191 related articles for article (PubMed ID: 35508147)
1. Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction.
Karkalousos D; Noteboom S; Hulst HE; Vos FM; Caan MWA
Phys Med Biol; 2022 Jun; 67(12):. PubMed ID: 35508147
[No Abstract] [Full Text] [Related]
2. Learning a variational network for reconstruction of accelerated MRI data.
Hammernik K; Klatzer T; Kobler E; Recht MP; Sodickson DK; Pock T; Knoll F
Magn Reson Med; 2018 Jun; 79(6):3055-3071. PubMed ID: 29115689
[TBL] [Abstract][Full Text] [Related]
3. Recurrent inference machines for reconstructing heterogeneous MRI data.
Lønning K; Putzky P; Sonke JJ; Reneman L; Caan MWA; Welling M
Med Image Anal; 2019 Apr; 53():64-78. PubMed ID: 30703579
[TBL] [Abstract][Full Text] [Related]
4. Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging.
Yaman B; Gu H; Hosseini SAH; Demirel OB; Moeller S; Ellermann J; Uğurbil K; Akçakaya M
NMR Biomed; 2022 Dec; 35(12):e4798. PubMed ID: 35789133
[TBL] [Abstract][Full Text] [Related]
5. Accelerating image reconstruction for multi-contrast MRI based on Y-Net3.
Cai X; Hou X; Sun R; Chang X; Zhu H; Jia S; Nie S
J Xray Sci Technol; 2023; 31(4):797-810. PubMed ID: 37248943
[TBL] [Abstract][Full Text] [Related]
6. Evaluation on the generalization of a learned convolutional neural network for MRI reconstruction.
Huang J; Wang S; Zhou G; Hu W; Yu G
Magn Reson Imaging; 2022 Apr; 87():38-46. PubMed ID: 34968699
[TBL] [Abstract][Full Text] [Related]
7. Deep compressed sensing MRI via a gradient-enhanced fusion model.
Dai Y; Wang C; Wang H
Med Phys; 2023 Mar; 50(3):1390-1405. PubMed ID: 36695158
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. A unified model for reconstruction and R
Zhang C; Karkalousos D; Bazin PL; Coolen BF; Vrenken H; Sonke JJ; Forstmann BU; Poot DHJ; Caan MWA
Neuroimage; 2022 Dec; 264():119680. PubMed ID: 36240989
[TBL] [Abstract][Full Text] [Related]
10. Reconstruction of multicontrast MR images through deep learning.
Do WJ; Seo S; Han Y; Ye JC; Choi SH; Park SH
Med Phys; 2020 Mar; 47(3):983-997. PubMed ID: 31889314
[TBL] [Abstract][Full Text] [Related]
11. Simultaneously optimizing sampling pattern for joint acceleration of multi-contrast MRI using model-based deep learning.
Seo S; Luu HM; Choi SH; Park SH
Med Phys; 2022 Sep; 49(9):5964-5980. PubMed ID: 35678739
[TBL] [Abstract][Full Text] [Related]
12. Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data.
Yaman B; Hosseini SAH; Moeller S; Ellermann J; Uğurbil K; Akçakaya M
Magn Reson Med; 2020 Dec; 84(6):3172-3191. PubMed ID: 32614100
[TBL] [Abstract][Full Text] [Related]
13. 2D probabilistic undersampling pattern optimization for MR image reconstruction.
Xue S; Cheng Z; Han G; Sun C; Fang K; Liu Y; Cheng J; Jin X; Bai R
Med Image Anal; 2022 Apr; 77():102346. PubMed ID: 35030342
[TBL] [Abstract][Full Text] [Related]
14. SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction.
Liu F; Samsonov A; Chen L; Kijowski R; Feng L
Magn Reson Med; 2019 Nov; 82(5):1890-1904. PubMed ID: 31166049
[TBL] [Abstract][Full Text] [Related]
15. Validation of a Denoising Method Using Deep Learning-Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging.
Yamamoto T; Lacheret C; Fukutomi H; Kamraoui RA; Denat L; Zhang B; Prevost V; Zhang L; Ruet A; Triaire B; Dousset V; Coupé P; Tourdias T
AJNR Am J Neuroradiol; 2022 Aug; 43(8):1099-1106. PubMed ID: 35902124
[TBL] [Abstract][Full Text] [Related]
16. DC-SiamNet: Deep contrastive Siamese network for self-supervised MRI reconstruction.
Yan Y; Yang T; Zhao X; Jiao C; Yang A; Miao J
Comput Biol Med; 2023 Dec; 167():107619. PubMed ID: 37925909
[TBL] [Abstract][Full Text] [Related]
17. Dynamic recurrent inference machines for accelerated MRI-guided radiotherapy of the liver.
Lønning K; Caan MWA; Nowee ME; Sonke JJ
Comput Med Imaging Graph; 2024 Apr; 113():102348. PubMed ID: 38368665
[TBL] [Abstract][Full Text] [Related]
18. Single patient convolutional neural networks for real-time MR reconstruction: coherent low-resolution versus incoherent undersampling.
Dietz B; Yun J; Yip E; Gabos Z; Fallone BG; Wachowicz K
Phys Med Biol; 2020 Apr; 65(8):08NT03. PubMed ID: 32135531
[TBL] [Abstract][Full Text] [Related]
19. An Efficient Light-weight Network for Fast Reconstruction on MR Images.
Zhen B; Zheng Y; Qiu B
Curr Med Imaging; 2021; 17(11):1374-1384. PubMed ID: 33459243
[TBL] [Abstract][Full Text] [Related]
20. Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images.
Rachmadi MF; Valdés-Hernández MDC; Li H; Guerrero R; Meijboom R; Wiseman S; Waldman A; Zhang J; Rueckert D; Wardlaw J; Komura T
Comput Med Imaging Graph; 2020 Jan; 79():101685. PubMed ID: 31846826
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]