Evidence map›Paper›PMID 34534365›Full record

ArticleMedical physics2021

An interactive eye-tracking system for measuring radiologists' visual fixations in volumetric CT images: Implementation and initial eye-tracking accuracy validation.

Hao Gong, Scott S Hsieh, David R Holmes, David A Cook, Akitoshi Inoue, David J Bartlett, Francis Baffour, Hiroaki Takahashi, Shuai Leng, Lifeng Yu and 2 more

Open access · greenAbstract read
In one paragraph

Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.9field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 1 country.

Hao GongDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Scott S HsiehDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
David R HolmesDepartment of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA.
David A CookDepartment of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Akitoshi InoueDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
David J BartlettDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Francis BaffourDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Hiroaki TakahashiDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Shuai LengDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Lifeng YuDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Cynthia H McColloughDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Joel G FletcherDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Mayo Clinic in Florida · USMayo Clinic · USMayo Clinic in Arizona · US

Funding

Trade-offs in human observer performance, image quality metrics, and patient doseR01EB017095 · NIBIB · MAYO CLINIC ROCHESTER · PI Hao Gong, Cynthia H McCollough · 2012 to 2026
$7.0M
NIBIB NIH HHS R01 EB017095NIH HHS R01 EB017095
6 · The paper itself

Abstract

purposeEye-tracking approaches have been used to understand the visual search process in radiology. However, previous eye-tracking work in computer tomography (CT) has been limited largely to single cross-sectional images or video playback of the reconstructed volume, which do not accurately reflect radiologists' visual search activities and their interactivity with three-dimensional image data at a computer workstation (e.g., scroll, pan, and zoom) for visual evaluation of diagnostic imaging targets. We have developed a platform that integrates eye-tracking hardware with in-house-developed reader workstation software to allow monitoring of the visual search process and reader-image interactions in clinically relevant reader tasks. The purpose of this work is to validate the spatial accuracy of eye-tracking data using this platform for different eye-tracking data acquisition modes.

methodsAn eye-tracker was integrated with a previously developed workstation designed for reader performance studies. The integrated system captured real-time eye movement and workstation events at 1000 Hz sampling frequency. The eye-tracker was operated either in head-stabilized mode or in free-movement mode. In head-stabilized mode, the reader positioned their head on a manufacturer-provided chinrest. In free-movement mode, a biofeedback tool emitted an audio cue when the head position was outside the data collection range (general biofeedback) or outside a narrower range of positions near the calibration position (strict biofeedback). Four radiologists and one resident were invited to participate in three studies to determine eye-tracking spatial accuracy under three constraint conditions: head-stabilized mode (i.e., with use of a chin rest), free movement with general biofeedback, and free movement with strict biofeedback. Study 1 evaluated the impact of head stabilization versus general or strict biofeedback using a cross-hair target prior to the integration of the eye-tracker with the image viewing workstation. In Study 2, after integration of the eye-tracker and reader workstation, readers were asked to fixate on targets that were randomly distributed within a volumetric digital phantom. In Study 3, readers used the integrated system to scroll through volumetric patient CT angiographic images while fixating on the centerline of designated blood vessels (from the left coronary artery to dorsalis pedis artery). Spatial accuracy was quantified as the offset between the center of the intended target and the detected fixation using units of image pixels and the degree of visual angle.

resultsThe three head position constraint conditions yielded comparable accuracy in the studies using digital phantoms. For Study 1 involving the digital crosshairs, the median ± the standard deviation of offset values among readers were 15.2 ± 7.0 image pixels with the chinrest, 14.2 ± 3.6 image pixels with strict biofeedback, and 19.1 ± 6.5 image pixels with general biofeedback. For Study 2 using the random dot phantom, the median ± standard deviation offset values were 16.7 ± 28.8 pixels with use of a chinrest, 16.5 ± 24.6 pixels using strict biofeedback, and 18.0 ± 22.4 pixels using general biofeedback, which translated to a visual angle of about 0.8° for all three conditions. We found no obvious association between eye-tracking accuracy and target size or view time. In Study 3 viewing patient images, use of the chinrest and strict biofeedback demonstrated comparable accuracy, while the use of general biofeedback demonstrated a slightly worse accuracy. The median ± standard deviation of offset values were 14.8 ± 11.4 pixels with use of a chinrest, 21.0 ± 16.2 pixels using strict biofeedback, and 29.7 ± 20.9 image pixels using general biofeedback. These corresponded to visual angles ranging from 0.7° to 1.3°.

conclusionsAn integrated eye-tracker system to assess reader eye movement and interactive viewing in relation to imaging targets demonstrated reasonable spatial accuracy for assessment of visual fixation. The head-free movement condition with audio biofeedback performed similarly to head-stabilized mode.

Indexed as

Cone-Beam Computed TomographyEye-Tracking TechnologyComputersHumansPhantoms, ImagingRadiologistsbiofeedbackcomputed tomographyeye trackingobserver performance

Identifiers

PMID34534365
PMCPMC8595866
OpenAlexW3200418665

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.