(Thursday, 7/18/2019) 1:00 PM - 3:00 PM
Room: 302
Purpose: Channelized Hotelling observer (CHO) models have been previously implemented for x-ray angiography; however, they have only accounted for static test objects and uniform background. The purpose of this work was to develop theory and methods to apply statistical performance tests to x-ray angiography systems with spatial and temporal non-stationarity, including anatomical background and moving test objects.
Methods: Unprocessed images of static and moving iodine-contrast test objects with both uniform and anatomical (anthropomorphic phantom) backgrounds were acquired with a commercial x-ray angiography system. Uniform background images were used to determine task-specific efficient sets of CHO Gabor channels via a novel backward elimination method and to compare the performance of the efficient CHO to that of a quasi-ideal Hotelling observer (HO). The efficient CHO was then adapted to perform a single-sample multivariate statistical test to enable estimation of d’ for moving test objects under anatomical background. Estimation and correction of bias due to finite sampling and system non-stationarity was performed for all experimental conditions.
Results: Channel reduction between 60-80% of the initial channel set (96 channels) was achieved. The efficient CHO related linearly with the quasi-ideal HO with an efficiency >90% across experimental conditions. d’ of the single-sample CHO applied to moving test objects and uniform background was equivalent to the standard CHO, albeit with less precision. Under anatomical background, d’ of the single-sample CHO highly reflected local attenuation properties and related linearly with dose per frame as expected from quantum SNR theory. The proposed d’ bias correction strategy effectively mitigated the impact of bias from non-stationary noise on estimates of d’, particularly for small objects and low doses.
Conclusion: This work demonstrates the theory and methods of the statistical CHO can be adapted to accommodate imaging systems with spatial and temporal non-stationarity. Bias management was critical to maintain model accuracy.
Keywords
Angiography, Observer Performance
Taxonomy
IM- X-ray: Fluoroscopy, digital angiography, and DSA
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