Literature DB >> 20118719

Computer-aided detection of lung nodules: influence of the image reconstruction kernel for computer-aided detection performance.

Jiyoung Hwang1, Myung Jin Chung, Younga Bae, Kyung Min Shin, Sun Young Jeong, Kyung Soo Lee.   

Abstract

OBJECTIVE: To evaluate the relationship between a computed tomographic reconstruction kernel and the sensitivity of a computer-aided detection (CAD) system for lung nodule detection.
METHODS: We retrospectively studied 36 consecutive patients with no known pulmonary nodules who underwent low-dose computed tomography for lung cancer screening with 3 different reconstruction kernels (B, C, and L). All series were reviewed with a commercial CAD system for lung nodule detection.
RESULTS: The 36 scans showed 231 uncalcified nodules (170 micronodules and 61 nodules). There was little variation of sensitivities for each series (82%, 88%, and 82% for the nodules of B, C, and L, respectively). When the results of 2 series were combined, sensitivities were boosted (B + C, 89%; B + L, 95%; and C + L, 96% for the nodules).
CONCLUSIONS: Sensitivity of the CAD system was influenced by the selection of the reconstruction kernel. By combining data from 2 different kernels, CAD sensitivity can be elevated without further patient radiation exposure.

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Year:  2010        PMID: 20118719     DOI: 10.1097/RCT.0b013e3181b5c630

Source DB:  PubMed          Journal:  J Comput Assist Tomogr        ISSN: 0363-8715            Impact factor:   1.826


  3 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.  Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography.

Authors:  Kyung Nyeo Jeon; Jin Mo Goo; Chang Hyun Lee; Youkyung Lee; Ji Yung Choo; Nyoung Keun Lee; Mi-Suk Shim; In Sun Lee; Kwang Gi Kim; David S Gierada; Kyongtae T Bae
Journal:  Invest Radiol       Date:  2012-08       Impact factor: 6.016

3.  Fuzzy Clustering Applied to ROI Detection in Helical Thoracic CT Scans with a New Proposal and Variants.

Authors:  Alfonso Castro; Alberto Rey; Carmen Boveda; Bernardino Arcay; Pedro Sanjurjo
Journal:  Biomed Res Int       Date:  2016-07-18       Impact factor: 3.411

  3 in total

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