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The impact of iterative reconstruction algorithms on machine learning-based coronary CT angiography-derived fractional flow reserve (CT-FFRML) values

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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-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.

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Acknowledgements

Thank American Journal Experts for providing language help.

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Authors and Affiliations

  1. Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Shujiao Li, Chihua Chen, Le Qin, Shengjia Gu, Huan Zhang, Fuhua Yan & Wenjie Yang

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  1. Shujiao Li
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [SL], [CC], [LQ] and [SG]. Conceptualization, resources, and methodology were performed by [WY], [HZ].and [FY]. The first draft of the manuscript was written by [SL]. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Wenjie Yang.

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The approval for this study had been granted by the local Institutional Review Board prior to its conduct and informed consent was waived. The study therefore had been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. No patient identifiers were collected, and details that might disclose the identity of the subjects under study should were omitted.

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Li, S., Chen, C., Qin, L. et al. The impact of iterative reconstruction algorithms on machine learning-based coronary CT angiography-derived fractional flow reserve (CT-FFRML) values. Int J Cardiovasc Imaging 36, 1177–1185 (2020). https://doi.org/10.1007/s10554-020-01807-7

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