139 related articles for article (PubMed ID: 31333440)
1. An Automatic Estimation of Arterial Input Function Based on Multi-Stream 3D CNN.
Fan S; Bian Y; Wang E; Kang Y; Wang DJJ; Yang Q; Ji X
Front Neuroinform; 2019; 13():49. PubMed ID: 31333440
[TBL] [Abstract][Full Text] [Related]
2. Core and penumbra estimation using deep learning-based AIF in association with clinical measures in computed tomography perfusion (CTP).
Bal SS; Yang FG; Chi NF; Yin JH; Wang TJ; Peng GS; Chen K; Hsu CC; Chen CI
Insights Imaging; 2023 Sep; 14(1):161. PubMed ID: 37775600
[TBL] [Abstract][Full Text] [Related]
3. Automatic arterial input function selection in CT and MR perfusion datasets using deep convolutional neural networks.
Winder A; d'Esterre CD; Menon BK; Fiehler J; Forkert ND
Med Phys; 2020 Sep; 47(9):4199-4211. PubMed ID: 32583617
[TBL] [Abstract][Full Text] [Related]
4. Penumbra quantification from MR SWI-DWI mismatch and its comparison with MR ASL PWI-DWI mismatch in patients with acute ischemic stroke.
Bhattacharjee R; Gupta RK; Das B; Dixit VK; Gupta P; Singh A
NMR Biomed; 2021 Jul; 34(7):e4526. PubMed ID: 33880799
[TBL] [Abstract][Full Text] [Related]
5. Arterial input function segmentation based on a contour geodesic model for tissue at risk identification in ischemic stroke.
Bal SS; Chen K; Yang FG; Peng GS
Med Phys; 2022 Apr; 49(4):2475-2485. PubMed ID: 35098544
[TBL] [Abstract][Full Text] [Related]
6. Effect of the arterial input function on the measured perfusion values and infarct volumetric in acute cerebral ischemia evaluated by perfusion computed tomography.
Bisdas S; Konstantinou GN; Gurung J; Lehnert T; Donnerstag F; Becker H; Vogl TJ; Koh TS
Invest Radiol; 2007 Mar; 42(3):147-56. PubMed ID: 17287644
[TBL] [Abstract][Full Text] [Related]
7. Blind deconvolution estimation of an arterial input function for small animal DCE-MRI.
Jiřík R; Taxt T; Macíček O; Bartoš M; Kratochvíla J; Souček K; Dražanová E; Krátká L; Hampl A; Starčuk Z
Magn Reson Imaging; 2019 Oct; 62():46-56. PubMed ID: 31150814
[TBL] [Abstract][Full Text] [Related]
8. An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors.
Rahimzadeh H; Fathi Kazerooni A; Deevband MR; Saligheh Rad H
J Biomed Phys Eng; 2019 Feb; 9(1):69-80. PubMed ID: 30881936
[TBL] [Abstract][Full Text] [Related]
9. Automatic measurements of arterial input and venous output functions on cerebral computed tomography perfusion images: a preliminary study.
Kao YH; Mu Huo Teng M; Kao YT; Chen YJ; Wu CH; Chen WC; Chiu FY; Chang FC
Comput Biol Med; 2014 Aug; 51():51-60. PubMed ID: 24880995
[TBL] [Abstract][Full Text] [Related]
10. Multi-stage automated local arterial input function selection in perfusion MRI.
Tabbara R; Connelly A; Calamante F
MAGMA; 2020 Jun; 33(3):357-365. PubMed ID: 31722036
[TBL] [Abstract][Full Text] [Related]
11. Optimal Scaling Approaches for Perfusion MRI with Distorted Arterial Input Function (AIF) in Patients with Ischemic Stroke.
Bal SS; Yang FPG; Sung YF; Chen K; Yin JH; Peng GS
Brain Sci; 2022 Jan; 12(1):. PubMed ID: 35053820
[TBL] [Abstract][Full Text] [Related]
12. Influence of the arterial input function on absolute and relative perfusion-weighted imaging penumbral flow detection: a validation with ¹⁵O-water positron emission tomography.
Zaro-Weber O; Moeller-Hartmann W; Heiss WD; Sobesky J
Stroke; 2012 Feb; 43(2):378-85. PubMed ID: 22135071
[TBL] [Abstract][Full Text] [Related]
13. Quantification of Collateral Supply with Local-AIF Dynamic Susceptibility Contrast MRI Predicts Infarct Growth.
Liu MM; Saadat N; Roth SP; Niekrasz MA; Giurcanu M; Carroll TJ; Christoforidis GA
ArXiv; 2024 Jun; ():. PubMed ID: 38883243
[TBL] [Abstract][Full Text] [Related]
14. Toward fully automated processing of dynamic susceptibility contrast perfusion MRI for acute ischemic cerebral stroke.
Kim J; Leira EC; Callison RC; Ludwig B; Moritani T; Magnotta VA; Madsen MT
Comput Methods Programs Biomed; 2010 May; 98(2):204-13. PubMed ID: 20060614
[TBL] [Abstract][Full Text] [Related]
15. Automated detection of the arterial input function using normalized cut clustering to determine cerebral perfusion by dynamic susceptibility contrast-magnetic resonance imaging.
Yin J; Sun H; Yang J; Guo Q
J Magn Reson Imaging; 2015 Apr; 41(4):1071-8. PubMed ID: 24753102
[TBL] [Abstract][Full Text] [Related]
16. Automatic selection of arterial input function on dynamic contrast-enhanced MR images.
Peruzzo D; Bertoldo A; Zanderigo F; Cobelli C
Comput Methods Programs Biomed; 2011 Dec; 104(3):e148-57. PubMed ID: 21458099
[TBL] [Abstract][Full Text] [Related]
17. Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15,000 patients.
Xue H; Tseng E; Knott KD; Kotecha T; Brown L; Plein S; Fontana M; Moon JC; Kellman P
Magn Reson Med; 2020 Nov; 84(5):2788-2800. PubMed ID: 32378776
[TBL] [Abstract][Full Text] [Related]
18. AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning.
de la Rosa E; Sima DM; Menze B; Kirschke JS; Robben D
Med Image Anal; 2021 Dec; 74():102211. PubMed ID: 34425318
[TBL] [Abstract][Full Text] [Related]
19. Reproducibility and Optimal Arterial Input Function Selection in Dynamic Contrast-Enhanced Perfusion MRI in the Healthy Brain.
Cramer SP; Larsson HBW; Knudsen MH; Simonsen HJ; Vestergaard MB; Lindberg U
J Magn Reson Imaging; 2023 Apr; 57(4):1229-1240. PubMed ID: 35993510
[TBL] [Abstract][Full Text] [Related]
20. Comparison of computed tomography perfusion and magnetic resonance imaging perfusion-diffusion mismatch in ischemic stroke.
Campbell BC; Christensen S; Levi CR; Desmond PM; Donnan GA; Davis SM; Parsons MW
Stroke; 2012 Oct; 43(10):2648-53. PubMed ID: 22858726
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]