Márton Kolossváry1, Bálint Szilveszter2, Júlia Karády2, Zsófia Dóra Drobni2, Béla Merkely2, Pál Maurovich-Horvat2. 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.
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.
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.
Authors: Andrew Lin; Márton Kolossváry; Jeremy Yuvaraj; Sebastien Cadet; Priscilla A McElhinney; Cathy Jiang; Nitesh Nerlekar; Stephen J Nicholls; Piotr J Slomka; Pál Maurovich-Horvat; Dennis T L Wong; Damini Dey Journal: JACC Cardiovasc Imaging Date: 2020-08-26
Authors: Márton Kolossváry; Júlia Karády; Yasuka Kikuchi; Alexander Ivanov; Christopher L Schlett; Michael T Lu; Borek Foldyna; Béla Merkely; Hugo J Aerts; Udo Hoffmann; Pál Maurovich-Horvat Journal: Radiology Date: 2019-08-06 Impact factor: 11.105