Statistical characterization of noise for spatial standardization of CT scans: Enabling comparison with multiple kernels and doses

Vegas Sánchez-Ferrero, Gonzalo and Ledesma Carbayo, Maria Jesus and Washko, George R. and San José Estépar, Raúl (2017). Statistical characterization of noise for spatial standardization of CT scans: Enabling comparison with multiple kernels and doses. "Medical Image Analysis", v. 40 ; pp. 44-59. ISSN 1361-8415. https://doi.org/10.1016/j.media.2017.06.001.

Description

Title: Statistical characterization of noise for spatial standardization of CT scans: Enabling comparison with multiple kernels and doses
Author/s:
  • Vegas Sánchez-Ferrero, Gonzalo
  • Ledesma Carbayo, Maria Jesus
  • Washko, George R.
  • San José Estépar, Raúl
Item Type: Article
Título de Revista/Publicación: Medical Image Analysis
Date: 1 August 2017
Volume: 40
Subjects:
Freetext Keywords: Computerized tomography, non-stationary noise, statistical characterization
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (8MB) | Preview

Abstract

Computerized tomography (CT) is a widely adopted modality for analyzing directly or indirectly functional, biological and morphological processes by means of the image characteristics. However, the potential utilization of the information obtained from CT images is often limited when considering the analysis of quantitative information involving different devices, acquisition protocols or reconstruction algorithms. Although CT scanners are calibrated as a part of the imaging workflow, the calibration is circumscribed to global reference values and does not circumvent problems that are inherent to the imaging modality. One of them is the lack of noise stationarity, which makes quantitative biomarkers extracted from the images less robust and stable. Some methodologies have been proposed for the assessment of non-stationary noise in reconstructed CT scans. However, those methods focused on the non-stationarity only due to the reconstruction geometry and are mainly based on the propagation of the variance of noise throughout the whole reconstruction process. Additionally, the philosophy followed in the state-of-the-art methods is based on the reduction of noise, but not in the standardization of it. This means that, even if the noise is reduced, the statistics of the signal remain non-stationary, which is insufficient to enable comparisons between different acquisitions with different statistical characteristics. In this work, we propose a statistical characterization of noise in reconstructed CT scans that leads to a versatile statistical model that effectively characterizes different doses, reconstruction kernels, and devices. The statistical model is generalized to deal with the partial volume effect via a localized mixture model that also describes the non-stationarity of noise. Finally, we propose a stabilization scheme to achieve stationary variance. The validation of the proposed methodology was performed with a physical phantom and clinical CT scans acquired with different configurations (kernels, doses, algorithms including iterative reconstruction). The results confirmed its suitability to enable comparisons with different doses, and acquisition protocols

Funding Projects

TypeCodeAcronymLeaderTitle
FP7291820MVISIONUnspecifiedMVISION
Government of SpainTEC-2013-48251-C2-2-RUnspecifiedUnspecifiedUnspecified

More information

Item ID: 50704
DC Identifier: http://oa.upm.es/50704/
OAI Identifier: oai:oa.upm.es:50704
DOI: 10.1016/j.media.2017.06.001
Official URL: https://www.sciencedirect.com/science/article/pii/S1361841517300889?via%3Dihub
Deposited by: Memoria Investigacion
Deposited on: 14 May 2018 16:43
Last Modified: 01 Jun 2019 22:30
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM