One-mSv CT colonography: Effect of different iterative reconstruction algorithms on radiologists' performance

Purpose: To analyze the effect of different reconstruction algorithms on image noise and radiologists' performance at ultra-low dose CT colonography (CTC) in human subjects. Materials and methods: This retrospective study had institutional review board approval, with waiver of the need to obtain informed consent. CTC and subsequent colonoscopy were performed at the same day in 28 patients. CTC was scanned at the supine/prone positions using 120/100 kVp and fixed 10 mAs, and reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based IR (Veo) algorithms. Size-specific dose estimates (SSDE) and effective radiation doses were recorded. Image noise was compared among the three datasets using repeated measures analysis of variance (ANOVA). Per-polyp sensitivity and figure-of-merits were compared among the datasets using the McNemar test and jackknife alternative free-response receiver operating characteristic (JAFROC) analysis, respectively, by one novice and one expert reviewer in CTC. Results: Mean SSDE and effective radiation dose of CTC were 1.732 mGy and 1.002 mSv, respectively. Mean image noise at supine/prone position datasets was significantly lowest with Veo (17.2/13.3), followed by ASIR (52.4/38.9) and FBP (69.9/50.8) (P < 0.0001). Forty-two polyps in 25 patients were reference polyps. For both readers, per-polyp sensitivity of all 42 polyps was highest with Veo reconstruction (81.0%, 64.3%), followed by ASIR (73.8%, 54.8%) and FBP (57.1%, 50.0%) with statistical significance between Veo and FBP for reader 1 (P = 0.002). JAFROC analysis revealed that the figure-of-merit for the detection of polyps was highest with Veo (0.917, 0.786), followed by ASIR (0.881, 0.750) and FBP (0.750, 0.746) with statistical significances between Veo or ASIR and FBP for reader 1 (P < 0.05). Conclusion: One-mSv CTC was not feasible using the standard FBP algorithm. However, diagnostic performance expressed as per-polyp sensitivity and figures-of-merit can be improved with the application of IR algorithms, particularly Veo. (C) 2016 Elsevier Ireland Ltd. All rights reserved.