Inter‐laboratory comparison of channelized hotelling observer computation

Ba, Alexandre; Abbey, Craig K.; Baek, Jongduk; Han, Minah; Bouwman, Ramona W.; Balta, Christiana; Brankov, Jovan G.; Massanes, Francesc; Gifford, Howard C.; Hernandez-Giron, Irene; Veldkamp, Wouter J. H.; Petrov, Dimitar; Marshall, Nicholas; Samuelson, F. W.; Zeng, Rongping; Solomon, Justin; Samei, Ehsan; Timberg, Pontus; Förnvik, Hannie; Reiser, Ingrid; Yu, Lifeng; Gong, Hao; Bochud, François

doi:10.1002/mp.12940

Cited by 16 publications

(21 citation statements)

References 38 publications

(66 reference statements)

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“…Low contrast detectability was assessed by CHO model observer computed on an homemade program in python 3.3 and validated with an international comparison, with 10 dense difference of Gaussian (DDoG) channels [27,40]. However, as CHO model observer are more efficient than human observers for simple detection task in uniform background, it is necessary to adjust the detection outcomes of model observers by adding internal noise.…”

Section: Task-based Image Quality Assessment 231 Channelized Hotelling Observermentioning

confidence: 99%

“…covariance matrix. The p factor was calibrated on the data from the inter-comparison study of Ba et al [41,40] and it was equal to 200.…”

Section: Task-based Image Quality Assessment 231 Channelized Hotelling Observermentioning

confidence: 99%

“…Then, to establish reliable confidence interval (CI) estimators of CHO performance and to overcome the bias associated with point estimates, the CHO performance was calculated using the method developed by Wunderlich et al [42] For this study, after evaluating the minimal number of images needed [40], 110 signal-absent images and 110 signal-present images were respectively used to compute 110 decision variables for the signal-absent image category and for the signal-present image category. Finally, the SNR was converted in Area under the ROC curve using the following formula:…”

Section: Task-based Image Quality Assessment 231 Channelized Hotelling Observermentioning

confidence: 99%

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Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

Racine

Brat²,

Dufour³

et al. 2021

European Journal of Radiology

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To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and potential dose reduction. Methods: Anthropomorphic phantoms (mimicking non-overweight and overweight patient), containing lesions of 6 mm in diameter with 20HU contrast, were scanned at five different dose levels (2,6,10,15,20 mGy) on a CT system, using clinical routine protocols for liver lesion detection. Images were reconstructed using ASiR-V 0% (surrogate for FBP), 60 % and TF at low, medium and high strength. Noise texture was characterized by computing a normalized Noise Power Spectrum filtered by an eye filter. The similarity against FBP texture was evaluated using peak frequency difference (PFD) and root mean square deviation (RMSD). Low contrast detectability was assessed using a channelized Hotelling observer and the area under the ROC curve (AUC) was used as figure of merit. Potential dose reduction was calculated to obtain the same AUC for TF and ASiR-V. Results: FBP-like noise texture was more preserved with TF (PFD from -0.043mm-1 to -0.09mm-1, RMSD from 0.12mm-1 to 0.21mm-1) than with ASiR-V (PFD equal to 0.12 mm-1, RMSD equal to 0.53mm-1), resulting in a sharper image. AUC was always higher with TF than ASIR-V. In average, TF compared to ASiR-V, enabled a radiation dose reduction potential of 7%, 25 % and 33 % for low, medium and high strength respectively. Conclusion: Compared to ASIR-V, TF at high strength does not impact noise texture and maintains low contrast liver lesions detectability at significant lower dose.

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Section: Task-based Image Quality Assessment 231 Channelized Hotelling Observermentioning

confidence: 99%

“…covariance matrix. The p factor was calibrated on the data from the inter-comparison study of Ba et al [41,40] and it was equal to 200.…”

Section: Task-based Image Quality Assessment 231 Channelized Hotelling Observermentioning

confidence: 99%

Section: Task-based Image Quality Assessment 231 Channelized Hotelling Observermentioning

confidence: 99%

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Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

Racine

Brat²,

Dufour³

et al. 2021

European Journal of Radiology

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“…As CHO model observers are more efficient than human observers for simple detection tasks in uniform background, it is necessary to adjust the detection outcomes of model observers by adding internal noise on the covariance matrix [ 28 ]. Internal noise was calibrated with the data from the inter-comparison study of Ba et al [ 29 ]. The area under the receiver operating characteristics curve (AUC) was used as the figure of merit to assess the detectability of low contrast lesions.…”

Section: Methodsmentioning

confidence: 99%

Assessment of task-based image quality for abdominal CT protocols linked with national diagnostic reference levels

et al. 2021

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Objectives To assess task-based image quality for two abdominal protocols on various CT scanners. To establish a relationship between diagnostic reference levels (DRLs) and task-based image quality. Methods A protocol for the detection of focal liver lesions was used to scan an anthropomorphic abdominal phantom containing 8- and 5-mm low-contrast (20 HU) spheres at five CTDIvol levels (4, 8, 12, 16, and 20 mGy) on 12 CTs. Another phantom with high-contrast calcium targets (200 HU) was scanned at 2, 4, 6, 10, and 15 mGy using a renal stones protocol on the same CTs. To assess the detectability, a channelized Hotelling observer was used for low-contrast targets and a non-prewhitening observer with an eye filter was used for high contrast targets. The area under the ROC curve and signal to noise ratio were used as figures of merit. Results For the detection of 8-mm spheres, the image quality reached a high level (mean AUC over all CTs higher than 0.95) at 11 mGy. For the detection of 5-mm spheres, the AUC never reached a high level of image quality. Variability between CTs was found, especially at low dose levels. For the search of renal stones, the AUC was nearly maximal even for the lowest dose level. Conclusions Comparable task-based image quality cannot be reached at the same dose level on all CT scanners. This variability implies the need for scanner-specific dose optimization. Key Points • There is an image quality variability for subtle low-contrast lesion detection in the clinically used dose range. • Diagnostic reference levels were linked with task-based image quality metrics. • There is a need for specific dose optimization for each CT scanner and clinical protocol.

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“…The increased reliance on peripheral processing with 3D images might lead to performance differences from those obtained from LKE. Also, current model observers in the literature incorporate properties of the human visual system only at the fovea [11], [12], [33], [37], [38], [71], [72] but do not account for observer's vision at the visual periphery. Thus, existing model observers might be unable to successfully predict search performance in 3D images for signals which detectability varies greatly from the fovea to the visual periphery.…”

Section: Introductionmentioning

confidence: 99%

Foveated Model Observers for Visual Search in 3D Medical Images

Lago

Abbey

Eckstein

2021

IEEE Trans. Med. Imaging

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Model observers have a long history of success in predicting human observer performance in clinically-relevant detection tasks. New 3D image modalities provide more signal information but vastly increase the search space to be scrutinized. Here, we compared standard linear model observers (ideal observers, non-pre-whitening matched filter with eye filter, and various versions of Channelized Hotelling models) to human performance searching in 3D 1/f 2.8 filtered noise images and assessed its relationship to the more traditional location known exactly detection tasks and 2D search. We investigated two different signal types that vary in their detectability away from the point of fixation (visual periphery). We show that the influence of 3D search on human performance interacts with the signal's detectability in the visual periphery. Detection performance for signals difficult to detect in the visual periphery deteriorates greatly in 3D search but not in 3D location known exactly and 2D search. Standard model observers do not predict the interaction between 3D search and signal type. A proposed extension of the Channelized Hotelling model (foveated search model) that processes the image with reduced spatial detail away from the point of fixation, explores the image through eye movements, and scrolls across slices can successfully predict the interaction observed in humans and also the types of errors in 3D search. Together, the findings highlight the need for foveated model observers for image quality evaluation with 3D search.

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Inter‐laboratory comparison of channelized hotelling observer computation

Cited by 16 publications

References 38 publications

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

Assessment of task-based image quality for abdominal CT protocols linked with national diagnostic reference levels

Foveated Model Observers for Visual Search in 3D Medical Images

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