Comparison of the polynomial model against explicit measurements of noise components for different mammography systems.

Détails

ID Serval
serval:BIB_815217988A0B
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Comparison of the polynomial model against explicit measurements of noise components for different mammography systems.
Périodique
Physics in Medicine and Biology
Auteur(s)
Monnin P., Bosmans H., Verdun F.R., Marshall N.W.
ISSN
1361-6560 (Electronic)
ISSN-L
0031-9155
Statut éditorial
Publié
Date de publication
2014
Peer-reviewed
Oui
Volume
59
Numéro
19
Pages
5741-5761
Langue
anglais
Notes
Publication types: Journal Article Publication Status: ppublish
Résumé
Given the adverse impact of image noise on the perception of important clinical details in digital mammography, routine quality control measurements should include an evaluation of noise. The European Guidelines, for example, employ a second-order polynomial fit of pixel variance as a function of detector air kerma (DAK) to decompose noise into quantum, electronic and fixed pattern (FP) components and assess the DAK range where quantum noise dominates. This work examines the robustness of the polynomial method against an explicit noise decomposition method. The two methods were applied to variance and noise power spectrum (NPS) data from six digital mammography units. Twenty homogeneously exposed images were acquired with PMMA blocks for target DAKs ranging from 6.25 to 1600 µGy. Both methods were explored for the effects of data weighting and squared fit coefficients during the curve fitting, the influence of the additional filter material (2 mm Al versus 40 mm PMMA) and noise de-trending. Finally, spatial stationarity of noise was assessed.Data weighting improved noise model fitting over large DAK ranges, especially at low detector exposures. The polynomial and explicit decompositions generally agreed for quantum and electronic noise but FP noise fraction was consistently underestimated by the polynomial method. Noise decomposition as a function of position in the image showed limited noise stationarity, especially for FP noise; thus the position of the region of interest (ROI) used for noise decomposition may influence fractional noise composition. The ROI area and position used in the Guidelines offer an acceptable estimation of noise components. While there are limitations to the polynomial model, when used with care and with appropriate data weighting, the method offers a simple and robust means of examining the detector noise components as a function of detector exposure.
Pubmed
Web of science
Création de la notice
23/10/2014 19:47
Dernière modification de la notice
03/03/2018 18:47
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