Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features.

Abstract

Purpose: The goal of this study was to quantify the effects of iterative reconstruction on radiomics features of normal anatomic structures on head and neck computed tomography (CT) scans. Methods: Regions of interest (ROI) containing five different tissue types and an ROI containing only air were extracted from CT scans of the head and neck from 108 patients. Each scan was reconstructed using three different reconstruction levels (2, 4, and 6) in addition to bone, thin slice (1-mm slice thickness), and thin-bone reconstructions. From each ROI in all reconstructions, 142 radiomic features were calculated. For each of the six ROIs, features were compared between combinations of iDose levels (2v4, 4v6, and 2v6) with a threshold of after correcting for multiple comparisons ( ). Features from reconstructions were also compared to bone, thin slice, and thin-bone reconstructions. Spearman's rank correlation coefficient, , quantified the relative feature value agreement across reconstructions. Results: When comparing radiomics features across the three reconstruction levels, over half of all features reflected significant differences for all tissue types, while no features demonstrated significant differences when extracted from air ROIs. When assessing feature value agreement, at least 97% of features reflected excellent agreement ( ) when comparing the three iDose levels for all ROIs. When comparing to bone, thin slice, and thin-bone reconstructions, more than half of all features demonstrated significant differences for all ROIs and of features reflected excellent agreement for all ROIs. Conclusion: Many radiomics features are dependent on the iterative reconstruction level, and the magnitude of this dependency is affected by the tissue from which features are extracted. For studies using images reconstructed using varying reconstruction levels, features robust to these should be used.