title: Compare display schemes for lung nodule CT screening.
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.
authors.affiliation: Image Research Group, University of Pittsburgh, School of Medicine, 3362 Fifth Ave., Pittsburgh, PA 15213, USA. xwang@mail.magee.edu
authors.forename: Xiao Hui
authors.initials: XH
authors.lastname: Wang
authors.forename: Janet E
authors.initials: JE
authors.lastname: Durick
authors.forename: Amy
authors.initials: A
authors.lastname: Lu
authors.forename: David L
authors.initials: DL
authors.lastname: Herbert
authors.forename: Carl R
authors.initials: CR
authors.lastname: Fuhrman
authors.forename: Joan M
authors.initials: JM
authors.lastname: Lacomis
authors.forename: Cynthia A
authors.initials: CA
authors.lastname: Britton
authors.forename: Diane C
authors.initials: DC
authors.lastname: Strollo
authors.forename: Sherry S
authors.initials: SS
authors.lastname: Shang
authors.forename: Saraswathi K
authors.initials: SK
authors.lastname: Golla
authors.forename: Walter F
authors.initials: WF
authors.lastname: Good
mesh_terms: D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D015588:Observer Variation; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*
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