Task-specific efficient channel selection and bias management for Gabor function channelized Hotelling observer model for the assessment of x-ray angiography system performance

Purpose: Channelized Hotelling observer (CHO) models have been implemented to assess imaging performance in x-ray angiography systems. While current methods are appropriate for assessing unprocessed images of moving test objects upon uniform-exposure backgrounds, they are inadequate for assessing conditions which more appropriately mimic clinical imaging conditions including the combination of moving test objects, complex anthropomorphic backgrounds, and image processing. In support of this broad goal, the purpose of this work was to develop theory and methods to automatically select a subset of task-specific efficient Gabor channels from a task-generic Gabor channel base set. Also, previously described theory and methods to manage detectability index (d′) bias due to nonrandom temporal variations in image electronic noise will be revisited herein. Methods: Starting with a base set of 96 Gabor channels, backward elimination of channels was used to automatically identify an “efficient” channel subset which reduced the number of channels retained in the subset while maintaining the magnitude of the d′ estimate. The concept of a pixelwise Hotelling observer (PHO) model was introduced and similarly implemented to assess the performance of the efficient-channel CHO model. Bias in d′ estimates arising from temporally variable nonstationary noise was modeled as a bivariate probability density function for normal distributions, where one variable corresponds to the signal from the test object and the other variable corresponds to the signal from temporally variable nonstationary noise. Theory and methods were tested on uniform-exposure unprocessed angiography images with detector target dose (DTD) of 6, 18, and 120 nGy containing static disk-shaped test objects with diameter in the range of 0.5 to 4 mm. Results: Considering all DTD levels and test object sizes, the proposed method reduced the number of Gabor channels in the final subset by 63–82% compared to the original 96 Gabor channel base set, while maintaining a mean relative performance ((Formula presented.)) of 95% (Formula presented.) 4% with respect to the reference PHO model. Experimental results demonstrated that the bivariate approach to account for bias due to temporally variable nonstationary noise resulted in improved correlation between the CHO and PHO models as compared to a previously proposed univariate approach. Conclusions: Computationally efficient backward elimination can be used to select an efficient subset of Gabor channels from an initial channel base set without substantially compromising the magnitude of the d′ estimate. Bias due to temporally variable nonstationary noise can be modeled through a bivariate approach leading to an improved unbiased estimate of d′.