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31.01.2020 | Computed Tomography

The influence of image quality on diagnostic performance of a machine learning–based fractional flow reserve derived from coronary CT angiography

verfasst von: Peng Peng Xu, Jian Hua Li, Fan Zhou, Meng Di Jiang, Chang Sheng Zhou, Meng Jie Lu, Chun Xiang Tang, Xiao Lei Zhang, Liu Yang, Yuan Xiu Zhang, Yi Ning Wang, Jia Yin Zhang, Meng Meng Yu, Yang Hou, Min Wen Zheng, Bo Zhang, Dai Min Zhang, Yan Yi, Lei Xu, Xiu Hua Hu, Hui Liu, Guang Ming Lu, Qian Qian Ni, Long Jiang Zhang

Erschienen in: European Radiology | Ausgabe 5/2020

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Abstract

Objective

To investigate the effect of image quality of coronary CT angiography (CCTA) on the diagnostic performance of a machine learning–based CT-derived fractional flow reserve (FFR_CT).

Methods

This nationwide retrospective study enrolled participants from 10 individual centers across China. FFR_CT analysis was performed in 570 vessels in 437 patients. Invasive FFR and FFR_CT values ≤ 0.80 were considered ischemia-specific. Four-score subjective assessment based on image quality and objective measurement of vessel enhancement was performed on a per-vessel basis. The effects of body mass index (BMI), sex, heart rate, and coronary calcium score on the diagnostic performance of FFR_CT were studied.

Results

Among 570 vessels, 216 were considered ischemia-specific by invasive FFR and 198 by FFR_CT. Sensitivity and specificity of FFR_CT for detecting lesion-specific ischemia were 0.82 and 0.93, respectively. Area under the curve (AUC) of high-quality images (0.93, n = 159) was found to be superior to low-quality images (0.80, n = 92, p = 0.02). Objective image quality and heart rate were also associated with diagnostic performance of FFR_CT, whereas there was no statistical difference in diagnostic performance among different BMI, sex, and calcium score groups (all p > 0.05, Bonferroni correction).

Conclusions

This retrospective multicenter study supported the FFR_CT as a noninvasive test in evaluating lesion-specific ischemia. Subjective image quality, vessel enhancement, and heart rate affect the diagnostic performance of FFR_CT.

Key Points

• FFR _CT can be used to evaluate lesion-specific ischemia.

• Poor image quality negatively affects the diagnostic performance of FFR _CT .

• CCTA with ≥ score 3, intracoronary enhancement degree of 300–400 HU, and heart rate below 70 bpm at scanning could be of great benefit to more accurate FFR _CT analysis.

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Titel: The influence of image quality on diagnostic performance of a machine learning–based fractional flow reserve derived from coronary CT angiography
verfasst von: Peng Peng Xu
Jian Hua Li
Fan Zhou
Meng Di Jiang
Chang Sheng Zhou
Meng Jie Lu
Chun Xiang Tang
Xiao Lei Zhang
Liu Yang
Yuan Xiu Zhang
Yi Ning Wang
Jia Yin Zhang
Meng Meng Yu
Yang Hou
Min Wen Zheng
Bo Zhang
Dai Min Zhang
Yan Yi
Lei Xu
Xiu Hua Hu
Hui Liu
Guang Ming Lu
Qian Qian Ni
Long Jiang Zhang
Publikationsdatum: 31.01.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 5/2020
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-019-06571-4

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The influence of image quality on diagnostic performance of a machine learning–based fractional flow reserve derived from coronary CT angiography