Automated cardiovascular risk categorization through AI-driven coronary calcium quantification in cardiac PET acquired attenuation correction CT

S. G. M. van Velzen, M. M. Dobrolinska, P. Knaapen, R. L. M. van Herten, R. Jukema, I. Danad, R. H. J. A. Slart, M. J. W. Greuter, I. Išgum

Research output: Contribution to JournalArticleAcademicpeer-review


Background: We present an automatic method for coronary artery calcium (CAC) quantification and cardiovascular risk categorization in CT attenuation correction (CTAC) scans acquired at rest and stress during cardiac PET/CT. The method segments CAC according to visual assessment rather than the commonly used CT-number threshold. Methods: The method decomposes an image containing CAC into a synthetic image without CAC and an image showing only CAC. Extensive evaluation was performed in a set of 98 patients, each having rest and stress CTAC scans and a dedicated calcium scoring CT (CSCT). Standard manual calcium scoring in CSCT provided the reference standard. Results: The interscan reproducibility of CAC quantification computed as average absolute relative differences between CTAC and CSCT scan pairs was 75% and 85% at rest and stress using the automatic method compared to 121% and 114% using clinical calcium scoring. Agreement between automatic risk assessment in CTAC and clinical risk categorization in CSCT resulted in linearly weighted kappa of 0.65 compared to 0.40 between CTAC and CSCT using clinically used calcium scoring. Conclusion: The increased interscan reproducibility achieved by our method may allow routine cardiovascular risk assessment in CTAC, potentially relieving the need for dedicated CSCT.
Original languageEnglish
JournalJournal of nuclear cardiology
Early online date2022
Publication statusE-pub ahead of print - 2022


  • CAD
  • CT
  • artificial intelligence
  • atherosclerosis
  • image analysis
  • risk categorization

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