Literature DB >> 25979210

Low contrast detectability performance of model observers based on CT phantom images: kVp influence.

I Hernandez-Giron1, A Calzado2, J Geleijns3, R M S Joemai3, W J H Veldkamp3.   

Abstract

This paper studies low contrast detectability (LCD) performance of two model observers in CT phantom images acquired at different kVp levels and compares the results with humans in a 2-alternative forced choice experiment (2-AFC). Images of the Catphan phantom with objects of different contrasts (0.5 and 1%) and diameters (2-15 mm) were acquired in an Aquilion ONE 320-detector row CT (Toshiba Medical Systems, Tokyo, Japan), in two experiments, selecting (80-100-120-135 kV) with fixed mAs and varying the mAs to keep the dose constant, respectively. Four human observers evaluated the objects visibility obtaining a proportion correct (PC) for each case. LCD was also analyzed with two model observers (non-prewhitening matched filter with an eye filter, NPWE, and channelized Hotelling observer with Gabor channels, CHO). Object contrast was affected by kV, with differences up to 17% between the lowest and highest kV. Both models overestimated human performance and were corrected by efficiency and internal noise factors. The NPWE model reproduced better the human PC values trends showing Pearson's correlation coefficients ≥0.976 (0.954-0.987, 95% CI) for both experiments, whereas for CHO they were ≥0.706 (0.493-0.839). Bland-Altman plots showed better agreement between NPWE and humans being the average difference Δ and the range of the differences Δ±2σ (σ, standard deviation) of Δ=-0.3%, Δ±2σ = [-4.0%,4.5%]. For CHO, Δ=-1.2%, Δ± 2σ= [-10.7%,8.3%]. The NPWE model can be a useful tool to predict human performance in CT low contrast detection tasks in a standard phantom and be potentially used in protocol optimization based on kV selection.
Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Computed tomography; Image quality; Low contrast; Model observer; kV

Mesh:

Year:  2015        PMID: 25979210     DOI: 10.1016/j.ejmp.2015.04.012

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  3 in total

1.  Channelized Hotelling observer correlation with human observers for low-contrast detection in liver CT images.

Authors:  Alexandre Ba; Craig K Abbey; Damien Racine; Anaïs Viry; Francis R Verdun; Sabine Schmidt; François O Bochud
Journal:  J Med Imaging (Bellingham)       Date:  2019-05-20

2.  Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages.

Authors:  André Euler; Bram Stieltjes; Zsolt Szucs-Farkas; Reto Eichenberger; Clemens Reisinger; Anna Hirschmann; Caroline Zaehringer; Achim Kircher; Matthias Streif; Sabine Bucher; David Buergler; Luigia D'Errico; Sebastién Kopp; Markus Wilhelm; Sebastian T Schindera
Journal:  Eur Radiol       Date:  2017-04-03       Impact factor: 5.315

3.  Deep-learning model observer for a low-contrast hepatic metastases localization task in computed tomography.

Authors:  Hao Gong; Joel G Fletcher; Jay P Heiken; Michael L Wells; Shuai Leng; Cynthia H McCollough; Lifeng Yu
Journal:  Med Phys       Date:  2021-12-01       Impact factor: 4.506

  3 in total

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