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Integration of Temporal Subtraction and Nodule Detection System for Digital Chest Radiographs into Picture Archiving and Communication System (PACS): Four-year Experience

  • Shuji SakaiEmail author
  • Hidetake Yabuuchi
  • Yoshio Matsuo
  • Takashi Okafuji
  • Takeshi Kamitani
  • Hiroshi Honda
  • Keiji Yamamoto
  • Keiichi Fujiwara
  • Naoki Sugiyama
  • Kunio Doi
Article

Abstract

Since May 2002, temporal subtraction and nodule detection systems for digital chest radiographs have been integrated into our hospital’s picture archiving and communication systems (PACS). Image data of digital chest radiographs were stored in PACS with the digital image and communication in medicine (DICOM) protocol. Temporal subtraction and nodule detection images were produced automatically in an exclusive server and delivered with current and previous images to the work stations. The problems that we faced and the solutions that we arrived at were analyzed. We encountered four major problems. The first problem, as a result of the storage of the original images’ data with the upside-down, reverse, or lying-down positioning on portable chest radiographs, was solved by postponing the original data storage for 30 min. The second problem, the variable matrix sizes of chest radiographs obtained with flat-panel detectors (FPDs), was solved by improving the computer algorithm to produce consistent temporal subtraction images. The third problem, the production of temporal subtraction images of low quality, could not be solved fundamentally when the original images were obtained with different modalities. The fourth problem, an excessive false-positive rate on the nodule detection system, was solved by adjusting this system to chest radiographs obtained in our hospital. Integration of the temporal subtraction and nodule detection system into our hospital’s PACS was customized successfully; this experience may be helpful to other hospitals.

Key words

Nodule detection system temporal subtraction picture archiving and communication systems (PACS) computed radiography (CR) flat-panel detectors (FPDs) 

Notes

Acknowledgments

[removed for blinding purposes].

References

  1. 1.
    Shah PK, Austin JH, White CS, Patel P, Haramati LB, Pearson GD, Shiau MC, Berkmen YM: Missed non-small cell lung cancer: radiographic findings of potentially resectable lesions evident only in retrospect. Radiology 226:235–241, 2003PubMedCrossRefGoogle Scholar
  2. 2.
    Kaneko M, Eguchi K, Ohmatsu H, Kakinuma R, Naruke T, Suemasu K, Moriyama N: Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography. Radiology 201:798–802, 1996PubMedGoogle Scholar
  3. 3.
    Doi K: Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 78:S3–S19, 2005PubMedCrossRefGoogle Scholar
  4. 4.
    Sakai S, Soeda H, Furuya A, Yabuuchi H, Okafuji T, Yamamoto K, Honda H, Doi K: Evaluation of the image quality of temporal subtraction images produced automatically in a PACS environment. J Digit Imaging 19(4):383–390, 2006PubMedCrossRefGoogle Scholar
  5. 5.
    Morishita J, Watanabe H, Katsuragawa S, Oda N, Sukenobu Y, Okazaki H, Nakata H, Doi K: Investigation of misfiled cases in the PACS environment and a solution to prevent filing errors for chest radiographs. Acad Radiol 12:97–103, 2005PubMedCrossRefGoogle Scholar
  6. 6.
    Kano A, Doi K, MacMahon H, Hassell DD, Giger ML: Digital image subtraction of temporally sequential chest images for detection of interval change. Med Phys 21:453–461, 1994PubMedCrossRefGoogle Scholar
  7. 7.
    Kobayashi T, Xu XW, MacMahon H, Metz CE, Doi K: Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs. Radiology 199:843–848, 1996PubMedGoogle Scholar
  8. 8.
    Katsuragawa S, Tagashira H, Li Q, et al: Comparison of the quality of temporal subtraction images obtained with manual and automated methods of digital chest radiography. J Digit Imaging 12:166–172, 1999PubMedCrossRefGoogle Scholar
  9. 9.
    Shiraishi J, Katsuragawa S, Ikezoe J, et al: Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. AJR 174:71–74, 2000PubMedGoogle Scholar
  10. 10.
    Kakeda S, Moriya J, Sato H, et al: Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system. AJR 182:505–510, 2004PubMedGoogle Scholar
  11. 11.
    Sakai S, Soeda H, Takahashi N, Okafuji T, Yoshitake T, Yabuuchi H, Yoshino I, Yamamoto K, Honda H, Doi K: Computer-aided nodule detection on digital chest radiography: validation test on consecutive T1 cases of resectable lung cancer. J Digit Imaging 19(4):376–382, 2006PubMedCrossRefGoogle Scholar
  12. 12.
    Johkoh T, Kozuka T, Tomiyama N, Hamada S, Honda O, Mihara N, Koyama M, Tsubamoto M, Maeda M, Nakamura H, Saki H, Fujiwara K: Temporal subtraction for detection of solitary pulmonary nodules on chest radiographs: evaluation of a commercially available computer-aided diagnosis system. Radiology 223:806–811, 2002PubMedCrossRefGoogle Scholar
  13. 13.
    Tsubamoto M, Johkoh T, Kozuka T, Tomiyama N, Hamada S, Honda O, Mihara N, Koyama M, Maeda M, Nakamura H, Fujiwara K: Temporal subtraction for the detection of hazy pulmonary opacities on chest radiography. AJR 179:467–471, 2002PubMedGoogle Scholar
  14. 14.
    Prokop M, Neitzel U, Schaefer-Prokop C: Principles of image processing in digital chest radiography. J Thorac Imaging 18:148–164, 2003PubMedCrossRefGoogle Scholar
  15. 15.
    Fernandez-Bayo J, Barbero O, Rubies C, Sentis M, Donoso L: Distributing medical images with internet technologies: a DICOM web server and a DICOM java viewer. Radiographics 20:581–590, 2000PubMedGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2007

Authors and Affiliations

  • Shuji Sakai
    • 1
    Email author
  • Hidetake Yabuuchi
    • 2
  • Yoshio Matsuo
    • 2
  • Takashi Okafuji
    • 2
  • Takeshi Kamitani
    • 2
  • Hiroshi Honda
    • 2
  • Keiji Yamamoto
    • 3
  • Keiichi Fujiwara
    • 3
  • Naoki Sugiyama
    • 4
  • Kunio Doi
    • 5
  1. 1.Department of Health SciencesSchool of Medicine, Kyushu UniversityFukuokaJapan
  2. 2.Department of Clinical RadiologyGraduate School of Medical Sciences, Kyushu UniversityFukuokaJapan
  3. 3.Mitsubishi Space SoftwareAmagasakiJapan
  4. 4.Toshiba Medical SystemsTokyoJapan
  5. 5.Kurt Rossmann Laboratory, Department of RadiologyThe University of ChicagoChicagoUSA

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