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 "backgroundknown-exactly" (BKE) and "background-assumedhomogeneous" 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. The prospect of better clinical outcomes is a major impetus for research in medical imaging. This motivation is formalized in the practice of task-based assessments, whereby image quality is defined by how well observers can perform a specified task with an appropriate set of test images.1 An observer could be a human or a mathematical algorithm, undertaking diagnostic tasks such as parameter estimation or tumour detection. With tumour detection, diagnostic accuracy as measured in observer studies provides a basic measure of imaging system performance.2 It has been suggested that consistent use of observer studies for evaluation and optimization in the early stages of technology development could improve both the focus of imaging research and the efficiency of later-stage studies. High levels of observer variance can make human-observer studies impractical for extensive assessments. A standard alternative is to employ a mathematical model observer that mimics humans for the bulk of the observing work, augmenting these data with occasional human-observer data for validation purposes. Many of these model observers in regular use trace their derivation to ideal observers from signal detection theory. 4 An ideal observer establishes the upper bound on diagnostic accuracy for a given task when performance of the task is limited by stochastic processes such as quantum and anatomical noise. Models of human observers generally build on an ideal observer by incorporating additional sources of noise or other inefficiencies such as limited visual-response characteristics.Among the widely used model examples are linear, Hotelling-type observers for two-hypothesis (binary) tasks. These observers are constructed by treating the image variations as a multivariate Gaussian process, with the relevant stoch...

Visual‐search observers for assessing tomographic x‐ray image quality

“…Overall, the VS paradigm offers considerable advantages for analyzing detection-localization tasks, with several studies having shown improved stability compared to scanning models in situations where organ-boundary artifacts interfered with detection. 37 There were strong limited-angle artifacts at the borders of the fibroglandular compartment in some of the test images, and while some effort was made to control their impact on the feature extraction, this was not completely successful.…”

Task Equivalence for Model and Human-Observer Comparisons in SPECT Localization Studies

“…Localization concordance is a more-stringent measure of agreement utilized in previous studies [26]. On the whole, it is reasonable to expect that the VS observer should improve concordance with humans when compared with scanning observers, as the initial hot-blob search is predicated on a basic tenet that humans understand about lesion detection for much of nuclear medicine.…”

Accounting for anatomical noise in search-capable model observers for planar nuclear imaging

“…A less-efficient gradient-ascent segmentation method yielded similar results in previous VS observer studies. 4 With the watershed algorithm, a 2-D test image is viewed as a topographic landscape with holes at the local minima. 13 When the landscape is lowered into a tank of water, water starts to fill the "catchment basins" around the holes.…”

“…3,4 These models perform distinct target search and analysis procedures, thereby simulating the visual-search (VS) process that trained radiologists engage in to interpret medical images. 5 Our VS model observer applies a feature-guided search that identifies target-like structure for subsequent analysis.…”