Quantitative kinetic analysis of lung nodules using the temporal subtraction technique in dynamic chest radiographies performed with a flat panel detector.

Early detection and treatment of lung cancer is one of the most effective means of reducing cancer mortality, and to this end, chest X-ray radiography has been widely used as a screening method. A related technique based on the development of computer analysis and a flat panel detector (FPD) has ena...

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Publicado en:Journal of Digital Imaging Vol. 22; no. 2; pp. 126 - 136
Autores principales: Tsuchiya Y, Kodera Y, Tanaka R, Sanada S
Formato: diagnostic images research tables/charts Journal Article
Publicado: Springer Nature Apr2009
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Apr2009
      vid: 22
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      pub: Springer Nature
      place: New York, New York
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        2010229055
        10.1007/s10278-008-9116-1
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        atl: Quantitative kinetic analysis of lung nodules using the temporal subtraction technique in dynamic chest radiographies performed with a flat panel detector.
      aug:
        au:
          Tsuchiya Y
          Kodera Y
          Tanaka R
          Sanada S
        affil: Department of Radiology, Shizuoka Children's Hospital, 860 Urushiyama, Aoi-ku, Shizuoka, 420-8660, Japan. yuichiro-rt@k9.dion.ne.jp
      sug:
        subj:
          Diagnosis, Computer Assisted
          Lung Neoplasms Diagnosis
          Radiographic Image Enhancement
          Radiography, Thoracic
          Algorithms
          Computer Simulation
          Evaluation Research
          Image Processing, Computer Assisted
          Kinetics
          Neural Networks (Computer)
          Respiration
          Human
      ab: Early detection and treatment of lung cancer is one of the most effective means of reducing cancer mortality, and to this end, chest X-ray radiography has been widely used as a screening method. A related technique based on the development of computer analysis and a flat panel detector (FPD) has enabled the functional evaluation of respiratory kinetics in the chest and is expected to be introduced into clinical practice in the near future. In this study, we developed a computer analysis algorithm to detect lung nodules and to evaluate quantitative kinetics. Breathing chest radiographs obtained by modified FPD and breath synchronization utilizing diaphragmatic analysis of vector movement were converted into four static images by sequential temporal subtraction processing, morphological enhancement processing, kinetic visualization processing, and lung region detection processing. An artificial neural network analyzed these density patterns to detect the true nodules and draw their kinetic tracks. Both the algorithm performance and the evaluation of clinical effectiveness of seven normal patients and simulated nodules showed sufficient detecting capability and kinetic imaging function without significant differences. Our technique can quantitatively evaluate the kinetic range of nodules and is effective in detecting a nodule on a breathing chest radiograph. Moreover, the application of this technique is expected to extend computer-aided diagnosis systems and facilitate the development of an automatic planning system for radiation therapy.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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