Image Resampling and Discretization Effect on the Estimate of Myocardial Radiomic Features from T1 and T2 Mapping in Hypertrophic Cardiomyopathy

Daniela Marfisi,Carlo Tessa,Chiara Marzi,Jacopo Del Meglio,Stefania Linsalata,Rita Borgheresi,Alessio Lilli,Riccardo Lazzarini,Luca Salvatori,Claudio Vignali,Andrea Barucci,Mario Mascalchi,Giancarlo Casolo,Stefano Diciotti,Antonio Claudio Traino,Marco Giannelli

Scientific reports（2022）SCI 3区

Cited 11|Views45

Abstract

Radiomics is emerging as a promising and useful tool in cardiac magnetic resonance (CMR) imaging applications. Accordingly, the purpose of this study was to investigate, for the first time, the effect of image resampling/discretization and filtering on radiomic features estimation from quantitative CMR T1 and T2 mapping. Specifically, T1 and T2 maps of 26 patients with hypertrophic cardiomyopathy (HCM) were used to estimate 98 radiomic features for 7 different resampling voxel sizes (at fixed bin width), 9 different bin widths (at fixed resampling voxel size), and 7 different spatial filters (at fixed resampling voxel size/bin width). While we found a remarkable dependence of myocardial radiomic features from T1 and T2 mapping on image filters, many radiomic features showed a limited sensitivity to resampling voxel size/bin width, in terms of intraclass correlation coefficient (> 0.75) and coefficient of variation (< 30%). The estimate of most textural radiomic features showed a linear significant (p < 0.05) correlation with resampling voxel size/bin width. Overall, radiomic features from T2 maps have proven to be less sensitive to image preprocessing than those from T1 maps, especially when varying bin width. Our results might corroborate the potential of radiomics from T1/T2 mapping in HCM and hopefully in other myocardial diseases.

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Key words

Cardiomyopathies,Data processing,Diagnostic markers,Image processing,Magnetic resonance imaging,Prognostic markers,Science,Humanities and Social Sciences,multidisciplinary

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