Comparison of CT Dose Reduction Algorithms in a Porcine Model

The present study utilized a porcine model for qualitative and quantitative assessment of the diagnostic quality of non-contrast abdominal computed tomography (CT) images generated by Adaptive Statistical Iterative Reconstruction (ASIR, GE Healthcare, Waukesha, Wisconsin, USA), Model-Based Iterative Reconstruction (GE company name VEO), and conventional Filtered back projection (FBP) technique. Methods: Multiple CT whole-body scans of a freshly euthanized pig carcass were performed on a 64-slice GE CT scanner at varying noise indices (5, 10, 15, 20, 30, 37, 40, 45), and with three different algorithms (VEO, FBP, and ASIR at 30%, 50%, and 70% levels of ASIR-FBP blending). Abdominal CT images were reviewed and scored in a blinded and randomized manner by two board-certified abdominal radiologists. The task was to evaluate the clarity of the images according to a rubric involving edge sharpness, presence of artifact, anatomical clarity (assessed at four regions), and perceived diagnostic acceptability. This amounted to seven criteria, each of which was graded on a scale of 1 to 5. A weighted formula was used to calculate a composite score for each scan. Results: VEO outperforms ASIR and FBP by an average of 0.5 points per the scoring system used (p < 0.05). Above a threshold noise index of 30, diagnostic acceptability is lost by all algorithms, and there is no diagnostic advantage to increasing the dose beyond a noise index of 10. Between a noise index of 25 - 30, VEO retains diagnostic acceptability, as opposed to ASIR and FBP which lose acceptability above noise index of 25. Conclusion: Model-based iterative reconstruction provides superior image quality and anatomical clarity at reduced radiation dosages, supporting the routine use of this technology, particularly in pediatric abdominal CT scans.