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This paper presents a novel study investigating the influences of Magnetic Resonance (MR) image anatomical
information and observer expertise on an abnormality detection task.
MRI is exquisitely sensitive for detecting brain abnormalities, particularly in the evaluation of white matter diseases, e.g.
multiple sclerosis (MS). For this reason, MS lesions are simulated as the target stimuli for detection in the present study.
Two different image backgrounds are used in the following experiments: a) homogeneous region of white matter tissue,
and b) one slice of a healthy brain MR image. One expert radiologist (more than 10 years' experience), three radiologists
(less than 5 years' experience) and eight naïve observers (without any prior medical knowledge) have performed these
experiments, during which they have been asked different questions dependent upon level of experience; the three
radiologists and eight naïve observers were asked if they were aware of any hyper-signal, likely to represent an MS
lesion, while the most experienced consultant was asked if a clinically significant sign was present. With the percentages
of response "yes" displayed on the y-axis and the lesion intensity contrasts on the x-axis, psychometric function is
generated from the observer' responses.
Results of psychometric functions and calculated thresholds indicate that radiologists have better hyper-signal detection
ability than naïve observers, which is intuitively shown by the lower simple visibility thresholds of radiologists.
However, when radiologists perform a task with clinical implications, e.g. to detect a clinically significant sign, their
detection thresholds are elevated. Moreover, the study indicates that for the radiologists, the simple visibility thresholds
remain the same with and without the anatomical information, which reduces the threshold for the clinically significant
sign detection task.
Findings provide further insight into human visual system processing for this specific task, and this study provides the
foundation for a series of studies investigating numerical observer modeling to be designed, with the ultimate aim of
investigating the medical image quality assessment approach by addressing the perspective of radiologist diagnostic
performance.
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