Effect of image reconstruction algorithms on volumetric and radiomic parameters of coronary plaques

Márton Kolossváry; Bálint Szilveszter; Júlia Karády; Zsófia Dóra Drobni; Béla Merkely; Pál Maurovich-Horvat

doi:10.1016/j.jcct.2018.11.004

Effect of image reconstruction algorithms on volumetric and radiomic parameters of coronary plaques

J Cardiovasc Comput Tomogr. 2019 Nov-Dec;13(6):325-330. doi: 10.1016/j.jcct.2018.11.004. Epub 2018 Nov 12.

Authors

Márton Kolossváry¹, Bálint Szilveszter², Júlia Karády², Zsófia Dóra Drobni², Béla Merkely², Pál Maurovich-Horvat²

Affiliations

¹ Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest Hungary, 68. Varosmajor Street, 1122, Budapest, Hungary. Electronic address: marton.kolossvary@cirg.hu.
² Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest Hungary, 68. Varosmajor Street, 1122, Budapest, Hungary.

PMID: 30447949
DOI: 10.1016/j.jcct.2018.11.004

Abstract

Background: Volumetric and radiomic analysis of atherosclerotic plaques on coronary CT angiography have been shown to predict high-risk plaque morphology and to predict patient outcomes. However, there is limited information whether image reconstruction algorithms and preprocessing steps (type of binning, number of bins used for discretization) may influence parameter values.

Methods: We retrospectively identified 60 coronary lesions on coronary CT angiography (CTA). All images were reconstructed using filtered back projection (FBP), hybrid (HIR) and model-based (MIR) iterative reconstruction. Plaques were segmented manually on HIR images and copied to FBP and MIR images to ensure identical voxels were analyzed. Overall, 4 volumetric and 169 radiomic parameters were calculated. Intra-class correlation coefficient (ICC) was used to assess reproducibility between image reconstructions, while linear regression analysis was used to assess the effect of preprocessing steps done before calculating radiomic metrics.

Results: All volumetric and radiomic metrics had ICC>0.90 except for first-order statistics: mode, harmonic mean, minimum (0.45, 0.76, 0.84; respectively) and gray level co-occurrence (GLCM) parameters: inverse difference sum and sum variance (0.01, 0.04; respectively). Among GLCM parameters 90% were significantly affected by the type of binning and 100% by the number of bins. In case of gray level run length matrix parameters 100% of metrics were affected by both preprocessing steps.

Conclusions: Volumetric and radiomic statistics are robust to image reconstruction algorithms. However, all radiomic variables were affected by preprocessing steps therefore, showing the need for standardization before being implemented into everyday clinical practice.

Keywords: Computer-assisted image analysis; Coronary atherosclerosis; Image reconstruction; Multislice computed tomography; Reproducibility of results.

MeSH terms

Aged
Algorithms*
Computed Tomography Angiography / methods*
Coronary Angiography / methods*
Coronary Artery Disease / diagnostic imaging*
Coronary Vessels / diagnostic imaging*
Female
Humans
Male
Middle Aged
Multidetector Computed Tomography / methods*
Plaque, Atherosclerotic*
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted / methods*
Reproducibility of Results
Retrospective Studies