The assessment of stroke multidimensional CT and MR imaging using eye movement analysis: does modality preference enhance observer performance?
conference contributionposted on 2010-06-02, 15:28 authored by Lindsey Cooper, Alastair Gale, Janak Saada, Swamy Gedela, Hazel J. Scott, Andoni Toms
Although CT and MR imaging is now commonplace in the radiology department, few studies have examined complex interpretative tasks such as the reading of multidimensional brain CT or MRI scans from the observer performance perspective, especially with reference to Stroke. Modality performance studies have demonstrated a similar sensitivity of less than 50% for both conventional modalities, with neither modality proving superior to the other in Stroke observer performance tasks (Mohr, 1995; Lansberg, 2000; Wintermark, 2007). Visual search studies have not extensively explored stroke imaging and an in-depth, comparative eye-movement study between CT and MRI has not yet been conducted. A computer-based, eye-tracking study was designed to assess diagnostic accuracy and interpretation in stroke CT and MR imagery. Forty eight predetermined clinical cases, with five images per case, were presented to participants (novices, trainees and radiologists; n=28). The presence or absence of abnormalities was rated on a four-point Likert scale and their locations reported. Results highlight differences in visual search patterns amongst novice, trainee and expert observers; the most marked differences occurred between novice readers and experts. In terms of modality differences; novice and expert readers spent longer appraising CT images than MR, compared with trainees, who spent longer appraising MR than CT images. Image analysis trends did not appear to differ between modalities, but time spent within clinical images, accuracy and relative confidence performing the task did differ between CT and MR reader groups. To-date few studies have explored observer performance in neuroradiology and the present study examines multi-slice image appraisal by comparing matched pairs of CT and MRI Stroke cases.
- Computer Science