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- Title
Automated estimation of image quality for coronary computed tomographic angiography using machine learning.
- Authors
Nakanishi, Rine; Sankaran, Sethuraman; Grady, Leo; Malpeso, Jenifer; Yousfi, Razik; Osawa, Kazuhiro; Ceponiene, Indre; Nazarat, Negin; Rahmani, Sina; Kissel, Kendall; Jayawardena, Eranthi; Dailing, Christopher; Zarins, Christopher; Koo, Bon-Kwon; Min, James K.; Taylor, Charles A.; Budoff, Matthew J.
- Abstract
<bold>Objectives: </bold>Our goal was to evaluate the efficacy of a fully automated method for assessing the image quality (IQ) of coronary computed tomography angiography (CCTA).<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusion: </bold>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.<bold>Key Points: </bold>• 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.
- Subjects
CORONARY angiography; IMAGE quality analysis; COMPUTED tomography; MACHINE learning; LIKERT scale; AUTOMATION; CLINICAL trials; COMPARATIVE studies; CORONARY disease; DIAGNOSTIC imaging; RESEARCH methodology; MEDICAL cooperation; PHARMACOKINETICS; RESEARCH; EVALUATION research
- Publication
European Radiology, 2018, Vol 28, Issue 9, p4018
- ISSN
0938-7994
- Publication type
journal article
- DOI
10.1007/s00330-018-5348-8