Evaluation of low contrast detectability performance using two-alternative forced choice method on computed tomography dose reduction algorithms

Fan, Jiahua; Madhav, Priti; Sainath, Paavana; Cao, Ximiao; Wu, Haifeng; Nilsen, Roy; Budde, A.; Yadava, G; Thibault, Jean‐Baptiste; Hsieh, Jiang

doi:10.1117/12.910754

Cited by 2 publications

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“…Other studies opted for a different strategy to perform 2-AFC human observer experiments based on the entire Catphan image and covering the objects that were not being scored by crops taken from the nearby background regions in the image. 30 The performed human observer study has some limitations. The first one is the reduced number of observers (only four).…”

Section: Discussionmentioning

confidence: 99%

Comparison between human and model observer performance in low-contrast detection tasks in CT images: application to images reconstructed with filtered back projection and iterative algorithms

Hernandez-Giron¹,

Calzado²,

Geleijns³

et al. 2014

BJR

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RMS, Veldkamp WJH. Comparison between human and model observer performance in lowcontrast detection tasks in CT images: application to images reconstructed with filtered back projection and iterative algorithms. Br J Radiol 2014;87:20140014. FULL PAPERComparison between human and model observer performance in low-contrast detection tasks in CT images: application to images reconstructed with filtered back projection and iterative algorithms Objective: To compare low-contrast detectability (LCDet) performance between a model [non-pre-whitening matched filter with an eye filter (NPWE)] and human observers in CT images reconstructed with filtered back projection (FBP) and iterative [adaptive iterative dose reduction three-dimensional (AIDR 3D; Toshiba Medical Systems, Zoetermeer, Netherlands)] algorithms. Methods: Images of the Catphan® phantom (Phantom Laboratories, New York, NY) were acquired with Aquilion ONE™ 320-detector row CT (Toshiba Medical Systems, Tokyo, Japan) at five tube current levels (20-500 mA range) and reconstructed with FBP and AIDR 3D. Samples containing either low-contrast objects (diameters, 2-15 mm) or background were extracted and analysed by the NPWE model and four human observers in a two-alternative forced choice detection task study. Proportion correct (PC) values were obtained for each analysed object and used to compare human and model observer performances. An efficiency factor (h) was calculated to normalize NPWE to human results. Results: Human and NPWE model PC values (normalized by the efficiency, h 5 0.44) were highly correlated for the whole dose range. The Pearson's product-moment correlation coefficients (95% confidence interval) between human and NPWE were 0.984 (0.972-0.991) for AIDR 3D and 0.984 (0.971-0.991) for FBP, respectively. Bland-Altman plots based on PC results showed excellent agreement between human and NPWE [mean absolute difference 0.5 6 0.4%; range of differences (24.7%, 5.6%)]. Conclusion: The NPWE model observer can predict human performance in LCDet tasks in phantom CT images reconstructed with FBP and AIDR 3D algorithms at different dose levels. Advances in knowledge: Quantitative assessment of LCDet in CT can accurately be performed using software based on a model observer.

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Section: Discussionmentioning

confidence: 99%

Comparison between human and model observer performance in low-contrast detection tasks in CT images: application to images reconstructed with filtered back projection and iterative algorithms

Hernandez-Giron¹,

Calzado²,

Geleijns³

et al. 2014

BJR

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“…In recent years, the channelized Hotelling model observer (CHO) has been increasingly used [1][2][3][4] and during the last decade, numerous studies have documented a good correlation with human observers in CT dose optimization or assessment of iterative reconstruction algorithms. [5][6][7][8][9][10] Many studies for low-contrast detection with model observers use images with a uniform background from homogeneous phantoms. 7,11 However, anatomical structures and inhomogeneities in organs generate noise that can affect the detection performance of human observers.…”

Section: Introductionmentioning

confidence: 99%

Channelized Hotelling observer correlation with human observers for low-contrast detection in liver CT images

Abbey

Racine

et al. 2019

J. Med. Imag.

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Task-based image quality procedures in CT that substitute a human observer with a model observer usually use single-slice images with uniform backgrounds from homogeneous phantoms. However, anatomical structures and inhomogeneities in organs generate noise that can affect the detection performance of human observers. The purpose of this work was to assess the impact of background type, uniform or liver, and the viewing modality, single-or multislice, on the detection performance of human and model observers. We collected abdominal CT scans from patients and homogeneous phantom scans in which we digitally inserted low-contrast signals that mimicked a liver lesion. We ran a rating experiment with the two background conditions with three signal sizes and three human observers presenting images in two reading modalities: single-and multislice. In addition, channelized Hotelling observers (CHO) for single-and multislice detection were implemented and evaluated according to their degree of correlation with the human observer performance. For human observers, there was a small but significant improvement in performance with multislice compared to the singleslice viewing mode. Our data did not reveal a significant difference between uniform and anatomical backgrounds. Model observers demonstrated a good correlation with human observers for both viewing modalities. Human observers have very similar performances in both multi-and single-slice viewing mode. It is therefore preferable to use single-slice CHO as this model is computationally more tractable than multislice CHO. However, using images from a homogeneous phantom can result in overestimating image quality as CHO performance tends to be higher in uniform than anatomical backgrounds, while human observers have similar detection performances.

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Evaluation of low contrast detectability performance using two-alternative forced choice method on computed tomography dose reduction algorithms

Cited by 2 publications

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Comparison between human and model observer performance in low-contrast detection tasks in CT images: application to images reconstructed with filtered back projection and iterative algorithms

Comparison between human and model observer performance in low-contrast detection tasks in CT images: application to images reconstructed with filtered back projection and iterative algorithms

Channelized Hotelling observer correlation with human observers for low-contrast detection in liver CT images

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