Efficient visual-search model observers for PET.

OBJECTIVE: Scanning model observers have been efficiently applied as a research tool to predict human-observer performance in F-18 positron emission tomography (PET). We investigated whether a visual-search (VS) observer could provide more reliable predictions with comparable efficiency.
METHODS: Simulated two-dimensional images of a digital phantom featuring tumours in the liver, lungs and background soft tissue were prepared in coronal, sagittal and transverse display formats. A localization receiver operating characteristic (LROC) study quantified tumour detectability as a function of organ and format for two human observers, a channelized non-prewhitening (CNPW) scanning observer and two versions of a basic VS observer. The VS observers compared watershed (WS) and gradient-based search processes that identified focal uptake points for subsequent analysis with the CNPW observer. The model observers treated "background-known-exactly" (BKE) and "background-assumed-homogeneous" assumptions, either searching the entire organ of interest (Task A) or a reduced area that helped limit false positives (Task B). Performance was indicated by area under the LROC curve. Concordance in the localizations between observers was also analysed.
RESULTS: With the BKE assumption, both VS observers demonstrated consistent Pearson correlation with humans (Task A: 0.92 and Task B: 0.93) compared with the scanning observer (Task A: 0.77 and Task B: 0.92). The WS VS observer read 624 study test images in 2.0 min. The scanning observer required 0.7 min.
CONCLUSION: Computationally efficient VS can enhance the stability of statistical model observers with regard to uncertainties in PET tumour detection tasks. ADVANCES IN KNOWLEDGE: VS models improve concordance with human observers.