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.
Pickhardt, P.J., Lubner, M.G., Kim, D.H., et al. (2012) Abdominal CT with Model-Based Iterative Reconstruction (MBIR): Initial Results of a Prospective Trial Comparing Ultralow-Dose with Standard-Dose Imaging. American Journal of Roentgenology, 199, 1266-1274. http://dx.doi.org/10.2214/AJR.12.9382
Khawaja, R.D.A., Singh, S., Otrakji, A., et al. (2014) Dose Reduction in Pediatric Abdominal CT: Use of Iterative Reconstruction Techniques across Different CT Platforms. Pediatric Radiology, 45, 1046-1055.
Rampinelli, C., Origgi, D., Vecchi, V., et al. (2015) Ultra-Low-Dose CT with Model-Based Iterative Reconstruction (MBIR): Detection of Ground-Glass Nodules in an Anthropomorphic Phantom Study. Radiologia Medica, 120, 611-617.
Nishizawa, M., Tanaka, H., Watanabe, Y., et al. (2014) Model-Based Iterative Reconstruction for Detection of Subtle Hypoattenuation in Early Cerebral Infarction: A Phantom Study. Japanese Journal of Radiology, 33, 26-32. http://dx.doi.org/10.1007/s11604-014-0376-z
Hu, M.-Q., Li, M., Liu, Z.-Y., et al. (2015) Image Quality Evaluation of Iterative Model Reconstruction on Low Tube Voltage (80 kVp) Coronary CT Angiography in an Animal Study. Acta Radiologica, pii: 0284185114568909.
Gramer, B.M., Muenzel, D., Leber, V., et al. (2012) Impact of Iterative Reconstruction on CNR and SNR in Dynamic Myocardial Perfusion Imaging in an Animal Model. European Radiology, 22, 2654-2661. http://dx.doi.org/10.1007/s00330-012-2525-z
Caywood, D., Paxton, B., Boll, D., et al. (2014) Effects of Model-Based Iterative Reconstruction on Image Quality for Low-Dose Computed Tomographic Angiography of the Thoracic Aorta in a Swine Model. Journal of Computer Assisted Tomography, 39, 196-201.
Hwang, H.J., Seo, J.B., Lee, H.J., et al. (2013) Low-Dose Chest Computed Tomography with Sinogram-Affirmed Iterative Reconstruction, Iterative Reconstruction in Image Space, and Filtered Back Projection: Studies on Image Quality. Journal of Computer Assisted Tomography, 37, 610-617. http://dx.doi.org/10.1097/RCT.0b013e31828f4dae
Lee, S.H., Kim, M.J., Yoon, C.S., et al. (2012) Radiation Dose Reduction with the Adaptive Statistical Iterative Reconstruction (ASIR) Technique for Chest CT in Children: An Intra-Individual Comparison. European Journal of Radiology, 81, e938-e943. http://dx.doi.org/10.1016/j.ejrad.2012.06.013
Ren, Q., Dewan, S.K., Li, M., et al. (2012) Comparison of Adaptive Statistical Iterative and Filtered Back Projection Reconstruction Techniques in Brain CT. European Journal of Radiology, 81, 2597-2601. http://dx.doi.org/10.1016/j.ejrad.2011.12.041
Vardhanabhuti, V., Ilyas, S., Gutteridge, C., et al. (2013) Comparison of Image Quality between Filtered Back-Projection and the Adaptive Statistical and Novel Model-Based Iterative Reconstruction Techniques in Abdominal CT for Renal Calculi. Insights Imaging, 4, 661-669. http://dx.doi.org/10.1007/s13244-013-0273-5
Katsura, M., Sato, J., Akahane, M., et al. (2013) Comparison of Pure and Hybrid Iterative Reconstruction Techniques with Conventional Filtered Back Projection: Image Quality Assessment in the Cervicothoracic Region. European Journal of Radiology, 82, 356-360. http://dx.doi.org/10.1016/j.ejrad.2012.11.004
Ichikawa, Y., Kitagawa, K., Nagasawa, N., et al. (2013) CT of the Chest with Model-Based, Fully Iterative Reconstruction: Comparison with Adaptive Statistical Iterative Reconstruction. BMC Medical Imaging, 13, 27. http://dx.doi.org/10.1186/1471-2342-13-27
Mueck, F.G., Roesch, S., Scherr, M., et al. (2015) How Low Can We Go in Contrast-Enhanced CT Imaging of the Chest? A Dose-Finding Cadaver Study Using the Model-Based Iterative Image Reconstruction Approach. Academic Radiology, 22, 345-356. http://dx.doi.org/10.1016/j.acra.2014.10.008
Yamada, Y., Jinzaki, M., Hosokawa, T., et al. (2012) Dose Reduction in Chest CT: Comparison of the Adaptive Iterative Dose Reduction 3D, Adaptive Iterative Dose Reduction, and Filtered Back Projection Reconstruction Techniques. European Journal of Radiology, 81, 4185-4195. http://dx.doi.org/10.1016/j.ejrad.2012.07.013
Wang, R., Yu, W., Wu, R., et al. (2012) Improved Image Quality in Dual-Energy Abdominal CT: Comparison of Iterative Reconstruction in Image Space and Filtered Back Projection Reconstruction. American Journal of Roentgenology, 199, 402-406. http://dx.doi.org/10.2214/AJR.11.7159
Gravante, G., Ong, S.L., Metcalfe, M.S., et al. (2011) The Porcine Hepatic Arterial Supply, Its Variations and Their Influence on the Extracorporeal Perfusion of the Liver. Journal of Surgical Research, 168, 56-61. http://dx.doi.org/10.1016/j.jss.2009.09.050
Martins, A.C. de A., Machado, M.A.C. and Ferraz, á.A.B. (2008) Porcine Liver: Experimental Model for the IntraHepatic Glissonian Approach. Acta Cirurgica Brasileira, 23, 204-207. http://dx.doi.org/10.1590/S0102-86502008000200015