159 related articles for article (PubMed ID: 38167996)
1. AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models.
Beljaards L; Pezzotti N; Rao C; Doneva M; van Osch MJP; Staring M
Med Phys; 2024 May; 51(5):3555-3565. PubMed ID: 38167996
[TBL] [Abstract][Full Text] [Related]
2. Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model.
Haskell MW; Cauley SF; Bilgic B; Hossbach J; Splitthoff DN; Pfeuffer J; Setsompop K; Wald LL
Magn Reson Med; 2019 Oct; 82(4):1452-1461. PubMed ID: 31045278
[TBL] [Abstract][Full Text] [Related]
3. Automatic MR image quality evaluation using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging.
Fantini I; Yasuda C; Bento M; Rittner L; Cendes F; Lotufo R
Comput Med Imaging Graph; 2021 Jun; 90():101897. PubMed ID: 33770561
[TBL] [Abstract][Full Text] [Related]
4. Deep learning-based image reconstruction and motion estimation from undersampled radial k-space for real-time MRI-guided radiotherapy.
Terpstra ML; Maspero M; d'Agata F; Stemkens B; Intven MPW; Lagendijk JJW; van den Berg CAT; Tijssen RHN
Phys Med Biol; 2020 Aug; 65(15):155015. PubMed ID: 32408295
[TBL] [Abstract][Full Text] [Related]
5. Deep learning-based motion quantification from k-space for fast model-based magnetic resonance imaging motion correction.
Hossbach J; Splitthoff DN; Cauley S; Clifford B; Polak D; Lo WC; Meyer H; Maier A
Med Phys; 2023 Apr; 50(4):2148-2161. PubMed ID: 36433748
[TBL] [Abstract][Full Text] [Related]
6. Choreography Controlled (ChoCo) brain MRI artifact generation for labeled motion-corrupted datasets.
Dabrowski O; Courvoisier S; Falcone JL; Klauser A; Songeon J; Kocher M; Chopard B; Lazeyras F
Phys Med; 2022 Oct; 102():79-87. PubMed ID: 36137403
[TBL] [Abstract][Full Text] [Related]
7. Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions.
Duffy BA; Zhao L; Sepehrband F; Min J; Wang DJ; Shi Y; Toga AW; Kim H;
Neuroimage; 2021 Apr; 230():117756. PubMed ID: 33460797
[TBL] [Abstract][Full Text] [Related]
8. Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics.
Vakli P; Weiss B; Szalma J; Barsi P; Gyuricza I; Kemenczky P; Somogyi E; Nárai Á; Gál V; Hermann P; Vidnyánszky Z
Med Image Anal; 2023 Aug; 88():102850. PubMed ID: 37263108
[TBL] [Abstract][Full Text] [Related]
9. Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI.
Al-Masni MA; Lee S; Yi J; Kim S; Gho SM; Choi YH; Kim DH
Neuroimage; 2022 Oct; 259():119411. PubMed ID: 35753594
[TBL] [Abstract][Full Text] [Related]
10. Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN.
Safari M; Yang X; Chang CW; Qiu RLJ; Fatemi A; Archambault L
Phys Med Biol; 2024 May; 69(11):. PubMed ID: 38714192
[No Abstract] [Full Text] [Related]
11. Phase2Phase: Respiratory Motion-Resolved Reconstruction of Free-Breathing Magnetic Resonance Imaging Using Deep Learning Without a Ground Truth for Improved Liver Imaging.
Eldeniz C; Gan W; Chen S; Fraum TJ; Ludwig DR; Yan Y; Liu J; Vahle T; Krishnamurthy U; Kamilov US; An H
Invest Radiol; 2021 Dec; 56(12):809-819. PubMed ID: 34038064
[TBL] [Abstract][Full Text] [Related]
12. Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease.
Hauptmann A; Arridge S; Lucka F; Muthurangu V; Steeden JA
Magn Reson Med; 2019 Feb; 81(2):1143-1156. PubMed ID: 30194880
[TBL] [Abstract][Full Text] [Related]
13. Metal artifact reduction for practical dental computed tomography by improving interpolation-based reconstruction with deep learning.
Liang K; Zhang L; Yang H; Yang Y; Chen Z; Xing Y
Med Phys; 2019 Dec; 46(12):e823-e834. PubMed ID: 31811792
[TBL] [Abstract][Full Text] [Related]
14. Navigator-based reacquisition and estimation of motion-corrupted data: Application to multi-echo spin echo for carotid wall MRI.
Frost R; Biasiolli L; Li L; Hurst K; Alkhalil M; Choudhury RP; Robson MD; Hess AT; Jezzard P
Magn Reson Med; 2020 Jun; 83(6):2026-2041. PubMed ID: 31697862
[TBL] [Abstract][Full Text] [Related]
15. Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis.
Cui L; Song Y; Wang Y; Wang R; Wu D; Xie H; Li J; Yang G
PLoS One; 2023; 18(1):e0278668. PubMed ID: 36603007
[TBL] [Abstract][Full Text] [Related]
16. A knowledge interaction learning for multi-echo MRI motion artifact correction towards better enhancement of SWI.
Al-Masni MA; Lee S; Al-Shamiri AK; Gho SM; Choi YH; Kim DH
Comput Biol Med; 2023 Feb; 153():106553. PubMed ID: 36641933
[TBL] [Abstract][Full Text] [Related]
17. Learning-based motion artifact removal networks for quantitative
Xu X; Kothapalli SVVN; Liu J; Kahali S; Gan W; Yablonskiy DA; Kamilov US
Magn Reson Med; 2022 Jul; 88(1):106-119. PubMed ID: 35257400
[TBL] [Abstract][Full Text] [Related]
18. Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks.
Lee D; Yoo J; Tak S; Ye JC
IEEE Trans Biomed Eng; 2018 Sep; 65(9):1985-1995. PubMed ID: 29993390
[TBL] [Abstract][Full Text] [Related]
19. Image-based motion artifact reduction on liver dynamic contrast enhanced MRI.
Wu Y; Liu J; White GM; Deng J
Phys Med; 2023 Jan; 105():102509. PubMed ID: 36565556
[TBL] [Abstract][Full Text] [Related]
20. Calibrationless reconstruction of uniformly-undersampled multi-channel MR data with deep learning estimated ESPIRiT maps.
Zhang J; Yi Z; Zhao Y; Xiao L; Hu J; Man C; Lau V; Su S; Chen F; Leong ATL; Wu EX
Magn Reson Med; 2023 Jul; 90(1):280-294. PubMed ID: 37119514
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
