121 related articles for article (PubMed ID: 38389358)
21. 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]
22. Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT.
Xu JJ; Lönn L; Budtz-Jørgensen E; Hansen KL; Ulriksen PS
Eur Radiol; 2022 Oct; 32(10):7098-7107. PubMed ID: 35895120
[TBL] [Abstract][Full Text] [Related]
23. Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI.
Wang H; Yue S; Liu N; Chen Y; Zhan P; Liu X; Shang B; Wang L; Li Z; Gao J; Lyu P
Eur Radiol; 2024 Mar; 34(3):1614-1623. PubMed ID: 37650972
[TBL] [Abstract][Full Text] [Related]
24. Deep-learning-based image reconstruction in dynamic contrast-enhanced abdominal CT: image quality and lesion detection among reconstruction strength levels.
Kaga T; Noda Y; Fujimoto K; Suto T; Kawai N; Miyoshi T; Hyodo F; Matsuo M
Clin Radiol; 2021 Sep; 76(9):710.e15-710.e24. PubMed ID: 33879322
[TBL] [Abstract][Full Text] [Related]
25. 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]
26. Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen.
Sato M; Ichikawa Y; Domae K; Yoshikawa K; Kanii Y; Yamazaki A; Nagasawa N; Nagata M; Ishida M; Sakuma H
Eur Radiol; 2022 Aug; 32(8):5499-5507. PubMed ID: 35238970
[TBL] [Abstract][Full Text] [Related]
27. Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography.
De Santis D; Polidori T; Tremamunno G; Rucci C; Piccinni G; Zerunian M; Pugliese L; Del Gaudio A; Guido G; Barbato L; Laghi A; Caruso D
Radiol Med; 2023 Apr; 128(4):434-444. PubMed ID: 36847992
[TBL] [Abstract][Full Text] [Related]
28. Application of deep learning image reconstruction in low-dose chest CT scan.
Wang H; Li LL; Shang J; Song J; Liu B
Br J Radiol; 2022 May; 95(1133):20210380. PubMed ID: 35084210
[TBL] [Abstract][Full Text] [Related]
29. Deep-learning image reconstruction for image quality evaluation and accurate bone mineral density measurement on quantitative CT: A phantom-patient study.
Li Y; Jiang Y; Yu X; Ren B; Wang C; Chen S; Ma D; Su D; Liu H; Ren X; Yang X; Gao J; Wu Y
Front Endocrinol (Lausanne); 2022; 13():884306. PubMed ID: 36034436
[TBL] [Abstract][Full Text] [Related]
30. A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images.
Nikolau EP; Toia GV; Nett B; Tang J; Szczykutowicz TP
J Comput Assist Tomogr; 2023 May-Jun 01; 47(3):437-444. PubMed ID: 37185008
[TBL] [Abstract][Full Text] [Related]
31. Iterative reconstruction
Qu T; Guo Y; Li J; Cao L; Li Y; Chen L; Sun J; Lu X; Guo J
Br J Radiol; 2022 Dec; 95(1140):20220196. PubMed ID: 36341682
[TBL] [Abstract][Full Text] [Related]
32. A preliminary evaluation study of applying a deep learning image reconstruction algorithm in low-kilovolt scanning of upper abdomen.
Wang YN; Du Y; Shi GF; Wang Q; Li RX; Qi XH; Cai XJ; Zhang X
J Xray Sci Technol; 2021; 29(4):687-695. PubMed ID: 34092694
[TBL] [Abstract][Full Text] [Related]
33. Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience.
Jensen CT; Liu X; Tamm EP; Chandler AG; Sun J; Morani AC; Javadi S; Wagner-Bartak NA
AJR Am J Roentgenol; 2020 Jul; 215(1):50-57. PubMed ID: 32286872
[No Abstract] [Full Text] [Related]
34. Deep learning image reconstruction algorithms in low-dose radiation abdominal computed tomography: assessment of image quality and lesion diagnostic confidence.
Yang C; Wang W; Cui D; Zhang J; Liu L; Wang Y; Li W
Quant Imaging Med Surg; 2023 May; 13(5):3161-3173. PubMed ID: 37179954
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study.
Li H; Li Z; Gao S; Hu J; Yang Z; Peng Y; Sun J
J Xray Sci Technol; 2024; 32(3):513-528. PubMed ID: 38393883
[TBL] [Abstract][Full Text] [Related]
37. Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?
Lyu P; Liu N; Harrawood B; Solomon J; Wang H; Chen Y; Rigiroli F; Ding Y; Schwartz FR; Jiang H; Lowry C; Wang L; Samei E; Gao J; Marin D
Eur Radiol; 2023 Mar; 33(3):1629-1640. PubMed ID: 36323984
[TBL] [Abstract][Full Text] [Related]
38. Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases.
Jensen CT; Gupta S; Saleh MM; Liu X; Wong VK; Salem U; Qiao W; Samei E; Wagner-Bartak NA
Radiology; 2022 Apr; 303(1):90-98. PubMed ID: 35014900
[TBL] [Abstract][Full Text] [Related]
39. Clinical evaluation of image quality and radiation dose reduction in upper abdominal computed tomography using model-based iterative reconstruction; comparison with filtered back projection and adaptive statistical iterative reconstruction.
Nakamoto A; Kim T; Hori M; Onishi H; Tsuboyama T; Sakane M; Tatsumi M; Tomiyama N
Eur J Radiol; 2015 Sep; 84(9):1715-23. PubMed ID: 26037266
[TBL] [Abstract][Full Text] [Related]
40. The value of a deep learning image reconstruction algorithm in whole-brain computed tomography perfusion in patients with acute ischemic stroke.
Lei L; Zhou Y; Guo X; Wang L; Zhao X; Wang H; Ma J; Yue S
Quant Imaging Med Surg; 2023 Dec; 13(12):8173-8189. PubMed ID: 38106310
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]