Filtered Back Projection, Adaptive Statistical Iterative Reconstruction, and a Model-based Iterative Reconstruction in Abdominal CT: An Experimental Clinical Study

To compare objective and subjective image quality parameters of three image reconstruction algorithms of different generations at routine multidetector computed tomographic (CT) examinations of the abdomen.This institutional review board-approved study included 22 consecutive patients (mean age, 56.1 years ± 15.8 [standard deviation]; mean weight, 79.1 kg ± 14.8) who underwent routine CT examinations of the abdomen. A low-contrast phantom was used for objective quality control. Raw data sets were reconstructed by using filtered back projection (FPB), adaptive statistical iterative reconstruction (ASIR), and a model-based iterative reconstruction (MBIR). Radiologists used a semiquantitative scale (-3 to +3) to rate subjective image quality and artifacts, comparing both FBP and MBIR images with ASIR images. The Wilcoxon test and the intraclass correlation coefficient were used to evaluate the data. Measurements of objective noise and CT numbers of soft tissue structures were compared with analysis of variance.The phantom study revealed an improved detectability of low-contrast targets for MBIR compared with ASIR or FBP. Subjective ratings showed higher image quality for MBIR, with better resolution (median value, 2; range, 1 to 3), lower noise (2; range, 1 to 3), and finer contours (2; range, 1 to 2) compared with ASIR (all P < .001). FBP performed inferiorly (0, range, -2 to 0]; -1 [range, -3 to 0]; 0 [range, -1 to 0], respectively; all, P < .001). Mean interobserver correlation was 0.9 for image perception and 0.7 for artifacts. Objective noise for FBP was 14%-68% higher and for MBIR was 18%-47% lower than that for ASIR (P < .001).The MBIR algorithm considerably improved objective and subjective image quality parameters of routine abdominal multidetector CT images compared with those of ASIR and FBP.