Implementation of a channelized Hotelling observer model to assess image quality of x-ray angiography systems

Abstract

Abstract. Evaluation of flat-panel angiography equipment through conventional image quality metrics is limited by the scope of standard spatial-domain image quality metric(s), such as contrast-to-noise ratio and spatial resolution, or by restricted access to appropriate data to calculate Fourier domain measurements, such as modulation transfer function, noise power spectrum, and detective quantum efficiency. Observer models have been shown capable of overcoming these limitations and are able to comprehensively evaluate medical-imaging systems. We present a spatial domain-based channelized Hotelling observer model to calculate the detectability index (DI) of our different sized disks and compare the performance of different imaging conditions and angiography systems. When appropriate, changes in DIs were compared to expectations based on the classical Rose model of signal detection to assess linearity of the model with quantum signal-to-noise ratio (SNR) theory. For these experiments, the estimated uncertainty of the DIs was less than 3%, allowing for precise comparison of imaging systems or conditions. For most experimental variables, DI changes were linear with expectations based on quantum SNR theory. DIs calculated for the smallest objects demonstrated nonlinearity with quantum SNR theory due to system blur. Two angiography systems with different detector element sizes were shown to perform similarly across the majority of the detection tasks.

Performance assessment of active vs passive pixel x‐ray angiography detector systems using a bias‐corrected channelized Hotelling observer and adult patient‐equivalent experimental conditions

“…In large part due to the versatility of the Gabor channel CHO as demonstrated in the literature, we previously implemented the 96 Gabor channel CHO used herein to assess technical performance of x‐ray angiography system performance . Under quantum noise‐limited conditions, the model was found to be linear with changes in quantum x‐ray fluence, linear with changes in test object diameter as predicted by the Rose model, and sensitive to the difference in penumbral blur between small and large focal spots during x‐ray angiography magnification imaging.…”

“…Model observer theory and experimental methods were similar to those previously described . Analytical methods were executed using MATLAB ® (Mathworks, Natick, MA).…”

“…The CHO method simultaneously considers test object properties and system properties including spatial resolution and noise, and estimates detectability index (d′) of the test object as a fundamental metric. Previous work described implementation of a CHO to assess x‐ray angiography system performance . Also, theory and methods to estimate and correct CHO bias due to temporally variable detector electronic noise were developed .…”

Determination of optimal image type and lowest detectable concentration for iodine detection on a photon counting detector-based multi-energy CT system

“…Channelized Hotelling observer (CHO) was used to predict and compare the performance of iodine detection in each of the image types: TL, VMI, and iodine map images [10–12]. For each image type, the test variable λ is given by: $$\mathrm{\lambda }={\omega }_{\mathrm{italicCHO}}^t{g}_C$$ where g c is the channel output of the test image and ω CHO is the CHO template defined as: $${\omega }_{\mathrm{italicCHO}}=S_C^{-1}[\overline{g_{sc}}-\overline{g_{bc}}]$$…”

Bayesian framework inspired no-reference region-of-interest quality measure for brain MRI images

“…The efficacy of task-based quality assessment reported in [27], [25], [28] is the motivation behind pioneering research in the field of task-based adaptive imaging. Task-based adaptive imaging has been applied for the optimization of imaging systems parameters [29], [8], [5], optimization of image quality in imaging systems [26], [30], [31], evaluation of cardiac ejection fraction estimation algorithms [32] and the evaluation of diffusion weighted MRI segmentation algorithms [33].…”