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DOI: 10.1148/radiol.2342032017
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(Radiology 2005;234:391-398.)
© RSNA, 2005


Computer Applications

Alternative Input Devices for Efficient Navigation of Large CT Angiography Data Sets1

Anthony J. Sherbondy, MS, Djamila Holmlund, BS, Geoffrey D. Rubin, MD, Pamela K. Schraedley, PhD, Terry Winograd, PhD and Sandy Napel, PhD

1 From the Radiology 3D Laboratory, James H. Clark Center, Stanford University School of Medicine, 318 Campus Dr, Rm S323, Stanford, CA 94305-5450. From the 2002 RSNA Annual Meeting. Received December 15, 2003; revision requested February 23, 2004; revision received April 30; accepted May 24. Supported by the National Institutes of Health 1R01HL67194, the Lucas Foundation, the Packard Foundation, the Phil N. Allen Trust, and the Wallenberg Foundation. Address correspondence to A.J.S. (e-mail: sherbond@stanford.edu).

PURPOSE: To compare devices for the task of navigating through large computed tomographic (CT) data sets at a picture archiving and communication system workstation.

MATERIALS AND 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.

CONCLUSION: 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.

© RSNA, 2005




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[Abstract] [Full Text] [PDF]




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