title: Alternative input devices for efficient navigation of large CT angiography data sets.
abstract: OBJECTIVE
  To compare devices for the task of navigating through large computed tomographic (CT) data sets at a picture archiving and communication system workstation.
  
  
  METHODS
  The institutional review board approved this study, and all subjects provided informed consent. Five radiologists were asked to find 25 different vascular targets in three CT angiography data sets (average number of sections, 1025) by using several devices (trackball, tablet, jog-shuttle wheel, and mouse). For each trial, the total time to acquire the targets (T1) was recorded. A secondary study in which 13 nonradiologists performed seven trials with an artificial target inserted at a random location in the same image data was also performed. For each trial, the following items were recorded: time until first target sighting (t2), time to manipulate the device after seeing the target, sections traversed during t2 (d1), time from first sight to target acquisition (t4), sections traversed during t4 (d2), and total trial time. Statistical analysis involved repeated-measures analysis of variance (ANOVA) and pairwise comparisons.
  
  
  RESULTS
  Repeated-measures ANOVA revealed that the device used had a significant (P < .05) effect on T1. Pairwise comparisons revealed that the trackball was significantly slower than the tablet (P < .05) and marginally slower than the jog-shuttle wheel (P < .10). Further repeated-measures ANOVA for each secondary outcome measure revealed significant differences between devices for all outcome measures (P < .005). Pairwise comparisons revealed the trackball to be significantly slower than the other devices in all measures (P < .05). The trackball was significantly (P < .05) more accurate than the other devices for d1 and d2.
  
  
  CONCLUSIONS
  The trackball may not be the optimal device for navigation of large CT angiography data sets; the use of other existing devices may improve the efficiency of interpretation of these sets.
authors.affiliation: Radiology 3D Laboratory, James H. Clark Center, Stanford University School of Medicine, 318 Campus Dr, Rm S323, Stanford, CA 94305-5450, USA. sherbond@stanford.edu
authors.forename: Anthony J
authors.initials: AJ
authors.lastname: Sherbondy
authors.forename: Djamila
authors.initials: D
authors.lastname: Holmlund
authors.forename: Geoffrey D
authors.initials: GD
authors.lastname: Rubin
authors.forename: Pamela K
authors.initials: PK
authors.lastname: Schraedley
authors.forename: Terry
authors.initials: T
authors.lastname: Winograd
authors.forename: Sandy
authors.initials: S
authors.lastname: Napel
mesh_terms: D000792:Angiography / Q000295:instrumentation*; D003627:Data Interpretation, Statistical; D006801:Humans; D011897:Random Allocation; D011795:Surveys and Questionnaires; D014057:Tomography, X-Ray Computed / Q000295:instrumentation*