A method for quantification of noise non-uniformity in computed tomography images: A computational study

Choirul Anam; Ariij Naufal; Kosuke Matsubara; Toshioh Fujibuchi; Geoff Dougherty

doi:10.14710/jpa.v5i2.17615

DOI: https://doi.org/10.14710/jpa.v5i2.17615

A method for quantification of noise non-uniformity in computed tomography images: A computational study

*Choirul Anam

- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang, Indonesia

Ariij Naufal

- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang, Indonesia

Kosuke Matsubara

- Department of Quantum Medical Technology, Faculty of Health Sciences, Institute of Medical Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan

Toshioh Fujibuchi

- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan

Geoff Dougherty

- Department of Applied Physics and Medical Imaging, California State University Channel Islands, Camarillo, CA, United States

Received: 9 Mar 2023; Revised: 21 May 2023; Accepted: 22 May 2023; Available online: 31 May 2023; Published: 1 Jun 2023.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

BibTex Citation Data :

@article{JPA17615,
    author = {Choirul Anam and Ariij Naufal and Kosuke Matsubara and Toshioh Fujibuchi and Geoff Dougherty},
    title = {A method for quantification of noise non-uniformity in computed tomography images: A computational study},
    journal = {Journal of Physics and Its Applications},
  volume = {5},
    number = {2},
    year = {2023},
    keywords = {noise non-uniformity metric; computational phantom; gradient phantom; noise-gradient spectrum},
    abstract = {In computed tomography (CT), the noise is sometimes non-uniform, i.e. the noise magnitude may vary with the gradient level within the image. The purpose of this study was to quantify the noise non-uniformity in CT images using appropriate 1D and 2D computational phantoms, and to validate the effectiveness of the proposed concept in images filtered by the bilateral filter (BF), as an example of a non-linear filter. We first developed 1D and 2D computational phantoms, and Gaussian noises with several noise levels were then added to the phantoms. In addition, to simulate the real form of noise from images obtained in a real CT scanner, a homogeneous water phantom image was used. These noise levels were referred to as ground truth noise (σ G ). The phantoms were then filtered by the bilateral filter with various pixel value spreads (σ) to produce non-uniform noise. The original gradient phantoms ( G ) were subtracted from both the noisy phantoms ( I N  ) and the filtered noisy phantoms ( I BF  ), and the magnitudes of the resulting noise for each gradient were computed. The noise-gradient dependency (NGD) curve was used to display the dependency of noise magnitude on image gradient in the non-uniform noise. It is found that for uniform noise, the magnitude of noise was constant for all gradients. However, for non-uniform noise, the measured noise was dependent on the gradient levels and on the strength of the BF for every ground truth noise (σ G ). It was found that the noise magnitude was large for the large gradients and decreased with the magnitude of the image gradient.},
   issn = {2622-5956},   pages = {48--57}  doi = {10.14710/jpa.v5i2.17615},
    url = {https://ejournal2.undip.ac.id/index.php/jpa/article/view/17615}
}

Citation Format:

Abstract

In computed tomography (CT), the noise is sometimes non-uniform, i.e. the noise magnitude may vary with the gradient level within the image. The purpose of this study was to quantify the noise non-uniformity in CT images using appropriate 1D and 2D computational phantoms, and to validate the effectiveness of the proposed concept in images filtered by the bilateral filter (BF), as an example of a non-linear filter. We first developed 1D and 2D computational phantoms, and Gaussian noises with several noise levels were then added to the phantoms. In addition, to simulate the real form of noise from images obtained in a real CT scanner, a homogeneous water phantom image was used. These noise levels were referred to as ground truth noise (σ_G). The phantoms were then filtered by the bilateral filter with various pixel value spreads (σ) to produce non-uniform noise. The original gradient phantoms (G) were subtracted from both the noisy phantoms (I_N) and the filtered noisy phantoms (I_BF), and the magnitudes of the resulting noise for each gradient were computed. The noise-gradient dependency (NGD) curve was used to display the dependency of noise magnitude on image gradient in the non-uniform noise. It is found that for uniform noise, the magnitude of noise was constant for all gradients. However, for non-uniform noise, the measured noise was dependent on the gradient levels and on the strength of the BF for every ground truth noise (σ_G). It was found that the noise magnitude was large for the large gradients and decreased with the magnitude of the image gradient.

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Keywords: noise non-uniformity metric; computational phantom; gradient phantom; noise-gradient spectrum

Funding: Diponegoro University

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Section: Articles

Language : EN

In Vol 5, No 2 (2023): May 2023

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