All Title Author
Keywords Abstract

Comparison of CT Dose Reduction Algorithms in a Porcine Model

DOI: 10.4236/act.2015.44008, PP. 57-65

Keywords: Computed Tomography, Dose Reduction, Iterative Reconstruction, Porcine, ASIR, VEO, FBP

Full-Text   Cite this paper   Add to My Lib


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.


[1]  Brenner, D.J. and Hall, E.J. (2007) Computed Tomography—An Increasing Source of Radiation Exposure. The New England Journal of Medicine, 357, 2277-2284.
[2]  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.
[3]  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.
[4]  Mathieu, K.B., Ai, H., Fox, P.S., et al. (2014) Radiation Dose Reduction for CT Lung Cancer Screening Using ASIR and MBIR: A Phantom Study. Journal of Applied Clinical Medical Physics, 15, 4515.
[5]  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.
[6]  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.
[7]  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.
[8]  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.
[9]  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.
[10]  (2000) European Guidelines for Quality Criteria for Computed Tomography. European Commission, Luxembourg.
[11]  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.
[12]  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.
[13]  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.
[14]  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.
[15]  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.
[16]  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.
[17]  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.
[18]  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.
[19]  May, M.S., Wüst, W., Brand, M., et al. (2011) Dose Reduction in Abdominal Computed Tomography. Investigative Radiology, 46, 465-470.
[20]  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.
[21]  Kamimura, K., Suda, T., Xu, W., et al. (2009) Image-Guided, Lobe-Specific Hydrodynamic Gene Delivery to Swine Liver. Molecular Therapy, 17, 491-499.
[22]  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.
[23]  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.


comments powered by Disqus

Contact Us


微信:OALib Journal