Literature DB >> 21498301

Learning aspects and potential pitfalls regarding detection of pulmonary nodules in chest tomosynthesis and proposed related quality criteria.

Sara Asplund1, Ase A Johnsson, Jenny Vikgren, Angelica Svalkvist, Marianne Boijsen, Valeria Fisichella, Agneta Flinck, Asa Wiksell, Jonas Ivarsson, Hans Rystedt, Lars Gunnar Månsson, Susanne Kheddache, Magnus Båth.   

Abstract

BACKGROUND: In chest tomosynthesis, low-dose projections collected over a limited angular range are used for reconstruction of an arbitrary number of section images of the chest, resulting in a moderately increased radiation dose compared to chest radiography.
PURPOSE: To investigate the effects of learning with feedback on the detection of pulmonary nodules for observers with varying experience of chest tomosynthesis, to identify pitfalls regarding detection of pulmonary nodules, and present suggestions for how to avoid them, and to adapt the European quality criteria for chest radiography and computed tomography (CT) to chest tomosynthesis.
MATERIAL AND METHODS: Six observers analyzed tomosynthesis cases for presence of nodules in a jackknife alternative free-response receiver-operating characteristics (JAFROC) study. CT was used as reference. The same tomosynthesis cases were analyzed before and after learning with feedback, which included a collective learning session. The difference in performance between the two readings was calculated using the JAFROC figure of merit as principal measure of detectability.
RESULTS: Significant improvement in performance after learning with feedback was found only for observers inexperienced in tomosynthesis. At the collective learning session, localization of pleural and subpleural nodules or structures was identified as the main difficulty in analyzing tomosynthesis images.
CONCLUSION: The results indicate that inexperienced observers can reach a high level of performance regarding nodule detection in tomosynthesis after learning with feedback and that the main problem with chest tomosynthesis is related to the limited depth resolution.

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Year:  2011        PMID: 21498301     DOI: 10.1258/ar.2011.100378

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  8 in total

1.  Evaluation of a new system for chest tomosynthesis: aspects of image quality of different protocols determined using an anthropomorphic phantom.

Authors:  M Jadidi; A Sundin; P Aspelin; M Båth; S Nyrén
Journal:  Br J Radiol       Date:  2015-06-29       Impact factor: 3.039

2.  Effect of radiation dose level on the detectability of pulmonary nodules in chest tomosynthesis.

Authors:  Sara A Asplund; Åse A Johnsson; Jenny Vikgren; Angelica Svalkvist; Agneta Flinck; Marianne Boijsen; Valeria A Fisichella; Lars Gunnar Månsson; Magnus Båth
Journal:  Eur Radiol       Date:  2014-05-04       Impact factor: 5.315

3.  Dependency of image quality on acquisition protocol and image processing in chest tomosynthesis-a visual grading study based on clinical data.

Authors:  Masoud Jadidi; Magnus Båth; Sven Nyrén
Journal:  Br J Radiol       Date:  2018-04-09       Impact factor: 3.039

4.  Digital tomosynthesis for evaluating metastatic lung nodules: nodule visibility, learning curves, and reading times.

Authors:  Kyung Hee Lee; Jin Mo Goo; Sang Min Lee; Chang Min Park; Young Eun Bahn; Hyungjin Kim; Yong Sub Song; Eui Jin Hwang
Journal:  Korean J Radiol       Date:  2015-02-27       Impact factor: 3.500

5.  Surveillance of small, solid pulmonary nodules at digital chest tomosynthesis: data from a cohort of the pilot Swedish CArdioPulmonary bioImage Study (SCAPIS).

Authors:  Carin Meltzer; Erika Fagman; Jenny Vikgren; David Molnar; Eivind Borna; Maral Mirzai Beni; John Brandberg; Bengt Bergman; Magnus Båth; Åse A Johnsson
Journal:  Acta Radiol       Date:  2020-05-21       Impact factor: 1.990

6.  Diagnostic performance of digital tomosynthesis to evaluate silicone airway stents and related complications.

Authors:  Bo-Guen Kim; Myung Jin Chung; Byeong-Ho Jeong; Hojoong Kim
Journal:  J Thorac Dis       Date:  2021-10       Impact factor: 2.895

7.  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

8.  VISIBILITY OF STRUCTURES OF RELEVANCE FOR PATIENTS WITH CYSTIC FIBROSIS IN CHEST TOMOSYNTHESIS: INFLUENCE OF ANATOMICAL LOCATION AND OBSERVER EXPERIENCE.

Authors:  Carin Meltzer; Magnus Båth; Susanne Kheddache; Helga Ásgeirsdóttir; Marita Gilljam; Åse Allansdotter Johnsson
Journal:  Radiat Prot Dosimetry       Date:  2016-02-03       Impact factor: 0.972

  8 in total

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