Efficient visual-search model observers for PET.

Abstract

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. 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. 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. Computationally efficient VS can enhance the stability of statistical model observers with regard to uncertainties in PET tumour detection tasks. VS models improve concordance with human observers.