119 related articles for article (PubMed ID: 38593627)
1. Absolute ground truth-based validation of computer-aided nodule detection and volumetry in low-dose CT imaging.
D'hondt L; Kellens PJ; Torfs K; Bosmans H; Bacher K; Snoeckx A
Phys Med; 2024 May; 121():103344. PubMed ID: 38593627
[TBL] [Abstract][Full Text] [Related]
2. Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.
D'hondt L; Franck C; Kellens PJ; Zanca F; Buytaert D; Van Hoyweghen A; Addouli HE; Carpentier K; Niekel M; Spinhoven M; Bacher K; Snoeckx A
Cancer Imaging; 2024 May; 24(1):60. PubMed ID: 38720391
[TBL] [Abstract][Full Text] [Related]
3. Comparative evaluation of newly developed model-based and commercially available hybrid-type iterative reconstruction methods and filter back projection method in terms of accuracy of computer-aided volumetry (CADv) for low-dose CT protocols in phantom study.
Ohno Y; Yaguchi A; Okazaki T; Aoyagi K; Yamagata H; Sugihara N; Koyama H; Yoshikawa T; Sugimura K
Eur J Radiol; 2016 Aug; 85(8):1375-82. PubMed ID: 27423675
[TBL] [Abstract][Full Text] [Related]
4. Accuracy of two deep learning-based reconstruction methods compared with an adaptive statistical iterative reconstruction method for solid and ground-glass nodule volumetry on low-dose and ultra-low-dose chest computed tomography: A phantom study.
Kim C; Kwack T; Kim W; Cha J; Yang Z; Yong HS
PLoS One; 2022; 17(6):e0270122. PubMed ID: 35737734
[TBL] [Abstract][Full Text] [Related]
5. Impact of dose reduction and iterative reconstruction algorithm on the detectability of pulmonary nodules by artificial intelligence.
Schwyzer M; Messerli M; Eberhard M; Skawran S; Martini K; Frauenfelder T
Diagn Interv Imaging; 2022 May; 103(5):273-280. PubMed ID: 34991993
[TBL] [Abstract][Full Text] [Related]
6. Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario.
Peters AA; Huber AT; Obmann VC; Heverhagen JT; Christe A; Ebner L
Eur Radiol; 2022 Jun; 32(6):4324-4332. PubMed ID: 35059804
[TBL] [Abstract][Full Text] [Related]
7. Accuracy of lung nodule volumetry in low-dose CT with iterative reconstruction: an anthropomorphic thoracic phantom study.
Doo KW; Kang EY; Yong HS; Woo OH; Lee KY; Oh YW
Br J Radiol; 2014 Sep; 87(1041):20130644. PubMed ID: 25026866
[TBL] [Abstract][Full Text] [Related]
8. Influence of radiation dose and iterative reconstruction algorithms for measurement accuracy and reproducibility of pulmonary nodule volumetry: A phantom study.
Kim H; Park CM; Song YS; Lee SM; Goo JM
Eur J Radiol; 2014 May; 83(5):848-57. PubMed ID: 24572380
[TBL] [Abstract][Full Text] [Related]
9. CT volumetry of artificial pulmonary nodules using an ex vivo lung phantom: influence of exposure parameters and iterative reconstruction on reproducibility.
Wielpütz MO; Lederlin M; Wroblewski J; Dinkel J; Eichinger M; Biederer J; Kauczor HU; Puderbach M
Eur J Radiol; 2013 Sep; 82(9):1577-83. PubMed ID: 23727376
[TBL] [Abstract][Full Text] [Related]
10. Pulmonary nodules with ground-glass opacity can be reliably measured with low-dose techniques regardless of iterative reconstruction: results of a phantom study.
Siegelman JW; Supanich MP; Gavrielides MA
AJR Am J Roentgenol; 2015 Jun; 204(6):1242-7. PubMed ID: 26001234
[TBL] [Abstract][Full Text] [Related]
11. Systematic error in lung nodule volumetry: effect of iterative reconstruction versus filtered back projection at different CT parameters.
Willemink MJ; Leiner T; Budde RP; de Kort FP; Vliegenthart R; van Ooijen PM; Oudkerk M; de Jong PA
AJR Am J Roentgenol; 2012 Dec; 199(6):1241-6. PubMed ID: 23169714
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Maximum-Intensity-Projection and Computer-Aided-Detection Algorithms as Stand-Alone Reader Devices in Lung Cancer Screening Using Different Dose Levels and Reconstruction Kernels.
Ebner L; Roos JE; Christensen JD; Dobrocky T; Leidolt L; Brela B; Obmann VC; Joy S; Huber A; Christe A
AJR Am J Roentgenol; 2016 Aug; 207(2):282-8. PubMed ID: 27249174
[TBL] [Abstract][Full Text] [Related]
14. First Performance Evaluation of an Artificial Intelligence-Based Computer-Aided Detection System for Pulmonary Nodule Evaluation in Dual-Source Photon-Counting Detector CT at Different Low-Dose Levels.
Jungblut L; Blüthgen C; Polacin M; Messerli M; Schmidt B; Euler A; Alkadhi H; Frauenfelder T; Martini K
Invest Radiol; 2022 Feb; 57(2):108-114. PubMed ID: 34324462
[TBL] [Abstract][Full Text] [Related]
15. Ultralow-dose CT with tin filtration for detection of solid and sub solid pulmonary nodules: a phantom study.
Martini K; Higashigaito K; Barth BK; Baumueller S; Alkadhi H; Frauenfelder T
Br J Radiol; 2015; 88(1056):20150389. PubMed ID: 26492317
[TBL] [Abstract][Full Text] [Related]
16. Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study.
Mikayama R; Shirasaka T; Kojima T; Sakai Y; Yabuuchi H; Kondo M; Kato T
Br J Radiol; 2022 Feb; 95(1130):20210915. PubMed ID: 34908478
[TBL] [Abstract][Full Text] [Related]
17. Accuracy of Pulmonary Nodule Volumetry Using Noise-Optimized Virtual Monoenergetic Image and Nonlinear Blending Image Algorithms in Dual-Energy Computed Tomography: A Phantom Study.
He C; Liu J; Hu S; Qing H; Qiao L; Luo H; Chen X; Zhou P
J Comput Assist Tomogr; 2020; 44(6):847-851. PubMed ID: 32976271
[TBL] [Abstract][Full Text] [Related]
18. [Artificial intelligence evaluation of simulated phantom lung nodules with different pre-adaptive iteration techniques].
Wen J; Kang WY; Lin M; Li L; Li TR; Zhong YH; Luo DH
Zhonghua Yi Xue Za Zhi; 2019 Nov; 99(43):3424-3427. PubMed ID: 31752472
[No Abstract] [Full Text] [Related]
19. The influence of a deep learning image reconstruction algorithm on the image quality and auto-analysis of pulmonary nodules at ultra-low dose chest CT: a phantom study.
Yao Y; Guo B; Li J; Yang Q; Li X; Deng L
Quant Imaging Med Surg; 2022 May; 12(5):2777-2791. PubMed ID: 35502370
[TBL] [Abstract][Full Text] [Related]
20. Computer-aided detection (CAD) of solid pulmonary nodules in chest x-ray equivalent ultralow dose chest CT - first in-vivo results at dose levels of 0.13mSv.
Messerli M; Kluckert T; Knitel M; Rengier F; Warschkow R; Alkadhi H; Leschka S; Wildermuth S; Bauer RW
Eur J Radiol; 2016 Dec; 85(12):2217-2224. PubMed ID: 27842670
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]