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A visual‐search model observer for multislice‐multiview SPECT images
Author(s) -
Gifford Howard C.
Publication year - 2013
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4818824
Subject(s) - observer (physics) , single photon emission computed tomography , artificial intelligence , computer vision , iterative reconstruction , image quality , computer science , sagittal plane , imaging phantom , correction for attenuation , image resolution , medical imaging , attenuation , mathematics , nuclear medicine , optics , image (mathematics) , physics , medicine , radiology , quantum mechanics
Purpose: Mathematical model observers are intended for efficient assessment of diagnostic image quality, but model‐observer studies often are not representative of clinical realities. Model observers based on a visual‐search (VS) paradigm may allow for greater clinical relevance. The author has compared the performances of several VS model observers with those of human observers and an existing scanning model observer for a study involving nodule detection and localization in simulated Tc‐99m single‐photon emission computed tomography (SPECT) lung volumes.Methods: A localization receiver operating characteristic (LROC) study compared two iterative SPECT reconstruction strategies: an all‐corrections (AllC) strategy with compensations for attenuation, scatter, and distance‐dependent camera resolution and an “RC” strategy with resolution compensation only. Nodules in the simulation phantom were of three different relative contrasts. Observers in the study had access to the coronal, sagittal, and transverse displays of the reconstructed volumes. Three human observers each read 50 training volumes and 100 test volumes per reconstruction strategy. The same images were analyzed by a channelized nonprewhitening (CNPW) scanning observer and two VS observers. The VS observers implemented holistic search processes that identified focal points of Tc‐99m uptake for subsequent analysis by the CNPW scanning model. The level of prior knowledge about the background structure in the images was a study variable for the model observers. Performance was scored by area under the LROC curve.Results: The average human‐observer performances were respectively 0.67 ± 0.04 and 0.61 ± 0.03 for the RC and AllC strategies. Given approximate knowledge about the background structure, both VS models scored 0.69 ± 0.08 (RC) and 0.66 ± 0.08 (AllC). The scanning observer reversed the strategy ranking in scoring 0.73 ± 0.08 with the AllC strategy and 0.64 ± 0.08 with the RC strategy. The VS observers exhibited less sensitivity to variations in background knowledge compared to the scanning observer.Conclusions: The VS framework has the potential to increase the clinical similitude of model‐observer studies and to enhance the ability of existing model observers to quantitatively predict human‐observer performance.