Automated estimation of image quality for coronary computed tomographic angiography using machine learning
Our goal was to evaluate the efficacy of a fully automated method for assessing the image quality (IQ) of coronary computed tomography angiography (CCTA).
The machine learning method was trained using 75 CCTA studies by mapping features (noise, contrast, misregistration scores, and un-interpretability index) to an IQ score based on manual ground truth data. The automated method was validated on a set of 50 CCTA studies and subsequently tested on a new set of 172 CCTA studies against visual IQ scores on a 5-point Likert scale.
The area under the curve in the validation set was 0.96. In the 172 CCTA studies, our method yielded a Cohen’s kappa statistic for the agreement between automated and visual IQ assessment of 0.67 (p < 0.01). In the group where good to excellent (n = 163), fair (n = 6), and poor visual IQ scores (n = 3) were graded, 155, 5, and 2 of the patients received an automated IQ score > 50 %, respectively.
Fully automated assessment of the IQ of CCTA data sets by machine learning was reproducible and provided similar results compared with visual analysis within the limits of inter-operator variability.
• The proposed method enables automated and reproducible image quality assessment.
• Machine learning and visual assessments yielded comparable estimates of image quality.
• Automated assessment potentially allows for more standardised image quality.
• Image quality assessment enables standardization of clinical trial results across different datasets.
KeywordComputed tomography angiography Coronary vessels Cardiac imaging techniques Machine learning Image enhancement
Area under the curve
Coronary artery disease
Coronary computed tomographic angiography
Fractional flow reserve
Invasive coronary angiography
Compliance with ethical standards
The scientific guarantor of this publication is Dr. Matthew J. Budoff.
Conflict of interest
The authors of this manuscript declare relationships with the following companies: Dr. Matthew Budoff receives grant support from GE Healthcare. Dr. Sankaran, Dr. Grady, Mr. Yousfi, Dr. Zarins, and Dr. Taylor are employees of HeartFlow. Dr. Min received modest speakers’ bureau medical advisory board compensation and significant research support from GE Healthcare. All other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary in the current study.
Written informed consent was obtained from all subjects (patients) in this study.
Institutional Review Board approval was obtained.
Study subjects or cohorts overlap
Some study subjects have been previously reported in the JAMA and JACC.
• This is a retrospective observational study using two previous multicentre studies.
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