Automated estimation of image quality for coronary computed tomographic angiography using machine learning

作者：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^*

来源：European Radiology, 2018, 28(9): 4018-4026.

DOI：10.1007/s00330-018-5348-8

• Computed tomography angiography
• Coronary vessels
• Cardiac imaging techniques
• Machine learning
• Image enhancement

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.
aEuro cent The proposed method enables automated and reproducible image quality assessment.
aEuro cent Machine learning and visual assessments yielded comparable estimates of image quality.
aEuro cent Automated assessment potentially allows for more standardised image quality.
aEuro cent Image quality assessment enables standardization of clinical trial results across different datasets.

• 出版日期2018-9
• 单位UCLA