Njølstad T, Jensen K, Dybwad A, Salvesen Ø, Andersen HK, Schulz A. Low-contrast detectability and potential for radiation dose reduction using deep learning image reconstruction-a 20-reader study on a semi-anthropomorphic liver phantom. Eur J Radiol Open. 2022;9:100418. https://doi.org/10.1016/j.ejro.2022.100418.

Article  Google Scholar 

Choi JY, Lee JM, Sirlin CB. CT and MR imaging diagnosis and staging of hepatocellular carcinoma: Part I. Development, growth, and spread: key pathologic and imaging aspects. Radiology. 2014;272(3):635–54. https://doi.org/10.1148/radiol.14132361.

Article  Google Scholar 

Anam C, Amilia R, Naufal A, Fujibuchi T, Dougherty G. A statistical-based automatic detection of a low-contrast object in the ACR CT phantom for measuring contrast-to-noise ratio of CT images. Biomed Phys Eng Express. 2025;11:017001. https://doi.org/10.1088/2057-1976/ad90e9.

Article  Google Scholar 

Dolly S, Chen HC, Anastasio M, Mutic S, Li H. Practical considerations for noise power spectra estimation for clinical CT scanners. J Appl Clin Med Phys. 2016;17(3):392–407. https://doi.org/10.1120/jacmp.v17i3.5841.

Article  Google Scholar 

Anam C, Naufal A, Budi WS, Sutanto H, Haryanto F, Dougherty G. IndoQCT: a platform for automated CT image quality assessment. Med Phys Int J. 2023;11(2):328–36.

Google Scholar 

Miéville FA, Gudinchet F, Brunelle F, Bochud FO, Verdun FR. Iterative reconstruction methods in two different MDCT scanners: physical metrics and 4-alternative forced-choice detectability experiments--a phantom approach. Phys Med. 2013;29(1):99–110. https://doi.org/10.1016/j.ejmp.2011.12.004.

Article  Google Scholar 

Ilham IR, Anam C, Sutanto H, Naufal A, Amilia R. Impact of radiation dose and iterative reconstruction (IR) level on low-contrast detectability with 4-AFC approach. Int J Sci Res Sci Technol. 2024;11(6):272–8. https://doi.org/10.32628/IJSRST24116181.

Article  Google Scholar 

Ott JG, Ba A, Racine D, Viry A, Bochud FO, Verdun FR. Assessment of low contrast detection in CT using model observers: developing a clinically-relevant tool for characterising adaptive statistical and model-based iterative reconstruction. Z Med Phys. 2017;27(2):86–97. https://doi.org/10.1016/j.zemedi.2016.04.002.

Article  Google Scholar 

Solomon J, Samei E. Correlation between human detection accuracy and observer model-based image quality metrics in computed tomography. J Med Imaging. 2016;3(3):035506. https://doi.org/10.1117/1.JMI.3.3.035506.

Article  Google Scholar 

Rotzinger DC, Racine D, Beigelman-Aubry C, Alfudhili KM, Keller N, Monnin P, et al. Task-based model observer assessment of a partial model-based iterative reconstruction algorithm in thoracic oncologic multidetector CT. Sci Rep. 2018;8(1):17734. https://doi.org/10.1038/s41598-018-36045-4.

Article  Google Scholar 

Dedovic E, Gazibegovic-Busuladzic A, Busuladzic M. How differently generated clinical tasks affect the observer performances in CT images analysis. IFMBE Proc. 2021;84. https://doi.org/10.1007/978-3-030-73909-6_91.

Othman N, Simon AC, Montagu T, Berteloot L, Grévent D, Habib Geryes B, et al. Toward a comparison and an optimization of CT protocols using new metrics of dose and image quality part I: prediction of human observers using a model observer for detection and discrimination tasks in low-dose CT images in various scanning conditions. Phys Med Biol. 2021;66(11):115003. https://doi.org/10.1088/1361-6560/abfad8.

Article  Google Scholar 

Hernandez-Giron I, Calzado A, Geleijns J, Joemai RMS, Veldkamp WJ. 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. Br J Radiol. 2015;87(1039):20140014. https://doi.org/10.1259/bjr.20140014.

Article  Google Scholar 

Liu Z, Wolfe S, Yu Z, Laforest R, Mhlanga JC, Fraum TJ, et al. Observer-study-based approaches to quantitatively evaluate the realism of synthetic medical images. Phys Med Biol. 2023;68:074001. https://doi.org/10.1088/1361-6560/acc0ce.

Article  Google Scholar 

Bellmann Q, Peng Y, Genske U, Yan L, Wagner M, Jahnke P. Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms. Eur Radiol Exp. 2024;8(1):84. https://doi.org/10.1186/s41747-024-00486-6.

Article  Google Scholar 

Macmillan NA, Creelman CD. Detection theory: a user’s guide. 2nd ed. Mahwah, NJ: Lawrence Erlbaum Associates; 2005.

Google Scholar 

Rajagopal JR, Symons R, Pourmorteza A, Ehsan S, Kappler S, Ulzheimer S, et al. A clinically driven task-based comparison of photon counting and conventional energy integrating CT for soft tissue, vascular, and high-resolution tasks. IEEE Trans Radiat Plasma Med Sci. 2021;5(4):588–95. https://doi.org/10.1109/TRPMS.2020.3019954.

Article  Google Scholar 

Dabli D, Beregi JP, Durand Q, Greffier J, Frandon J, de Oliveira F, et al. Impact of the automatic tube current modulation system on virtual monoenergetic image quality for dual-source CT: a phantom study. Phys Med. 2023;109:102574. https://doi.org/10.1016/j.ejmp.2023.102574.

Article  Google Scholar 

Anam C, Amilia R, Naufal A, Hidayanto E, Sutanto H, Lubis LE, et al. Automated task-transfer function measurement for CT image quality assessment based on AAPM TG 233. J Imaging. 2025;11:277. https://doi.org/10.3390/jimaging11030277.

Article  Google Scholar 

Samei E, Bakalyar D, Boedeker KL, Brady S, Fan J, Leng S, et al. Performance evaluation of computed tomography systems: summary of AAPM Task Group 233. Med Phys. 2019;46(11):e735–56. https://doi.org/10.1002/mp.13763.

Article  Google Scholar 

Thor D, Titternes R, Poludniowski G. Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual-energy CT of the abdomen. Med Phys. 2023;50(5):2775–86. https://doi.org/10.1002/mp.16300.

Article  Google Scholar 

Greffier J, Barbotteau Y, Gardavaud F. iQMetrix-CT: new software for task-based image quality assessment of phantom CT images. Diagn Interv Imaging. 2022;103:555–62. https://doi.org/10.1016/j.diii.2022.05.007.

Article  Google Scholar 

Toia GV, Zamora DA, Singleton M, Liu A, Tan E, Leng S, et al. Detectability of small low-attenuation lesions with deep learning CT image reconstruction: a 24-reader phantom study. AJR Am J Roentgenol. 2023;220(2):277–86. https://doi.org/10.2214/AJR.22.28407.

Article  Google Scholar 

Saunders JRS, Samei E. Resolution and noise measurements of five CRT and LCD medical displays. Med Phys. 2006;33:308–19. https://doi.org/10.1118/1.2150777.

Article  Google Scholar 

Solomon J, Samei E. What observer models best reflect low-contrast detectability in CT? Proc SPIE Med Imaging. 2015;9416:94160I. https://doi.org/10.1117/12.2081655.

Article  Google Scholar 

García-Pérez MA, Alcalá-Quintana R. Interval bias in 2AFC detection tasks: sorting out the artifacts. Atten Percept Psychophys. 2011;73:2332–52. https://doi.org/10.3758/s13414-011-0167-x.

Article  Google Scholar 

Verdun FR, Racine D, Ott JG, Tapiovaara MJ, Toroi P, Bochud FO, et al. Image quality in CT: from physical measurements to model observers. Phys Med. 2015;31(8):823–43. https://doi.org/10.1016/j.ejmp.2015.08.007.

Article  Google Scholar 

Evans JD. Straightforward statistics for the behavioral sciences. Pacific Grove, CA: Brooks/Cole Publishing; 1996.

Google Scholar 

Anam C, Naufal A, Sutanto H, Adi K, Yeong CH, Dougherty G. Automated computation of detectability index and generation of contrast–detail curves for CT protocol optimization. Phys Med Biol. 2025;70:19NT03. https://doi.org/10.1088/1361-6560/ae0ab0.

Article  Google Scholar 

Burgess AE. Statistically defined backgrounds: performance of a modified nonprewhitening matched filter model. J Opt Soc Am. 1994;11(4):1237–42. https://doi.org/10.1364/JOSAA.11.001237.

Article  Google Scholar 

Hernandez-Giron I, Geleijns J, Calzado A, Veldkamp WJH. Automated assessment of low contrast sensitivity for CT systems using a model observer. Med Phys. 2011;38:S25. https://doi.org/10.1118/1.3577757.

Article  Google Scholar 

Balta C, Bouwman RW, Sechopoulos I, Broeders MJM, Karssemeijer N, van Engen RE, et al. A model observer study using acquired mammographic images of an anthropomorphic breast phantom. Med Phys. 2018;45(2):655–65. https://doi.org/10.1002/mp.12703.

Article  Google Scholar 

Bouwman RW, Goffi M, van Engen RE, Broeders MJM, Dance DR, Young KC, et al. Can the channelized Hotelling observer including aspects of the human visual system predict human observer performance in mammography? Phys Med. 2017;33:95–105. https://doi.org/10.1016/j.ejmp.2016.12.015.

Article  Google Scholar