817 related articles for article (PubMed ID: 31484849)
1. Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers.
Kidoh M; Shinoda K; Kitajima M; Isogawa K; Nambu M; Uetani H; Morita K; Nakaura T; Tateishi M; Yamashita Y; Yamashita Y
Magn Reson Med Sci; 2020 Aug; 19(3):195-206. PubMed ID: 31484849
[TBL] [Abstract][Full Text] [Related]
2. Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images.
Tajima T; Akai H; Yasaka K; Kunimatsu A; Yamashita Y; Akahane M; Yoshioka N; Abe O; Ohtomo K; Kiryu S
Clin Radiol; 2023 Jan; 78(1):e13-e21. PubMed ID: 36116967
[TBL] [Abstract][Full Text] [Related]
3. [Improvement of Motion Artifacts in Brain MRI Using Deep Learning by Simulation Training Data].
Muro I; Shimizu S; Tsukamoto H
Nihon Hoshasen Gijutsu Gakkai Zasshi; 2022; 78(1):13-22. PubMed ID: 35046218
[TBL] [Abstract][Full Text] [Related]
4. Clinical feasibility of an abdominal thin-slice breath-hold single-shot fast spin echo sequence processed using a deep learning-based noise-reduction approach.
Tajima T; Akai H; Yasaka K; Kunimatsu A; Akahane M; Yoshioka N; Abe O; Ohtomo K; Kiryu S
Magn Reson Imaging; 2022 Jul; 90():76-83. PubMed ID: 35504409
[TBL] [Abstract][Full Text] [Related]
5. MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR-CDPM).
Safari M; Yang X; Fatemi A; Archambault L
Med Phys; 2024 Apr; 51(4):2598-2610. PubMed ID: 38009583
[TBL] [Abstract][Full Text] [Related]
6. Introducing Swish and Parallelized Blind Removal Improves the Performance of a Convolutional Neural Network in Denoising MR Images.
Sugai T; Takano K; Ouchi S; Ito S
Magn Reson Med Sci; 2021 Dec; 20(4):410-424. PubMed ID: 33583867
[TBL] [Abstract][Full Text] [Related]
7. Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes.
Yasaka K; Tanishima T; Ohtake Y; Tajima T; Akai H; Ohtomo K; Abe O; Kiryu S
Eur Radiol; 2022 Sep; 32(9):6118-6125. PubMed ID: 35348861
[TBL] [Abstract][Full Text] [Related]
8. Deep learning-based convolutional neural network for intramodality brain MRI synthesis.
Osman AFI; Tamam NM
J Appl Clin Med Phys; 2022 Apr; 23(4):e13530. PubMed ID: 35044073
[TBL] [Abstract][Full Text] [Related]
9. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network.
Jiang D; Dou W; Vosters L; Xu X; Sun Y; Tan T
Jpn J Radiol; 2018 Sep; 36(9):566-574. PubMed ID: 29982919
[TBL] [Abstract][Full Text] [Related]
10. Thin-slice 2D MR Imaging of the Shoulder Joint Using Denoising Deep Learning Reconstruction Provides Higher Image Quality Than 3D MR Imaging.
Kakigi T; Sakamoto R; Arai R; Yamamoto A; Kuriyama S; Sano Y; Imai R; Numamoto H; Miyake KK; Saga T; Matsuda S; Nakamoto Y
Magn Reson Med Sci; 2024 May; ():. PubMed ID: 38777762
[TBL] [Abstract][Full Text] [Related]
11. Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance.
Oshima S; Fushimi Y; Miyake KK; Nakajima S; Sakata A; Okuchi S; Hinoda T; Otani S; Numamoto H; Fujimoto K; Shima A; Nambu M; Sawamoto N; Takahashi R; Ueno K; Saga T; Nakamoto Y
Jpn J Radiol; 2023 Nov; 41(11):1216-1225. PubMed ID: 37256470
[TBL] [Abstract][Full Text] [Related]
12. Self-supervised structural similarity-based convolutional neural network for cardiac diffusion tensor image denoising.
Yuan N; Wang L; Ye C; Deng Z; Zhang J; Zhu Y
Med Phys; 2023 Oct; 50(10):6137-6150. PubMed ID: 36775901
[TBL] [Abstract][Full Text] [Related]
13. Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI.
Gong E; Pauly JM; Wintermark M; Zaharchuk G
J Magn Reson Imaging; 2018 Aug; 48(2):330-340. PubMed ID: 29437269
[TBL] [Abstract][Full Text] [Related]
14. Deep Learning Approach for Generating MRA Images From 3D Quantitative Synthetic MRI Without Additional Scans.
Fujita S; Hagiwara A; Otsuka Y; Hori M; Takei N; Hwang KP; Irie R; Andica C; Kamagata K; Akashi T; Kunishima Kumamaru K; Suzuki M; Wada A; Abe O; Aoki S
Invest Radiol; 2020 Apr; 55(4):249-256. PubMed ID: 31977603
[TBL] [Abstract][Full Text] [Related]
15. High-fidelity fast volumetric brain MRI using synergistic wave-controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN).
Li Z; Tian Q; Ngamsombat C; Cartmell S; Conklin J; Filho ALMG; Lo WC; Wang G; Ying K; Setsompop K; Fan Q; Bilgic B; Cauley S; Huang SY
Med Phys; 2022 Feb; 49(2):1000-1014. PubMed ID: 34961944
[TBL] [Abstract][Full Text] [Related]
16. Diagnostic advantage of thin slice 2D MRI and multiplanar reconstruction of the knee joint using deep learning based denoising approach.
Kakigi T; Sakamoto R; Tagawa H; Kuriyama S; Goto Y; Nambu M; Sagawa H; Numamoto H; Miyake KK; Saga T; Matsuda S; Nakamoto Y
Sci Rep; 2022 Jun; 12(1):10362. PubMed ID: 35725760
[TBL] [Abstract][Full Text] [Related]
17. A Generative Adversarial Network technique for high-quality super-resolution reconstruction of cardiac magnetic resonance images.
Zhao M; Wei Y; Wong KKL
Magn Reson Imaging; 2022 Jan; 85():153-160. PubMed ID: 34699953
[TBL] [Abstract][Full Text] [Related]
18. Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer.
Tajima T; Akai H; Sugawara H; Furuta T; Yasaka K; Kunimatsu A; Yoshioka N; Akahane M; Abe O; Ohtomo K; Kiryu S
Magn Reson Imaging; 2022 Oct; 92():169-179. PubMed ID: 35772583
[TBL] [Abstract][Full Text] [Related]
19. Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography.
Usui K; Ogawa K; Goto M; Sakano Y; Kyougoku S; Daida H
Vis Comput Ind Biomed Art; 2021 Jul; 4(1):21. PubMed ID: 34304321
[TBL] [Abstract][Full Text] [Related]
20. High-field mr diffusion-weighted image denoising using a joint denoising convolutional neural network.
Wang H; Zheng R; Dai F; Wang Q; Wang C
J Magn Reson Imaging; 2019 Dec; 50(6):1937-1947. PubMed ID: 31012226
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]