The role of pure iterative reconstruction in conventional dose CT enterography.

ABSTRACT

PURPOSE: Pure iterative reconstruction (Pure IR) has been proposed as a solution to improve diagnostic quality of low dose CT images. We assess the performance of model based iterative reconstruction (MBIR) in improving conventional dose CT enterography (CTE) images. METHODS: 43 Crohn's patients (27 female) (38.5 ± 12.98 years) referred for CTE were included. Images were reconstructed with pure IR (MBIR, General Electric Healthcare) in addition to standard department protocol (reconstructed with hybrid iterative reconstruction (Hybrid IR) [60% filtered back projection/40% adaptive statistical IR (General Electric Healthcare)]. Image quality was assessed objectively and subjectively at 6 anatomical levels. Clinical interpretation was undertaken in consensus by 2 blinded radiologists along with 2 non-blinded readers ('gold standard'). Results were analyzed using Statistical Package for Social Scientists. RESULTS: Mean effective radiation dose was 6.05 ± 2.84 mSv (size specific dose estimates 9.25 ± 2.9 mGy). Objective and subjective assessment yielded 6106 data points. Pure IR images significantly outperformed those using standard reconstruction techniques across all subjective (p < 0.001 for all comparisons) (noise, contrast resolution, spatial resolution, streak artifact, axial diagnostic acceptability, coronal diagnostic acceptability) and objective (p < 0.004) (noise, signal-to-noise ratio) parameters. Clinical reads of the pure IR images agreed more closely with the gold standard reads than the hybrid IR image reads in terms of overall Crohn's activity grade (κ = 0.630, 0.308) and detection of acute complications (κ = 1.0, 0.896). Results were comparable for bowel wall disease severity assessment (κ = 0.523, 0.593). CONCLUSIONS: Pure IR considerably improves image quality of conventional dose CTE images and therefore its use should be expanded beyond low dose protocols to improving image quality at conventional dose CT imaging.