143 related articles for article (PubMed ID: 37183490)
1. Dose-efficiency quantification of computed tomography systems using a model-observer.
Göppel M; Anton M; Gala HLH; Giussani A; Trinkl S; Renger B; Brix G
Med Phys; 2023 Dec; 50(12):7594-7605. PubMed ID: 37183490
[TBL] [Abstract][Full Text] [Related]
2. Objective assessment of low contrast detectability in computed tomography with Channelized Hotelling Observer.
Racine D; Ba AH; Ott JG; Bochud FO; Verdun FR
Phys Med; 2016 Jan; 32(1):76-83. PubMed ID: 26515665
[TBL] [Abstract][Full Text] [Related]
3. Accurate and efficient measurement of channelized Hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom.
Fan M; Thayib T; McCollough C; Yu L
Med Phys; 2023 Feb; 50(2):737-749. PubMed ID: 36273393
[TBL] [Abstract][Full Text] [Related]
4. 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-Giron I; Calzado A; Geleijns J; Joemai RM; Veldkamp WJ
Br J Radiol; 2014 Jul; 87(1039):20140014. PubMed ID: 24837275
[TBL] [Abstract][Full Text] [Related]
5. Localization of liver lesions in abdominal CT imaging: II. Mathematical model observer performance correlates with human observer performance for localization of liver lesions in abdominal CT imaging.
Dilger SKN; Leng S; Chen B; Carter RE; Favazza CP; Fletcher JG; McCollough CH; Yu L
Phys Med Biol; 2019 May; 64(10):105012. PubMed ID: 30995626
[TBL] [Abstract][Full Text] [Related]
6. Channelized hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom: Part II. Repeatability study.
Fan M; Zhou Z; McCollough C; Yu L
Med Phys; 2024 Mar; 51(3):1714-1725. PubMed ID: 38305692
[TBL] [Abstract][Full Text] [Related]
7. Effects of various generations of iterative CT reconstruction algorithms on low-contrast detectability as a function of the effective abdominal diameter: A quantitative task-based phantom study.
Viry A; Aberle C; Racine D; Knebel JF; Schindera ST; Schmidt S; Becce F; Verdun FR
Phys Med; 2018 Apr; 48():111-118. PubMed ID: 29728223
[TBL] [Abstract][Full Text] [Related]
8. Reducing CT radiation dose with iterative reconstruction algorithms: the influence of scan and reconstruction parameters on image quality and CTDIvol.
Klink T; Obmann V; Heverhagen J; Stork A; Adam G; Begemann P
Eur J Radiol; 2014 Sep; 83(9):1645-54. PubMed ID: 25037931
[TBL] [Abstract][Full Text] [Related]
9. Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms.
Solomon J; Ba A; Bochud F; Samei E
Med Phys; 2016 Dec; 43(12):6497. PubMed ID: 27908164
[TBL] [Abstract][Full Text] [Related]
10. Automated assessment of low contrast sensitivity for CT systems using a model observer.
Hernandez-Giron I; Geleijns J; Calzado A; Veldkamp WJ
Med Phys; 2011 Jul; 38 Suppl 1():S25. PubMed ID: 21978115
[TBL] [Abstract][Full Text] [Related]
11. Detectability of Small Low-Attenuation Lesions With Deep Learning CT Image Reconstruction: A 24-Reader Phantom Study.
Toia GV; Zamora DA; Singleton M; Liu A; Tan E; Leng S; Shuman WP; Kanal KM; Mileto A
AJR Am J Roentgenol; 2023 Feb; 220(2):283-295. PubMed ID: 36129222
[No Abstract] [Full Text] [Related]
12. Assessing image quality and dose reduction of a new x-ray computed tomography iterative reconstruction algorithm using model observers.
Tseng HW; Fan J; Kupinski MA; Sainath P; Hsieh J
Med Phys; 2014 Jul; 41(7):071910. PubMed ID: 24989388
[TBL] [Abstract][Full Text] [Related]
13. Improving Low-contrast Detectability and Noise Texture Pattern for Computed Tomography Using Iterative Reconstruction Accelerated with Machine Learning Method: A Phantom Study.
Funama Y; Takahashi H; Goto T; Aoki Y; Yoshida R; Kumagai Y; Awai K
Acad Radiol; 2020 Jul; 27(7):929-936. PubMed ID: 31918961
[TBL] [Abstract][Full Text] [Related]
14. Deep Learning Reconstruction at CT: Phantom Study of the Image Characteristics.
Higaki T; Nakamura Y; Zhou J; Yu Z; Nemoto T; Tatsugami F; Awai K
Acad Radiol; 2020 Jan; 27(1):82-87. PubMed ID: 31818389
[TBL] [Abstract][Full Text] [Related]
15. Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: impact of radiation dose and reconstruction algorithms.
Yu L; Leng S; Chen L; Kofler JM; Carter RE; McCollough CH
Med Phys; 2013 Apr; 40(4):041908. PubMed ID: 23556902
[TBL] [Abstract][Full Text] [Related]
16. CT Detectability of Small Low-Contrast Hypoattenuating Focal Lesions: Iterative Reconstructions versus Filtered Back Projection.
Mileto A; Zamora DA; Alessio AM; Pereira C; Liu J; Bhargava P; Carnell J; Cowan SM; Dighe MK; Gunn ML; Kim S; Kolokythas O; Lee JH; Maki JH; Moshiri M; Nasrullah A; O'Malley RB; Schmiedl UP; Soloff EV; Toia GV; Wang CL; Kanal KM
Radiology; 2018 Nov; 289(2):443-454. PubMed ID: 30015591
[TBL] [Abstract][Full Text] [Related]
17. CHARACTERIZATION OF TWO CT SYSTEMS USING A CHANNELIZED HOTELLING OBSERVER AND NPS METRIC.
Ortenzia O; D'Alessio A; Noferini L; Ghetti C
Radiat Prot Dosimetry; 2020 Jul; 189(2):224-233. PubMed ID: 32161966
[TBL] [Abstract][Full Text] [Related]
18. Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.
Greffier J; Si-Mohamed S; Frandon J; Loisy M; de Oliveira F; Beregi JP; Dabli D
Med Phys; 2022 Aug; 49(8):5052-5063. PubMed ID: 35696272
[TBL] [Abstract][Full Text] [Related]
19. Image Quality and Lesion Detectability of Lower-Dose Abdominopelvic CT Obtained Using Deep Learning Image Reconstruction.
Park J; Shin J; Min IK; Bae H; Kim YE; Chung YE
Korean J Radiol; 2022 Apr; 23(4):402-412. PubMed ID: 35289146
[TBL] [Abstract][Full Text] [Related]
20. Performance evaluation of iterative reconstruction algorithms for achieving CT radiation dose reduction - a phantom study.
Dodge CT; Tamm EP; Cody DD; Liu X; Jensen CT; Wei W; Kundra V; Rong XJ
J Appl Clin Med Phys; 2016 Mar; 17(2):511-531. PubMed ID: 27074454
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
