140 related articles for article (PubMed ID: 37693207)
1. Super-Resolution Deep Learning Reconstruction for Improved Image Quality of Coronary CT Angiography.
Takafuji M; Kitagawa K; Mizutani S; Hamaguchi A; Kisou R; Iio K; Ichikawa K; Izumi D; Sakuma H
Radiol Cardiothorac Imaging; 2023 Aug; 5(4):e230085. PubMed ID: 37693207
[TBL] [Abstract][Full Text] [Related]
2. Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography.
Nagayama Y; Emoto T; Kato Y; Kidoh M; Oda S; Sakabe D; Funama Y; Nakaura T; Hayashi H; Takada S; Uchimura R; Hatemura M; Tsujita K; Hirai T
Eur Radiol; 2023 Dec; 33(12):8488-8500. PubMed ID: 37432405
[TBL] [Abstract][Full Text] [Related]
3. Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience.
Orii M; Sone M; Osaki T; Ueyama Y; Chiba T; Sasaki T; Yoshioka K
BMC Med Imaging; 2023 Oct; 23(1):171. PubMed ID: 37904089
[TBL] [Abstract][Full Text] [Related]
4. Improved stent sharpness evaluation with super-resolution deep learning reconstruction in coronary CT angiography.
Ryu JK; Kim KH; Otgonbaatar C; Kim DS; Shim H; Seo JW
Br J Radiol; 2024 Jun; 97(1159):1286-1294. PubMed ID: 38733576
[TBL] [Abstract][Full Text] [Related]
5. Coronary Stent Evaluation by CTA: Image Quality Comparison Between Super-Resolution Deep Learning Reconstruction and Other Reconstruction Algorithms.
Nagayama Y; Emoto T; Hayashi H; Kidoh M; Oda S; Nakaura T; Sakabe D; Funama Y; Tabata N; Ishii M; Yamanaga K; Fujisue K; Takashio S; Yamamoto E; Tsujita K; Hirai T
AJR Am J Roentgenol; 2023 Nov; 221(5):599-610. PubMed ID: 37377362
[No Abstract] [Full Text] [Related]
6. Improvement of Spatial Resolution on Coronary CT Angiography by Using Super-Resolution Deep Learning Reconstruction.
Tatsugami F; Higaki T; Kawashita I; Fukumoto W; Nakamura Y; Matsuura M; Lee TC; Zhou J; Cai L; Kitagawa T; Nakano Y; Awai K
Acad Radiol; 2023 Nov; 30(11):2497-2504. PubMed ID: 36681533
[TBL] [Abstract][Full Text] [Related]
7. A preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms.
Otgonbaatar C; Kim H; Jeon PH; Jeon SH; Cha SJ; Ryu JK; Jung WB; Shim H; Ko SM; Kim JW
Br J Radiol; 2024 Jun; ():. PubMed ID: 38917414
[TBL] [Abstract][Full Text] [Related]
8. Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction.
Otgonbaatar C; Ryu JK; Shin J; Woo JY; Seo JW; Shim H; Hwang DH
Korean J Radiol; 2022 Nov; 23(11):1044-1054. PubMed ID: 36196766
[TBL] [Abstract][Full Text] [Related]
9. Super resolution deep learning reconstruction for coronary CT angiography: A structured phantom study.
Higaki T; Tatsugami F; Ohana M; Nakamura Y; Kawashita I; Awai K
Eur J Radiol Open; 2024 Jun; 12():100570. PubMed ID: 38828096
[TBL] [Abstract][Full Text] [Related]
10. [Deep learning reconstruction algorithm for coronary CT angiography in assessing obstructive coronary artery disease caused by calcified lesions: the clinical application value].
Xu C; Yi Y; Li YY; Guo YB; Jin ZY; Wang YN
Zhonghua Yi Xue Za Zhi; 2021 Oct; 101(39):3202-3207. PubMed ID: 34689531
[No Abstract] [Full Text] [Related]
11. Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction.
Yasaka K; Uehara S; Kato S; Watanabe Y; Tajima T; Akai H; Yoshioka N; Akahane M; Ohtomo K; Abe O; Kiryu S
J Imaging Inform Med; 2024 Apr; ():. PubMed ID: 38671337
[TBL] [Abstract][Full Text] [Related]
12. Deep learning-based image restoration algorithm for coronary CT angiography.
Tatsugami F; Higaki T; Nakamura Y; Yu Z; Zhou J; Lu Y; Fujioka C; Kitagawa T; Kihara Y; Iida M; Awai K
Eur Radiol; 2019 Oct; 29(10):5322-5329. PubMed ID: 30963270
[TBL] [Abstract][Full Text] [Related]
13. Super-resolution Deep Learning Reconstruction for 3D Brain MR Imaging: Improvement of Cranial Nerve Depiction and Interobserver Agreement in Evaluations of Neurovascular Conflict.
Yasaka K; Kanzawa J; Nakaya M; Kurokawa R; Tajima T; Akai H; Yoshioka N; Akahane M; Ohtomo K; Abe O; Kiryu S
Acad Radiol; 2024 Jun; ():. PubMed ID: 38897913
[TBL] [Abstract][Full Text] [Related]
14. Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning-Based Reconstruction: A Clinical and Phantom Study.
Nagayama Y; Goto M; Sakabe D; Emoto T; Shigematsu S; Oda S; Tanoue S; Kidoh M; Nakaura T; Funama Y; Uchimura R; Takada S; Hayashi H; Hatemura M; Hirai T
AJR Am J Roentgenol; 2022 Aug; 219(2):315-324. PubMed ID: 35195431
[No Abstract] [Full Text] [Related]
15. Exploring the impact of super-resolution deep learning on MR angiography image quality.
Hokamura M; Uetani H; Nakaura T; Matsuo K; Morita K; Nagayama Y; Kidoh M; Yamashita Y; Ueda M; Mukasa A; Hirai T
Neuroradiology; 2024 Feb; 66(2):217-226. PubMed ID: 38148334
[TBL] [Abstract][Full Text] [Related]
16. Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality.
Emoto T; Nagayama Y; Takada S; Sakabe D; Shigematsu S; Goto M; Nakato K; Yoshida R; Harai R; Kidoh M; Oda S; Nakaura T; Hirai T
Phys Eng Sci Med; 2024 Jun; ():. PubMed ID: 38884668
[TBL] [Abstract][Full Text] [Related]
17. A preliminary study of deep learning-based reconstruction specialized for denoising in high-frequency domain: usefulness in high-resolution three-dimensional magnetic resonance cisternography of the cerebellopontine angle.
Uetani H; Nakaura T; Kitajima M; Yamashita Y; Hamasaki T; Tateishi M; Morita K; Sasao A; Oda S; Ikeda O; Yamashita Y
Neuroradiology; 2021 Jan; 63(1):63-71. PubMed ID: 32794075
[TBL] [Abstract][Full Text] [Related]
18. Feasibility of high-resolution magnetic resonance imaging of the liver using deep learning reconstruction based on the deep learning denoising technique.
Tanabe M; Higashi M; Yonezawa T; Yamaguchi T; Iida E; Furukawa M; Okada M; Shinoda K; Ito K
Magn Reson Imaging; 2021 Jul; 80():121-126. PubMed ID: 33971240
[TBL] [Abstract][Full Text] [Related]
19. Assessment of Image Quality of Coronary CT Angiography Using Deep Learning-Based CT Reconstruction: Phantom and Patient Studies.
Jeon PH; Jeon SH; Ko D; An G; Shim H; Otgonbaatar C; Son K; Kim D; Ko SM; Chung MA
Diagnostics (Basel); 2023 May; 13(11):. PubMed ID: 37296714
[TBL] [Abstract][Full Text] [Related]
20. Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom.
Sawamura S; Kato S; Funama Y; Oda S; Mochizuki H; Inagaki S; Takeuchi Y; Morioka T; Izumi T; Ota Y; Kawagoe H; Cheng S; Nakayama N; Fukui K; Tsutsumi T; Iwasawa T; Utsunomiya D
Quant Imaging Med Surg; 2024 Apr; 14(4):2870-2883. PubMed ID: 38617144
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]