Literature DB >> 25533196

A comparison of axial versus coronal image viewing in computer-aided detection of lung nodules on CT.

Tae Iwasawa1, Sumiaki Matsumoto, Takatoshi Aoki, Fumito Okada, Yoshihiro Nishimura, Hitoshi Yamagata, Yoshiharu Ohno.   

Abstract

PURPOSE: To compare primarily viewing axial images (Axial mode) versus coronal reconstruction images (Coronal mode) in computer-aided detection (CAD) of lung nodules on multidetector computed tomography (CT) in terms of detection performance and reading time.
MATERIALS AND METHODS: Sixty CT data sets from two institutions were collected prospectively. Ten observers (6 radiologists, 4 pulmonologists) with varying degrees of experience interpreted the data sets using CAD as a second reader (performing nodule detection first without then with aid). The data sets were interpreted twice, once each for Axial and Coronal modes, in two sessions held 4 weeks apart. Jackknife free-response receiver-operating characteristic analysis was used to compare detection performances in the two modes.
RESULTS: Mean figure-of-merit values with and without aid were 0.717 and 0.684 in Axial mode and 0.702 and 0.671 in Coronal mode; use of CAD significantly increased the performance of observers in both modes (P < 0.01). Mean reading times for radiologists did not significantly differ between Axial (156 ± 74 s) and Coronal mode (164 ± 69 s; P = 0.08). Mean reading times for pulmonologists were significantly lower in Coronal (112 ± 53 s) than in Axial mode (130 ± 80 s; P < 0.01).
CONCLUSION: There was no statistically significant difference between Axial and Coronal modes for lung nodule detection with CAD.

Entities:  

Mesh:

Year:  2014        PMID: 25533196     DOI: 10.1007/s11604-014-0383-0

Source DB:  PubMed          Journal:  Jpn J Radiol        ISSN: 1867-1071            Impact factor:   2.374


  16 in total

1.  Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance.

Authors:  Kazuo Awai; Kohei Murao; Akio Ozawa; Masanori Komi; Haruo Hayakawa; Shinichi Hori; Yasumasa Nishimura
Journal:  Radiology       Date:  2004-02       Impact factor: 11.105

2.  Comparison of axial high-resolution CT and thin-section multiplanar reformation (MPR) for diagnosis of diseases of the pulmonary parenchyma: preliminary study in 49 patients.

Authors:  Hiroaki Arakawa; Kaoru Sasaka; Wo Meng Lu; Noriyuki Hirayanagi; Yasuo Nakajima
Journal:  J Thorac Imaging       Date:  2004-01       Impact factor: 3.000

3.  Observer studies involving detection and localization: modeling, analysis, and validation.

Authors:  Dev P Chakraborty; Kevin S Berbaum
Journal:  Med Phys       Date:  2004-08       Impact factor: 4.071

4.  Evaluation of thoracic abnormalities on 64-row multi-detector row CT: comparison between axial images versus coronal reformations.

Authors:  Mizuki Nishino; Takeshi Kubo; Milliam L Kataoka; Shiva Gautam; Vassilios Raptopoulos; Hiroto Hatabu
Journal:  Eur J Radiol       Date:  2006-02-15       Impact factor: 3.528

5.  Comparison between coronal reformatted images and direct coronal CT images of the swine lung specimen: assessment of image quality with 64-detector row CT.

Authors:  E J Choi; Y-W Oh; S Y Ham; K Y Lee; E-Y Kang
Journal:  Br J Radiol       Date:  2008-02-18       Impact factor: 3.039

6.  Lung nodule CAD software as a second reader: a multicenter study.

Authors:  Charles S White; Robert Pugatch; Thomas Koonce; Steven W Rust; Ekta Dharaiya
Journal:  Acad Radiol       Date:  2008-03       Impact factor: 3.173

7.  Combination of computer-aided detection algorithms for automatic lung nodule identification.

Authors:  Niccolò Camarlinghi; Ilaria Gori; Alessandra Retico; Roberto Bellotti; Paolo Bosco; Piergiorgio Cerello; Gianfranco Gargano; Ernesto Lopez Torres; Rosario Megna; Marco Peccarisi; Maria Evelina Fantacci
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-07-08       Impact factor: 2.924

8.  Potential contribution of multiplanar reconstruction (MPR) to computer-aided detection of lung nodules on MDCT.

Authors:  Sumiaki Matsumoto; Yoshiharu Ohno; Hitoshi Yamagata; Munenobu Nogami; Atsushi Kono; Kazuro Sugimura
Journal:  Eur J Radiol       Date:  2011-01-11       Impact factor: 3.528

9.  Computer-aided detection of lung nodules on multidetector CT in concurrent-reader and second-reader modes: a comparative study.

Authors:  Sumiaki Matsumoto; Yoshiharu Ohno; Takatoshi Aoki; Hitoshi Yamagata; Munenobu Nogami; Keiko Matsumoto; Yoshiko Yamashita; Kazuro Sugimura
Journal:  Eur J Radiol       Date:  2013-03-06       Impact factor: 3.528

10.  Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume.

Authors:  Yingru Zhao; Geertruida H de Bock; Rozemarijn Vliegenthart; Rob J van Klaveren; Ying Wang; Luca Bogoni; Pim A de Jong; Willem P Mali; Peter M A van Ooijen; Matthijs Oudkerk
Journal:  Eur Radiol       Date:  2012-07-20       Impact factor: 5.315

View more
  2 in total

1.  Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification.

Authors:  Sunyi Zheng; Ludo J Cornelissen; Xiaonan Cui; Xueping Jing; Raymond N J Veldhuis; Matthijs Oudkerk; Peter M A van Ooijen
Journal:  Med Phys       Date:  2020-12-30       Impact factor: 4.071

2.  Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system.

Authors:  Kunwei Li; Kunfeng Liu; Yinghua Zhong; Mingzhu Liang; Peixin Qin; Haijun Li; Rongguo Zhang; Shaolin Li; Xueguo Liu
Journal:  Quant Imaging Med Surg       Date:  2021-08
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.