The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis
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Impact of deep learning image reconstructions (DLIR) on coronary artery calcium quantification
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Influence of deep learning image reconstruction and adaptive statistical iterative reconstruction-V on coronary artery calcium quantification
10.21037/atm-21-5548 · 2021
Influence of deep learning based image reconstruction on quantitative results of coronary artery calcium scoring
10.1016/j.acra.2024.03.020 · 2024
Assessment of dose exposure and image quality in coronary angiography performed by 640-slice CT: a comparison between adaptive iterative and filtered back-projection algorithm by propensity analysis
10.1007/s11547-014-0382-3 · 2014
Estimates of effective dose for CT scans of the lower extremities
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CAC-DRS: coronary artery calcium data and reporting system. an expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT)
10.1016/j.jcct.2018.03.008 · 2018
Coronary artery calcium data and reporting system (CAC-DRS): a primer
10.4250/jcvi.2022.0029 · 2023
The effect of iterative model reconstruction on coronary artery calcium quantification
10.1007/s10554-015-0740-9 · 2016
Impact of advanced modeled iterative reconstruction on coronary artery calcium quantification
10.1016/j.acra.2016.08.008 · 2016
Coronary artery calcium quantification: comparison between filtered-back projection, hybrid iterative reconstruction, and deep learning reconstruction techniques
10.1177/02841851231174463 · 2023
Comparison of the effect of iterative reconstruction versus filtered back projection on cardiac CT postprocessing
10.1016/j.acra.2013.11.008 · 2014
Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks
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Clinical feasibility of deep learning-based image reconstruction on coronary computed tomography angiography
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Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography
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Systematic assessment of coronary calcium detectability and quantification on four generations of CT reconstruction techniques: a patient and phantom study
10.1007/s10554-022-02703-y · 2023
Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review
10.12688/f1000research.147345.1 · 2024
Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: emphasis on image quality and noise
10.3348/kjr.2020.0116 · 2021
ESR statement on the validation of imaging biomarkers
10.1186/s13244-020-00872-9 · 2020
Establishing a validation infrastructure for imaging-based artificial intelligence algorithms before clinical implementation
2024
2024 ESC Guidelines for the management of chronic coronary syndromes
10.1093/eurheartj/ehae177 · 2024
2024 ESC Guidelines for the management of chronic coronary syndromes
10.1093/eurheartj/ehae177 · doi-reference
ESR statement on the validation of imaging biomarkers
10.1186/s13244-020-00872-9 · doi-reference
Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: emphasis on image quality and noise
10.3348/kjr.2020.0116 · doi-reference
Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review
10.12688/f1000research.147345.1 · doi-reference
Systematic assessment of coronary calcium detectability and quantification on four generations of CT reconstruction techniques: a patient and phantom study
10.1007/s10554-022-02703-y · doi-reference
Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography
10.1007/s00330-021-08367-x · doi-reference
Clinical feasibility of deep learning-based image reconstruction on coronary computed tomography angiography
10.3390/jcm12103501 · doi-reference
Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks
10.1016/j.compbiomed.2023.106668 · doi-reference
Comparison of the effect of iterative reconstruction versus filtered back projection on cardiac CT postprocessing
10.1016/j.acra.2013.11.008 · doi-reference
Coronary artery calcium quantification: comparison between filtered-back projection, hybrid iterative reconstruction, and deep learning reconstruction techniques
10.1177/02841851231174463 · doi-reference
Impact of advanced modeled iterative reconstruction on coronary artery calcium quantification
10.1016/j.acra.2016.08.008 · doi-reference
The effect of iterative model reconstruction on coronary artery calcium quantification
10.1007/s10554-015-0740-9 · doi-reference
Coronary artery calcium data and reporting system (CAC-DRS): a primer
10.4250/jcvi.2022.0029 · doi-reference
CAC-DRS: coronary artery calcium data and reporting system. an expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT)
10.1016/j.jcct.2018.03.008 · doi-reference
2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans: a report of the society of cardiovascular computed tomography and society of thoracic radiology
10.1016/j.jcct.2016.11.003 · doi-reference
Estimates of effective dose for CT scans of the lower extremities
10.1148/radiol.14132903 · doi-reference
Assessment of dose exposure and image quality in coronary angiography performed by 640-slice CT: a comparison between adaptive iterative and filtered back-projection algorithm by propensity analysis
10.1007/s11547-014-0382-3 · doi-reference
Influence of deep learning based image reconstruction on quantitative results of coronary artery calcium scoring
10.1016/j.acra.2024.03.020 · doi-reference
Influence of deep learning image reconstruction and adaptive statistical iterative reconstruction-V on coronary artery calcium quantification
10.21037/atm-21-5548 · doi-reference
Impact of deep learning image reconstructions (DLIR) on coronary artery calcium quantification
10.1007/s00330-022-09287-0 · doi-reference
The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis
10.1007/s00330-021-08438-z · doi-reference
Chest computed tomography using iterative reconstruction vs filtered back projection (Part 2): image quality of low-dose CT examinations in 80 patients
10.1007/s00330-010-1991-4 · doi-reference
Chest computed tomography using iterative reconstruction vs filtered back projection (Part 1): evaluation of image noise reduction in 32 patients
10.1007/s00330-010-1990-5 · doi-reference
Physics-informed Fourier neural network with self-adaptive loss weight for modeling transient wave propagation
10.1016/j.engappai.2026.113994 · doi-reference
Long-term prognostic value of coronary calcification detected by electron-beam computed tomography in patients undergoing coronary angiography
10.1161/hc2901.093112 · doi-reference
Feasibility of coronary artery calcium scoring on dual-energy chest computed tomography: a prospective comparison with electrocardiogram-gated calcium score computed tomography
10.3390/jcm10040653 · doi-reference
Evaluating the use of coronary artery calcium scoring as a tool for coronary artery disease (CAD) risk stratification and its association with coronary stenosis and CAD risk factors: a single-centre, retrospective, cross-sectional study at a tertiary centre in Pakistan
10.1136/bmjopen-2021-057703 · doi-reference
Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT: A validation study
10.1016/j.ejrad.2020.109428 · doi-reference
Quantification of coronary artery calcium using ultrafast computed tomography
10.1016/0735-1097(90)90282-t · doi-reference
Coronary artery calcium score and risk classification for coronary heart disease prediction
10.1001/jama.2010.461 · doi-reference
Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017
10.1016/s0140-6736(18)32203-7 · doi-reference
Global burden of cardiovascular diseases and risks, 1990-2022: a systematic analysis for the Global Burden of Disease Study
10.1016/j.jacc.2023.11.007 · doi-reference