TY - JOUR
T1 - Automated estimation of image quality for coronary computed tomographic angiography using machine learning
AU - Nakanishi, Rine
AU - Sankaran, Sethuraman
AU - Grady, Leo
AU - Malpeso, Jenifer
AU - Yousfi, Razik
AU - Osawa, Kazuhiro
AU - Ceponiene, Indre
AU - Nazarat, Negin
AU - Rahmani, Sina
AU - Kissel, Kendall
AU - Jayawardena, Eranthi
AU - Dailing, Christopher
AU - Zarins, Christopher
AU - Koo, Bon Kwon
AU - Min, James K.
AU - Taylor, Charles A.
AU - Budoff, Matthew J.
N1 - Publisher Copyright:
© 2018, European Society of Radiology.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Objectives: Our goal was to evaluate the efficacy of a fully automated method for assessing the image quality (IQ) of coronary computed tomography angiography (CCTA). Methods: 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. Results: 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. Conclusion: 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. Key points: • 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.
AB - Objectives: Our goal was to evaluate the efficacy of a fully automated method for assessing the image quality (IQ) of coronary computed tomography angiography (CCTA). Methods: 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. Results: 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. Conclusion: 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. Key points: • 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.
KW - Cardiac imaging techniques
KW - Computed tomography angiography
KW - Coronary vessels
KW - Image enhancement
KW - Machine learning
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U2 - 10.1007/s00330-018-5348-8
DO - 10.1007/s00330-018-5348-8
M3 - Article
C2 - 29572635
AN - SCOPUS:85044347435
SN - 0938-7994
VL - 28
SP - 4018
EP - 4026
JO - European Radiology
JF - European Radiology
IS - 9
ER -