208 related articles for article (PubMed ID: 32981888)
21. Image quality improvement with deep learning-based reconstruction on abdominal ultrahigh-resolution CT: A phantom study.
Shirasaka T; Kojima T; Funama Y; Sakai Y; Kondo M; Mikayama R; Hamasaki H; Kato T; Ushijima Y; Asayama Y; Nishie A
J Appl Clin Med Phys; 2021 Jul; 22(7):286-296. PubMed ID: 34159736
[TBL] [Abstract][Full Text] [Related]
22. Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography.
Narita K; Nakamura Y; Higaki T; Akagi M; Honda Y; Awai K
Abdom Radiol (NY); 2020 Sep; 45(9):2698-2704. PubMed ID: 32248261
[TBL] [Abstract][Full Text] [Related]
23. Basics of iterative reconstruction methods in computed tomography: A vendor-independent overview.
Stiller W
Eur J Radiol; 2018 Dec; 109():147-154. PubMed ID: 30527298
[TBL] [Abstract][Full Text] [Related]
24. Complex Relationship Between Artificial Intelligence and CT Radiation Dose.
Gupta RV; Kalra MK; Ebrahimian S; Kaviani P; Primak A; Bizzo B; Dreyer KJ
Acad Radiol; 2022 Nov; 29(11):1709-1719. PubMed ID: 34836775
[TBL] [Abstract][Full Text] [Related]
25. Evaluating medical images using deep convolutional neural networks: A simulated CT phantom image study.
Hayashi N; Maruyama T; Sato Y; Watanabe H; Ogura T; Ogura A
Technol Health Care; 2020; 28(2):113-120. PubMed ID: 31156187
[TBL] [Abstract][Full Text] [Related]
26. A knowledge-based iterative model reconstruction algorithm: can super-low-dose cardiac CT be applicable in clinical settings?
Oda S; Utsunomiya D; Funama Y; Katahira K; Honda K; Tokuyasu S; Vembar M; Yuki H; Noda K; Oshima S; Yamashita Y
Acad Radiol; 2014 Jan; 21(1):104-10. PubMed ID: 24331272
[TBL] [Abstract][Full Text] [Related]
27. 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]
28. 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]
29. Detecting and Extracting Brain Hemorrhages from CT Images Using Generative Convolutional Imaging Scheme.
Pandimurugan V; Rajasoundaran S; Routray S; Prabu AV; Alyami H; Alharbi A; Ahmad S
Comput Intell Neurosci; 2022; 2022():6671234. PubMed ID: 35571726
[TBL] [Abstract][Full Text] [Related]
30. A qualitative and quantitative analysis of radiation dose and image quality of computed tomography images using adaptive statistical iterative reconstruction.
Hussain FA; Mail N; Shamy AM; Suliman A; Saoudi A
J Appl Clin Med Phys; 2016 May; 17(3):419-432. PubMed ID: 27167261
[TBL] [Abstract][Full Text] [Related]
31. Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality.
Nakamura Y; Higaki T; Tatsugami F; Honda Y; Narita K; Akagi M; Awai K
J Comput Assist Tomogr; 2020; 44(2):161-167. PubMed ID: 31789682
[TBL] [Abstract][Full Text] [Related]
32. Unpaired Low-Dose CT Denoising Network Based on Cycle-Consistent Generative Adversarial Network with Prior Image Information.
Tang C; Li J; Wang L; Li Z; Jiang L; Cai A; Zhang W; Liang N; Li L; Yan B
Comput Math Methods Med; 2019; 2019():8639825. PubMed ID: 31885686
[TBL] [Abstract][Full Text] [Related]
33. Assessment of noise reduction potential and image quality improvement of a new generation adaptive statistical iterative reconstruction (ASIR-V) in chest CT.
Tang H; Yu N; Jia Y; Yu Y; Duan H; Han D; Ma G; Ren C; He T
Br J Radiol; 2018 Jan; 91(1081):20170521. PubMed ID: 29076347
[TBL] [Abstract][Full Text] [Related]
34. Artificial intelligence in image reconstruction: The change is here.
Singh R; Wu W; Wang G; Kalra MK
Phys Med; 2020 Nov; 79():113-125. PubMed ID: 33246273
[TBL] [Abstract][Full Text] [Related]
35. Dose Reduction While Preserving Diagnostic Quality in Head CT: Advancing the Application of Iterative Reconstruction Using a Live Animal Model.
Raslau FD; Escott EJ; Smiley J; Adams C; Feigal D; Ganesh H; Wang C; Zhang J
AJNR Am J Neuroradiol; 2019 Nov; 40(11):1864-1870. PubMed ID: 31601574
[TBL] [Abstract][Full Text] [Related]
36. Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations.
Fischer AM; Yacoub B; Savage RH; Martinez JD; Wichmann JL; Sahbaee P; Grbic S; Varga-Szemes A; Schoepf UJ
J Thorac Imaging; 2020 May; 35 Suppl 1():S21-S27. PubMed ID: 32317574
[TBL] [Abstract][Full Text] [Related]
37. Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction.
Ichikawa Y; Kanii Y; Yamazaki A; Nagasawa N; Nagata M; Ishida M; Kitagawa K; Sakuma H
Jpn J Radiol; 2021 Jun; 39(6):598-604. PubMed ID: 33449305
[TBL] [Abstract][Full Text] [Related]
38. Improvements to image quality using hybrid and model-based iterative reconstructions: a phantom study.
Aurumskjöld ML; Ydström K; Tingberg A; Söderberg M
Acta Radiol; 2017 Jan; 58(1):53-61. PubMed ID: 26924832
[TBL] [Abstract][Full Text] [Related]
39. Demystification of AI-driven medical image interpretation: past, present and future.
Savadjiev P; Chong J; Dohan A; Vakalopoulou M; Reinhold C; Paragios N; Gallix B
Eur Radiol; 2019 Mar; 29(3):1616-1624. PubMed ID: 30105410
[TBL] [Abstract][Full Text] [Related]
40. Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images.
Tian SF; Liu AL; Liu JH; Liu YJ; Pan JD
Jpn J Radiol; 2019 Feb; 37(2):186-190. PubMed ID: 30523499
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]