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Journal of Digital Imaging

, Volume 24, Issue 3, pp 478–484 | Cite as

Compare Display Schemes for Lung Nodule CT Screening

  • Xiao Hui WangEmail author
  • Janet E. Durick
  • Amy Lu
  • David L. Herbert
  • Carl R. Fuhrman
  • Joan M. Lacomis
  • Cynthia A. Britton
  • Diane C. Strollo
  • Sherry S. Shang
  • Saraswathi K. Golla
  • Walter F. Good
Article

Abstract

This study investigated the relative efficiencies of a stereographic display and two monoscopic display schemes for detecting lung nodules in chest computed tomography (CT). The ultimate goal was to determine whether stereoscopic display provides advantages for visualization and interpretation of three-dimensional (3D) medical image datasets. A retrospective study that compared lung nodule detection performances achieved using three different schemes for displaying 3D CT data was conducted. The display modes included slice-by-slice, orthogonal maximum intensity projection (MIP), and stereoscopic display. One hundred lung-cancer screening CT examinations containing 647 nodules were interpreted by eight radiologists, in each of the display modes. Reading times and displayed slab thickness versus time were recorded, as well as the probability, location, and size for each detected nodule. Nodule detection performance was analyzed using the receiver operating characteristic method. The stereo display mode provided higher detection performance with a shorter interpretation time, as compared to the other display modes tested in the study, although the difference was not statistically significant. The analysis also showed that there was no difference in the patterns of displayed slab thickness versus time between the stereo and MIP display modes. Most radiologists preferred reading the 3D data at a slab thickness that corresponded to five CT slices. Our results indicate that stereo display has the potential to improve radiologists' performance for detecting lung nodules in CT datasets. The experience gained in conducting the study also strongly suggests that further benefits can be achieved through providing readers with additional functionality.

Key words

Volume visualization lung neoplasm image display image interpretation 

Notes

Acknowledgement

This work is sponsored in part by the US Army Medical Research Acquisition Center, 820 Chandler Street, Fort Detrick, MD 21702-5014, under Contract PR043488. The content of the contained information does not necessarily reflect the position or the policy of the government, and no official endorsement should be inferred.

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Copyright information

© Society for Imaging Informatics in Medicine 2010

Authors and Affiliations

  • Xiao Hui Wang
    • 1
    Email author
  • Janet E. Durick
    • 2
  • Amy Lu
    • 3
  • David L. Herbert
    • 2
  • Carl R. Fuhrman
    • 2
  • Joan M. Lacomis
    • 2
  • Cynthia A. Britton
    • 2
  • Diane C. Strollo
    • 2
  • Sherry S. Shang
    • 4
  • Saraswathi K. Golla
    • 2
  • Walter F. Good
    • 1
  1. 1.Image Research GroupUniversity of Pittsburgh, School of MedicinePittsburghUSA
  2. 2.UPMC RadiologyUniversity of Pittsburgh, School of MedicinePittsburghUSA
  3. 3.UPMC RadiologyUniversity of Pittsburgh, School of MeidicinePittsburghUSA
  4. 4.Department of RadiologyHahnemann University Hospital, Drexel University College of MedicinePhiladelphiaUSA

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