Automated estimation of image quality for coronary computed tomographic angiography using machine learning
- 269 Downloads
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
The authors state that this work has not received any funding.
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.
- 1.Budoff MJ, Dowe D, Jollis JG et al (2008) Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial. J Am Coll Cardiol 52(21):1724–1732CrossRefPubMedGoogle Scholar
- 2.Achenbach S, Moselewski F, Ropers D et al (2004) Detection of calcified and noncalcified coronary atherosclerotic plaque by contrast-enhanced, submillimeter multidetector spiral computed tomography: a segment-based comparison with intravascular ultrasound. Circulation. 109(1):14–17CrossRefPubMedGoogle Scholar
- 5.SCOT-HEART investigators (2015) CT coronary angiography in patients with suspected angina due to coronary heart disease (SCOT-HEART): an open-label, parallel-group, multicentre trial. Lancet 385(9985):2383–91.Google Scholar
- 7.Naeemi MDRJ, Hollcroft N, Alessio AM, Roychowdhury S (2016) Application of big data analytics for automated estimation of CT image quality. Big Data (Big Data). IEEE International Conference on. IEEE 2016:3422–3431Google Scholar
- 10.Koo BK, Erglis A, Doh JH et al (2011) Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) study. J Am Coll Cardiol. 58(19):1989–1997CrossRefPubMedGoogle Scholar
- 14.Abbara S, Blanke P, Maroules CD et al (2016) SCCT guidelines for the performance and acquisition of coronary computed tomographic angiography: A report of the society of Cardiovascular Computed Tomography Guidelines Committee: Endorsed by the North American Society for Cardiovascular Imaging (NASCI). J Cardiovasc Comput Tomogr. 10(6):435–449CrossRefPubMedGoogle Scholar
- 17.Cury RC, Abbara S, Achenbach S et al (2016) CAD-RADS(TM) Coronary Artery Disease - Reporting and Data System. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American Society for Cardiovascular Imaging (NASCI). Endorsed by the American College of Cardiology. J Cardiovasc Comput Tomogr. 10(4):269–281CrossRefPubMedGoogle Scholar