The impact of iterative reconstruction algorithms on machine learning-based coronary CT angiography-derived fractional flow reserve (CT-FFRML) values
To evaluate the impact of an iterative reconstruction (IR) algorithm (advanced modeled iterative reconstruction, ADMIRE) on machine learning-based coronary computed tomography angiography–derived fractional flow reserve (CT-FFR ML ) measurements compared with filtered back projection (FBP). 170 plaq...
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| Vydáno v: | The International Journal of Cardiovascular Imaging Ročník 36; číslo 6; s. 1177 - 1185 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Dordrecht
Springer Netherlands
01.06.2020
Springer Nature B.V |
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| ISSN: | 1569-5794, 1875-8312, 1573-0743, 1875-8312 |
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| Abstract | To evaluate the impact of an iterative reconstruction (IR) algorithm (advanced modeled iterative reconstruction, ADMIRE) on machine learning-based coronary computed tomography angiography–derived fractional flow reserve (CT-FFR
ML
) measurements compared with filtered back projection (FBP). 170 plaque-containing vessels in 107 patients were included. CT-FFR
ML
values were measured and compared among 5 imaging reconstruction algorithms (FBP and ADMIRE at strength levels of 1, 2, 3 and 5). The plaques were classified as, ‘calcified” or “noncalcified” and “≥ 50% stenosis” or “< 50% stenosis’, a total of four subgroups by consensus. There were no significant differences of CT-FFR
ML
values among the FBP and ADMIRE 1, 2, 3 and 5 groups wherever comparisons were done at the level of subgroups (P = 0.676, 0.414, 0.849, 0.873, respectively) or overall (P = 0.072). There were 20, 21, 19, 19 and 29 vessels with lesion-specific ischemia (CT-FFR
ML
≤ 0.80) in FBP and ADMIRE 1, 2, 3 and 5 datasets, respectively, but no statistical differences were found (P = 0.437). Compared with CT-FFR
ML
value of FBP dataset, the CT-FFR
ML
values of 9 (5.3%) vessels from 8 patients (7.5%) in ADMIRE5 dataset switched from above 0.8 to below or equal to 0.8. There were no significant differences of the CT-FFR
ML
values among the FBP and IR image algorithms at different strength levels. However, high iterative strength level (ADMIRE 5) was not recommended, which might have an impact on diagnosis of lesion-specific ischemia, although changes only occurred in a modest number of subjects. |
|---|---|
| AbstractList | To evaluate the impact of an iterative reconstruction (IR) algorithm (advanced modeled iterative reconstruction, ADMIRE) on machine learning-based coronary computed tomography angiography-derived fractional flow reserve (CT-FFRML) measurements compared with filtered back projection (FBP). 170 plaque-containing vessels in 107 patients were included. CT-FFRML values were measured and compared among 5 imaging reconstruction algorithms (FBP and ADMIRE at strength levels of 1, 2, 3 and 5). The plaques were classified as, 'calcified" or "noncalcified" and "≥ 50% stenosis" or "< 50% stenosis', a total of four subgroups by consensus. There were no significant differences of CT-FFRML values among the FBP and ADMIRE 1, 2, 3 and 5 groups wherever comparisons were done at the level of subgroups (P = 0.676, 0.414, 0.849, 0.873, respectively) or overall (P = 0.072). There were 20, 21, 19, 19 and 29 vessels with lesion-specific ischemia (CT-FFRML ≤ 0.80) in FBP and ADMIRE 1, 2, 3 and 5 datasets, respectively, but no statistical differences were found (P = 0.437). Compared with CT-FFRML value of FBP dataset, the CT-FFRML values of 9 (5.3%) vessels from 8 patients (7.5%) in ADMIRE5 dataset switched from above 0.8 to below or equal to 0.8. There were no significant differences of the CT-FFRML values among the FBP and IR image algorithms at different strength levels. However, high iterative strength level (ADMIRE 5) was not recommended, which might have an impact on diagnosis of lesion-specific ischemia, although changes only occurred in a modest number of subjects.To evaluate the impact of an iterative reconstruction (IR) algorithm (advanced modeled iterative reconstruction, ADMIRE) on machine learning-based coronary computed tomography angiography-derived fractional flow reserve (CT-FFRML) measurements compared with filtered back projection (FBP). 170 plaque-containing vessels in 107 patients were included. CT-FFRML values were measured and compared among 5 imaging reconstruction algorithms (FBP and ADMIRE at strength levels of 1, 2, 3 and 5). The plaques were classified as, 'calcified" or "noncalcified" and "≥ 50% stenosis" or "< 50% stenosis', a total of four subgroups by consensus. There were no significant differences of CT-FFRML values among the FBP and ADMIRE 1, 2, 3 and 5 groups wherever comparisons were done at the level of subgroups (P = 0.676, 0.414, 0.849, 0.873, respectively) or overall (P = 0.072). There were 20, 21, 19, 19 and 29 vessels with lesion-specific ischemia (CT-FFRML ≤ 0.80) in FBP and ADMIRE 1, 2, 3 and 5 datasets, respectively, but no statistical differences were found (P = 0.437). Compared with CT-FFRML value of FBP dataset, the CT-FFRML values of 9 (5.3%) vessels from 8 patients (7.5%) in ADMIRE5 dataset switched from above 0.8 to below or equal to 0.8. There were no significant differences of the CT-FFRML values among the FBP and IR image algorithms at different strength levels. However, high iterative strength level (ADMIRE 5) was not recommended, which might have an impact on diagnosis of lesion-specific ischemia, although changes only occurred in a modest number of subjects. To evaluate the impact of an iterative reconstruction (IR) algorithm (advanced modeled iterative reconstruction, ADMIRE) on machine learning-based coronary computed tomography angiography-derived fractional flow reserve (CT-FFR ) measurements compared with filtered back projection (FBP). 170 plaque-containing vessels in 107 patients were included. CT-FFR values were measured and compared among 5 imaging reconstruction algorithms (FBP and ADMIRE at strength levels of 1, 2, 3 and 5). The plaques were classified as, 'calcified" or "noncalcified" and "≥ 50% stenosis" or "< 50% stenosis', a total of four subgroups by consensus. There were no significant differences of CT-FFR values among the FBP and ADMIRE 1, 2, 3 and 5 groups wherever comparisons were done at the level of subgroups (P = 0.676, 0.414, 0.849, 0.873, respectively) or overall (P = 0.072). There were 20, 21, 19, 19 and 29 vessels with lesion-specific ischemia (CT-FFR ≤ 0.80) in FBP and ADMIRE 1, 2, 3 and 5 datasets, respectively, but no statistical differences were found (P = 0.437). Compared with CT-FFR value of FBP dataset, the CT-FFR values of 9 (5.3%) vessels from 8 patients (7.5%) in ADMIRE5 dataset switched from above 0.8 to below or equal to 0.8. There were no significant differences of the CT-FFR values among the FBP and IR image algorithms at different strength levels. However, high iterative strength level (ADMIRE 5) was not recommended, which might have an impact on diagnosis of lesion-specific ischemia, although changes only occurred in a modest number of subjects. To evaluate the impact of an iterative reconstruction (IR) algorithm (advanced modeled iterative reconstruction, ADMIRE) on machine learning-based coronary computed tomography angiography–derived fractional flow reserve (CT-FFRML) measurements compared with filtered back projection (FBP). 170 plaque-containing vessels in 107 patients were included. CT-FFRML values were measured and compared among 5 imaging reconstruction algorithms (FBP and ADMIRE at strength levels of 1, 2, 3 and 5). The plaques were classified as, ‘calcified” or “noncalcified” and “≥ 50% stenosis” or “< 50% stenosis’, a total of four subgroups by consensus. There were no significant differences of CT-FFRML values among the FBP and ADMIRE 1, 2, 3 and 5 groups wherever comparisons were done at the level of subgroups (P = 0.676, 0.414, 0.849, 0.873, respectively) or overall (P = 0.072). There were 20, 21, 19, 19 and 29 vessels with lesion-specific ischemia (CT-FFRML ≤ 0.80) in FBP and ADMIRE 1, 2, 3 and 5 datasets, respectively, but no statistical differences were found (P = 0.437). Compared with CT-FFRML value of FBP dataset, the CT-FFRML values of 9 (5.3%) vessels from 8 patients (7.5%) in ADMIRE5 dataset switched from above 0.8 to below or equal to 0.8. There were no significant differences of the CT-FFRML values among the FBP and IR image algorithms at different strength levels. However, high iterative strength level (ADMIRE 5) was not recommended, which might have an impact on diagnosis of lesion-specific ischemia, although changes only occurred in a modest number of subjects. To evaluate the impact of an iterative reconstruction (IR) algorithm (advanced modeled iterative reconstruction, ADMIRE) on machine learning-based coronary computed tomography angiography–derived fractional flow reserve (CT-FFR ML ) measurements compared with filtered back projection (FBP). 170 plaque-containing vessels in 107 patients were included. CT-FFR ML values were measured and compared among 5 imaging reconstruction algorithms (FBP and ADMIRE at strength levels of 1, 2, 3 and 5). The plaques were classified as, ‘calcified” or “noncalcified” and “≥ 50% stenosis” or “< 50% stenosis’, a total of four subgroups by consensus. There were no significant differences of CT-FFR ML values among the FBP and ADMIRE 1, 2, 3 and 5 groups wherever comparisons were done at the level of subgroups (P = 0.676, 0.414, 0.849, 0.873, respectively) or overall (P = 0.072). There were 20, 21, 19, 19 and 29 vessels with lesion-specific ischemia (CT-FFR ML ≤ 0.80) in FBP and ADMIRE 1, 2, 3 and 5 datasets, respectively, but no statistical differences were found (P = 0.437). Compared with CT-FFR ML value of FBP dataset, the CT-FFR ML values of 9 (5.3%) vessels from 8 patients (7.5%) in ADMIRE5 dataset switched from above 0.8 to below or equal to 0.8. There were no significant differences of the CT-FFR ML values among the FBP and IR image algorithms at different strength levels. However, high iterative strength level (ADMIRE 5) was not recommended, which might have an impact on diagnosis of lesion-specific ischemia, although changes only occurred in a modest number of subjects. |
| Author | Li, Shujiao Chen, Chihua Gu, Shengjia Zhang, Huan Qin, Le Yan, Fuhua Yang, Wenjie |
| Author_xml | – sequence: 1 givenname: Shujiao orcidid: 0000-0001-5139-3893 surname: Li fullname: Li, Shujiao organization: Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine – sequence: 2 givenname: Chihua orcidid: 0000-0002-3547-7584 surname: Chen fullname: Chen, Chihua organization: Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine – sequence: 3 givenname: Le orcidid: 0000-0002-8244-8217 surname: Qin fullname: Qin, Le organization: Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine – sequence: 4 givenname: Shengjia orcidid: 0000-0002-5659-5133 surname: Gu fullname: Gu, Shengjia organization: Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine – sequence: 5 givenname: Huan orcidid: 0000-0001-5506-5627 surname: Zhang fullname: Zhang, Huan organization: Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine – sequence: 6 givenname: Fuhua orcidid: 0000-0003-4503-5007 surname: Yan fullname: Yan, Fuhua organization: Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine – sequence: 7 givenname: Wenjie orcidid: 0000-0002-3959-5043 surname: Yang fullname: Yang, Wenjie email: lisa_ywj@163.com organization: Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32130576$$D View this record in MEDLINE/PubMed |
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| Copyright | Springer Nature B.V. 2020 Springer Nature B.V. 2020. |
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| Keywords | Myocardial fractional flow reserve Coronary computed tomography angiography Image reconstruction Machine learning Coronary stenosis |
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| PublicationTitle | The International Journal of Cardiovascular Imaging |
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| SubjectTerms | Algorithms Angiography Blood vessels Cardiac Imaging Cardiology Computed tomography Datasets Image reconstruction Imaging Ischemia Iterative methods Learning algorithms Machine learning Medical imaging Medicine Medicine & Public Health Original Paper Plaques Radiology Stenosis Subgroups |
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| Title | The impact of iterative reconstruction algorithms on machine learning-based coronary CT angiography-derived fractional flow reserve (CT-FFRML) values |
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