Direct estimation and correction of bias from temporally variable non-stationary noise in a channelized Hotelling model observer

Fetterly, Kenneth A.; Favazza, Christopher P.

doi:10.1088/0031-9155/61/15/5606

Cited by 7 publications

(63 citation statements)

References 10 publications

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“…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. Next, we found that temporally variable nonstationary noise (detector electronic noise) resulted in positive bias to d′ estimates for low DTD and proposed theory to and methods to correct it . That same 96‐channel, bias‐corrected CHO model was used for this work.…”

Section: Discussionmentioning

confidence: 97%

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

Section: Methodsmentioning

confidence: 99%

“…Previous work demonstrated that d′ estimates were also biased by temporally variable nonstationary noise, or electronic noise which is different between the signal present and signal absent image samples. Theory and methods to estimate and correct for this bias were previously described and will be used for this work . In brief, electronic noise is not random but rather is correlated between image frames .…”

Section: Methodsmentioning

confidence: 99%

“…For all real test objects, the contribution of electronic noise bias to d′ was directly estimated from an otherwise blank portion of the sample images and subtracted from the composite test object and electronic noise d′(n) estimates prior to performing the linear fit of d′(n) vs 1/n. In the previous work, d′ ∞,β was used to distinguish detectability index estimates had an infinite number of samples been used (∞) and corrected for bias from temporally variable electronic noise (β) . In this work, that same estimate d′ ∞,β will be referred to simply as d′.…”

Section: Methodsmentioning

confidence: 99%

“…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 . For this work, quantitative assessment of two models of PP detector systems (total 16 units) and the AP detector systems (2 units) was performed using the bias‐corrected CHO.…”

Section: Introductionmentioning

confidence: 99%

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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

Fetterly

2018

Medical Physics

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Purpose Electronic noise associated with passive pixel (PP) x‐ray angiography flat panel detectors is known to compromise fluoroscopic image quality. An active pixel (AP) crystalline silicon x‐ray detector with potential for reduced influence of electronic noise is commercially available. The purpose of this work was to compare the performance of the AP vs PP x‐ray angiography detectors over a detector target dose (DTD) range relevant for invasive cardiology procedures. Methods A total of 16 passive pixel detector systems representing two models and two active pixel detector systems of a single model were tested. Iodine contrast (160 mg I ml−1) disk‐shaped test objects of diameter 0.5–4.0 mm were embedded in 30 × 30 cm2 25‐cm‐thick PMMA phantom. Detector target dose was 6, 18, and 120 nGy and 1204 test signal present and signal absent images were acquired. A channelized Hotelling observer statistical model (CHO) was used to estimate detectability index (d′) of the detectors for the various test objects. The CHO included correction for bias from finite sampling and that due to temporally variable electronic noise. Results Detectability index estimates demonstrated similar performance between the two models of PP detectors and relatively improved performance for the AP detectors for all DTD levels and test object diameters. For DTD = 120 nGy and the 4.0 mm test object, d′ of the AP detectors was 13% and 20% greater than that of the PP detectors. For DTD = 6 nGy, d′ of the AP detectors was 42% and 54% greater. Conclusions The AP x‐ray angiography detector demonstrated superior performance throughout the DTD range tested and especially for DTD consistent with low‐dose fluoroscopy. The improved performance of the AP detectors may facilitate reduced patient dose and/or improved image quality.

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Section: Discussionmentioning

confidence: 97%

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

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

Fetterly

2018

Medical Physics

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

et al. 2021

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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 0 ) 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 0 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 0 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 (ðd 0 CHO =d 0 PHO Þ Â 100%) of 95% AE 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 0 estimate. Bias due to temporally variable nonstationary noise can be modeled through a bivariate approach leading to an improved unbiased estimate of d 0 .

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Adaptation of a channelized Hotelling observer model to accommodate anthropomorphic backgrounds and moving test objects in X‐ray angiography

Gomez‐Cardona,

Favazza,

Leng

et al. 2023

Medical Physics

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BackgroundPrior implementations of the channelized Hotelling observer (CHO) model have succeeded in assessing the performance of X‐ray angiography systems under a variety of imaging conditions. However, often times these conditions do not resemble those present in routine clinical imaging scenarios, such as having complex anthropomorphic backgrounds in conjunction with moving test objects.PurposeThis work builds up on prior established CHO methods and introduces a new approach to switch from the already established “multiple‐sample” CHO implementation to a “single‐sample” technique. The proposed implementation enables the inclusion of moving test objects upon nonuniform backgrounds by allowing only a single sample to represent the test object present condition that is to be used within the statistical test to estimate the detectability index.MethodsTo assess the proposed method, two image data sets were acquired with a clinical X‐ray angiography system. The first set consisted of a uniform background in combination with static test objects while the second consisted of an anthropomorphic chest phantom in conjunction with moving test objects. The first set was used to validate the proposed approach against the multiple‐sample method while the second was used to assess the feasibility of the proposed method under a variety of imaging conditions, including seven object sizes and seven detector target dose (DTD) levels.ResultsFor the uniform background data set, considering all detectability indices greater or equal than 1, the ratio between the detectability indices of the proposed single‐sample approach versus the multiple‐sample method was 0.997 ± 0.056 (range 0.884–1.159). The average single‐direction width of the 95% confidence intervals (CIs) of the detectability index estimates for the multiple‐sample method was 0.38 ± 0.43 (range 0.03–2.20). For the single‐sample approach, the average width was 2.52 ± 0.63 (range 1.11–5.44). For the anthropomorphic background image set, the results were consistent with classical quantum‐limited signal‐to‐noise ratio (SNR) theory. The magnitude of the detectability indices varied predictably with changes in both object size and DTD, with the highest value associated with the highest dose and the largest object size. Additionally, the proposed method was able to capture differences in the imaging performance for a given test object across the field of view, which was associated with the attenuation levels provided by different features of the anthropomorphic background.ConclusionsA new single‐sample variant of the CHO model to assess the performance of X‐ray angiography imaging systems is proposed. The new approach is consistent with quantum‐limited image quality theory and with a standard implementation of the CHO model. The proposed method enables the assessment of moving test objects in combination with complex, nonuniform image backgrounds, thereby opening the possibility to assess imaging conditions which more closely resemble those used in clinical care.

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Direct estimation and correction of bias from temporally variable non-stationary noise in a channelized Hotelling model observer

Cited by 7 publications

References 10 publications

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

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

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

Adaptation of a channelized Hotelling observer model to accommodate anthropomorphic backgrounds and moving test objects in X‐ray angiography

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