Literature DB >> 25740701

Computer-aided detection of artificial pulmonary nodules using an ex vivo lung phantom: influence of exposure parameters and iterative reconstruction.

Mark O Wielpütz1, Jacek Wroblewski2, Mathieu Lederlin3, Julien Dinkel2, Monika Eichinger2, M Koenigkam-Santos2, Jürgen Biederer2, Hans-Ulrich Kauczor4, Michael U Puderbach2, Bertram J Jobst2.   

Abstract

OBJECTIVES: To evaluate the influence of exposure parameters and raw-data based iterative reconstruction (IR) on the performance of computer-aided detection (CAD) of pulmonary nodules on chest multidetector computed tomography (MDCT).
MATERIAL AND METHODS: Seven porcine lung explants were inflated in a dedicated ex vivo phantom shell and prepared with n=162 artificial nodules of a clinically relevant volume and maximum diameter (46-1063 μl, and 6.2-21.5 mm). n=118 nodules were solid and n=44 part-solid. MDCT was performed with different combinations of 120 and 80 kV with 120, 60, 30 and 12 mA*s, and reconstructed with both filtered back projection (FBP) and IR. Subsequently, 16 datasets per lung were subjected to dedicated CAD software. The rate of true positive, false negative and false positive CAD marks was measured for each reconstruction.
RESULTS: The rate of true positive findings ranged between 88.9-91.4% for FBP and 88.3-90.1% for IR (n.s.) with most exposure settings, but was significantly lower with the combination of 80 kV and 12 mA*s (80.9% and 81.5%, respectively, p<0.05). False positive findings ranged between 2.3-8.1 annotations per lung. For nodule volumes <200 μl the rate of true positives was significantly lower than for >300 μl (p<0.05). Similarly, it was significantly lower for diameters <12 mm compared to ≥12 mm (p<0.05). The rate of true positives for solid and part-solid nodules was similar.
CONCLUSIONS: Nodule CAD on chest MDCT is robust over a wide range of exposure settings. Noise reduction by IR is not detrimental for CAD, and may be used to improve image quality in the setting of low-dose MDCT for lung cancer screening.
Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  CAD; Computer-aided diagnosis; Low-dose CT; Lung cancer screening

Mesh:

Year:  2015        PMID: 25740701     DOI: 10.1016/j.ejrad.2015.01.025

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  5 in total

1.  A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density.

Authors:  Hajime Kobayashi; Masaki Ohkubo; Akihiro Narita; Janaka C Marasinghe; Kohei Murao; Toru Matsumoto; Shusuke Sone; Shinichi Wada
Journal:  Br J Radiol       Date:  2017-01-03       Impact factor: 3.039

2.  The effect of radiation dose reduction on computer-aided detection (CAD) performance in a low-dose lung cancer screening population.

Authors:  Stefano Young; Pechin Lo; Grace Kim; Matthew Brown; John Hoffman; William Hsu; Wasil Wahi-Anwar; Carlos Flores; Grace Lee; Frederic Noo; Jonathan Goldin; Michael McNitt-Gray
Journal:  Med Phys       Date:  2017-03-14       Impact factor: 4.071

3.  Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario.

Authors:  Alan A Peters; Adrian T Huber; Verena C Obmann; Johannes T Heverhagen; Andreas Christe; Lukas Ebner
Journal:  Eur Radiol       Date:  2022-01-21       Impact factor: 5.315

4.  Detection of artificial pulmonary lung nodules in ultralow-dose CT using an ex vivo lung phantom.

Authors:  Caroline Alexandra Burgard; Thomas Gaass; Madeleine Bonert; David Bondesson; Natalie Thaens; Maximilian Ferdinand Reiser; Julien Dinkel
Journal:  PLoS One       Date:  2018-01-03       Impact factor: 3.240

5.  Influence of exposure parameters and iterative reconstruction on automatic airway segmentation and analysis on MDCT-An ex vivo phantom study.

Authors:  Patricia Leutz-Schmidt; Oliver Weinheimer; Bertram J Jobst; Julien Dinkel; Jürgen Biederer; Hans-Ulrich Kauczor; Michael U Puderbach; Mark O Wielpütz
Journal:  PLoS One       Date:  2017-08-02       Impact factor: 3.240

  5 in total

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