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Implementation and initial experience with an interactive eye-tracking system for measuring radiologists' visual search in diagnostic tasks using volumetric CT images

Authors
  • Gong, Hao
  • Hsieh, Scott S.
  • Holmes, David
  • Cook, David
  • Inoue, Akitoshi
  • Bartlett, David
  • Baffour, Francis
  • Takahashi, Hiroaki
  • Leng, Shuai
  • Yu, Lifeng
  • Fletcher, Joel
  • McCollough, Cynthia
Type
Published Article
Publisher
SPIE
Volume
12031
Pages
120310–120310
Identifiers
DOI: 10.1117/12.2611808
Source
SPIE
License
Yellow

Abstract

Eye-tracking techniques can be used to understand the visual search process in diagnostic radiology. Nonetheless, most prior eye-tracking studies in CT only involved single cross-sectional images or video playback of the reconstructed volume and meanwhile applied strong constraints to reader-image interactivity, yielding a disconnection between the corresponding experimental setup and clinical reality. To overcome this limitation, we developed an eye-tracking system that integrates eye-tracking hardware with in-house-built image viewing software. This system enabled recording of radiologists’ real-time eye-movement and interactivity with the displayed images in clinically relevant tasks. In this work, the system implementation was demonstrated, and the spatial accuracy of eye-tracking data was evaluated using digital phantom images and patient CT angiography exam. The measured offset between targets and gaze points was comparable to that of many prior eye-tracking systems (The median offset: phantom – visual angle ~0.8; patient CTA – visual angle ~0.7 – 1.3). Further, the eye-tracking system was used to record radiologists’ visual search in a liver lesion detection task with contrast-enhanced abdominal CT. From the measured data, several variables were found to correlate with radiologists’ sensitivity, e.g., mean sensitivity of readers with longer interpretation time was higher than that of the others (88 3% vs 78 10%; p < 0.001). In summary, the proposed eye-tracking system has the potential of providing high-quality data to characterize radiologists’ visual-search process in clinical CT tasks.

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