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Todd Horowitz, Melissa Treviño, Marcin Czarniecki, Ismail B Turkbey, Peter L Choyke; Searching for the gist of the prostate. Journal of Vision 2019;19(10):226a. doi: https://doi.org/10.1167/19.10.226a.
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© ARVO (1962-2015); The Authors (2016-present)
Humans can extract the gist of a visual scene in a fraction of a second, categorizing it as, say, indoor or outdoor, open or closed. Expert radiologists can do this for complex medical images, such as those generated by prostate multiparametric magnetic resonance imaging (mpMRI). MpMRI combines anatomical information from T2-weighted (T2W) sequences, and functional sequences such as conventional diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC). Standard workstation formats present these imaging modalities side-by-side. The goal of this study was to study the nature of mpMRI gist in different modalities. Which modality generates the strongest gist? Are anatomical or functional sequences more useful? Do these modalities provide independent gist information? We tested three groups of five radiologists with prostate mpMRI experience. Each group was shown 100 images from a single modality (T2W, DWI, or ADC). The same cases were used across groups to allow comparison across modalities. Lesions (Gleason scores 6–9) were present in 50% of the images. Images were taken from the base, mid, or apex regions of the prostate. Stimuli were presented for 500 ms, followed by a sector map of the prostate. Participants were first asked to localize the lesion on the sector map (even if they did not see a lesion), then indicate whether or not they thought a lesion was present, and then provided a confidence rating. All three groups detected lesions better than chance [d’ mean(sd): T2W 0.83(0.51); DWI 0.80 (0.29); ADC 1.16(0.31)]. These results suggest that both anatomical and functional information contribute to mpMRI gist. Furthermore, there was little consistency from modality to modality as to which cases produced the best performance, indicating that each modality contributes unique information.
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