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

Rine Nakanishi, Sethuraman Sankaran, Leo Grady, Jenifer Malpeso, Razik Yousfi, Kazuhiro Osawa, Indre Ceponiene, Negin Nazarat, Sina Rahmani, Kendall Kissel, Eranthi Jayawardena, Christopher Dailing, Christopher Zarins, Bon Kwon Koo, James K. Min, Charles A. Taylor, Matthew J. Budoff

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)4018-4026
Number of pages9
JournalEuropean Radiology
Volume28
Issue number9
DOIs
StatePublished - Sep 1 2018
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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