Investigation of Noise Level and Spatial Resolution of CT Images Filtered with a Selective Mean Filter and Its Comparison to an Adaptive Statistical Iterative Reconstruction

Document Type : Original Paper

Authors

1 Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia

2 Department of Radiology, Indriati Hospital Solo Baru, Jl. Palem Raya, Dusun III, Sukoharjo 57552, Central Java, Indonesia

Abstract

Introduction: A simple noise reduction algorithm, i.e. a selective mean filter (SMF), had been previously introduced. The aim of this study is to investigate the image qualities filtered by a SMF and its comparison to an adaptive statistical iterative reconstruction (ASIR).
Material and Methods: To assess the basic image quality, an American Association of Physicists in Medicine Computed Tomography (AAPM CT) performance phantom was used. The phantom was scanned by 128 Multiple Slices Computed Tomography. The tube current varied from 50 mA to 100, 150, and 200 mA. The images of a phantom were reconstructed by filtered back projection (FBP) followed by SMF and ASIR (20, 40, 60, 80, and 100%). The image quality assessment was in terms of noise level, noise power spectrum (NPS), and modulation transfer function (MTF).
Results: The noise level and NPS of SMF was similar with ASIR 100%. The values of the MTF10 of the ASIR filter at any level and SMF were comparable. The MTF10 values of ASIR 60%, and SMF with 50 mA (low) were 0.76 ± 0.02 and 0.75 ± 0.02 cycle/mm, respectively. Meanwhile, the MTF10 of ASIR 60% and SMF with 200 mA (high) were 0.74 ± 0.00 and 0.73 ± 0.00 cycles/mm, respectively.
Conclusion: Our results indicated that the performance of the SMF in reducing noise is equivalent to the maximum level of ASIR strength, i.e., ASIR 100%.

Keywords

Main Subjects

References

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Iranian Journal of Medical Physics
Volume 18, Issue 5
September and October 2021
Pages 376-383

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History

  • Receive Date: 16 May 2020
  • Revise Date: 15 July 2020
  • Accept Date: 26 July 2020

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