Efficient visual-search model observers for PET

Abstract

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.

Figure 1.

An example of positron emission tomography study images. The top row (a–c) shows a case with a liver tumour in the coronal, sagittal and transverse views. Images in the middle row (d–f) pertain to a lung tumour case, whereas the bottom row (g–i) shows a case with a soft-tissue tumour.

Figure 2.

Performance comparisons of the visual-search observers based on the gradient-ascent and watershed algorithms. Values of area under the localization receiver operating characteristic curve (A_L) are plotted for (a) the background-assumed-homogeneous (BAH)-Ω_R task and (b) the BAH-Ω task. The dotted diagonal line in each plot is included to help judge deviations from equality.

Figure 3.

(a–h) A comparison of human and model observer performances. Each plot compares the nine values of from the human observers to the areas obtained from a model observer with one of the four tasks. The correlation coefficient (r) and root-mean-squared error (ε) are provided for each comparison. BAH, background-assumed-homogeneous; BKE, background-known-exactly; VS, visual search.

Figure 4.

Matching fractions for the human and model observers as a function of prospective threshold radius. Plot (a) is for the abnormal test images, and plot (b) is for the normal test images. F_m between the two human observers is indicated by the solid line in each plot. The other eight curves per plot indicate for the model observers with the background-known-exactly and background-assumed-homogeneous tasks. Based on these plots, the threshold R_m was set to 15 mm.