153 related articles for article (PubMed ID: 37454556)
41. Determining the radiation dose reduction potential for coronary calcium scanning with computed tomography: an anthropomorphic phantom study comparing filtered backprojection and the adaptive iterative dose reduction algorithm for image reconstruction.
Blobel J; Mews J; Schuijf JD; Overlaet W
Invest Radiol; 2013 Dec; 48(12):857-62. PubMed ID: 23917328
[TBL] [Abstract][Full Text] [Related]
42. Computed tomography radiation dose reduction: effect of different iterative reconstruction algorithms on image quality.
Willemink MJ; Takx RA; de Jong PA; Budde RP; Bleys RL; Das M; Wildberger JE; Prokop M; Buls N; de Mey J; Leiner T; Schilham AM
J Comput Assist Tomogr; 2014; 38(6):815-23. PubMed ID: 24983438
[TBL] [Abstract][Full Text] [Related]
43. 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]
44. Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra-Low-Dose Chest CT.
Jiang B; Li N; Shi X; Zhang S; Li J; de Bock GH; Vliegenthart R; Xie X
Radiology; 2022 Apr; 303(1):202-212. PubMed ID: 35040674
[TBL] [Abstract][Full Text] [Related]
45. Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection.
Noda Y; Kaga T; Kawai N; Miyoshi T; Kawada H; Hyodo F; Kambadakone A; Matsuo M
Br J Radiol; 2021 May; 94(1121):20201329. PubMed ID: 33571010
[TBL] [Abstract][Full Text] [Related]
46. 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]
47. 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]
48. Hybrid Type iterative reconstruction method vs. filter back projection method: Capability for radiation dose reduction and perfusion assessment on dynamic first-pass contrast-enhanced perfusion chest area-detector CT.
Ohno Y; Koyama H; Fujisawa Y; Yoshikawa T; Inokawa H; Sugihara N; Seki S; Sugimura K
Eur J Radiol; 2016 Jan; 85(1):164-175. PubMed ID: 26724662
[TBL] [Abstract][Full Text] [Related]
49. A tin filter's dose reduction effect revisited: Using the detectability index in low-dose computed tomography for the chest.
Hasegawa A; Ichikawa K; Morioka Y; Kawashima H
Phys Med; 2022 Jul; 99():61-67. PubMed ID: 35623206
[TBL] [Abstract][Full Text] [Related]
50. CT image quality in sinogram affirmed iterative reconstruction phantom study - is there a point of diminishing returns?
Infante JC; Liu Y; Rigsby CK
Pediatr Radiol; 2017 Mar; 47(3):333-341. PubMed ID: 27891546
[TBL] [Abstract][Full Text] [Related]
51. Hybrid iterative reconstruction technique for abdominal CT protocols in obese patients: assessment of image quality, radiation dose, and low-contrast detectability in a phantom.
Schindera ST; Odedra D; Mercer D; Thipphavong S; Chou P; Szucs-Farkas Z; Rogalla P
AJR Am J Roentgenol; 2014 Feb; 202(2):W146-52. PubMed ID: 24450696
[TBL] [Abstract][Full Text] [Related]
52. Image quality and radiologists' subjective acceptance using model-based iterative and deep learning reconstructions as adjuncts to ultrahigh-resolution CT in low-dose contrast-enhanced abdominopelvic CT: phantom and clinical pilot studies.
Nishikawa M; Machida H; Shimizu Y; Kariyasu T; Morisaka H; Adachi T; Nakai T; Sakaguchi K; Saito S; Matsumoto S; Koyanagi M; Yokoyama K
Abdom Radiol (NY); 2022 Feb; 47(2):891-902. PubMed ID: 34914007
[TBL] [Abstract][Full Text] [Related]
53. Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT.
Nakamura Y; Narita K; Higaki T; Akagi M; Honda Y; Awai K
Eur Radiol; 2021 Jul; 31(7):4700-4709. PubMed ID: 33389036
[TBL] [Abstract][Full Text] [Related]
54. Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction.
Brady SL; Trout AT; Somasundaram E; Anton CG; Li Y; Dillman JR
Radiology; 2021 Jan; 298(1):180-188. PubMed ID: 33201790
[TBL] [Abstract][Full Text] [Related]
55. Lung nodule detection by microdose CT versus chest radiography (standard and dual-energy subtracted).
Ebner L; Bütikofer Y; Ott D; Huber A; Landau J; Roos JE; Heverhagen JT; Christe A
AJR Am J Roentgenol; 2015 Apr; 204(4):727-35. PubMed ID: 25794062
[TBL] [Abstract][Full Text] [Related]
56. Comparison of image quality between spectral photon-counting CT and dual-layer CT for the evaluation of lung nodules: a phantom study.
Si-Mohamed SA; Greffier J; Miailhes J; Boccalini S; Rodesch PA; Vuillod A; van der Werf N; Dabli D; Racine D; Rotzinger D; Becce F; Yagil Y; Coulon P; Vlassenbroek A; Boussel L; Beregi JP; Douek P
Eur Radiol; 2022 Jan; 32(1):524-532. PubMed ID: 34185147
[TBL] [Abstract][Full Text] [Related]
57. Iterative reconstruction algorithm for abdominal multidetector CT at different tube voltages: assessment of diagnostic accuracy, image quality, and radiation dose in a phantom study.
Schindera ST; Diedrichsen L; Müller HC; Rusch O; Marin D; Schmidt B; Raupach R; Vock P; Szucs-Farkas Z
Radiology; 2011 Aug; 260(2):454-62. PubMed ID: 21493795
[TBL] [Abstract][Full Text] [Related]
58. 3D automatic exposure control for 64-detector row CT: radiation dose reduction in chest phantom study.
Matsumoto K; Ohno Y; Koyama H; Kono A; Inokawa H; Onishi Y; Nogami M; Takenaka D; Araki T; Sugimura K
Eur J Radiol; 2011 Mar; 77(3):522-7. PubMed ID: 19836179
[TBL] [Abstract][Full Text] [Related]
59. Pulmonary nodule volumetric accuracy of a deep learning-based reconstruction algorithm in low-dose computed tomography: A phantom study.
Watanabe S; Sakaguchi K; Kitaguchi S; Ishii K
Phys Med; 2022 Dec; 104():1-9. PubMed ID: 36347080
[TBL] [Abstract][Full Text] [Related]
60. Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm.
Solomon J; Lyu P; Marin D; Samei E
Med Phys; 2020 Sep; 47(9):3961-3971. PubMed ID: 32506661
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]