Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/wiley/estimation-of-the-capillary-level-input-function-for-dynamic-contrast-xj70JgBVOg
References (45)
-
M. Narvaez, Silvia Ruiz-España, E. Arana, D. Moratal (2015)
Automatic detection of local arterial input functions through Independent Component Analysis on Dynamic Contrast enhanced Magnetic Resonance Imaging2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
-
S. Chen, Y. Tyan, Jui-Jen Lai, Chin-Ching Chang (2016)
Automated Determination of Arterial Input Function for Dynamic Susceptibility Contrast MRI from Regions around Arteries Using Independent Component AnalysisRadiology Research and Practice, 2016
-
Chong Duan, J. Kallehauge, C. Pérez-Torres, C. Pérez-Torres, G. Bretthorst, S. Beeman, Kari Tanderup, Kari Tanderup, J. Ackerman, J. Garbow (2018)
Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIFMolecular Imaging and Biology, 20
-
Anthony Winder, C. d'Esterre, B. Menon, J. Fiehler, N. Forkert (2020)
Automatic arterial input function selection in CT and MR perfusion datasets using deep convolutional neural networks.Medical physics
-
R. Sanz-Requena, J. Prats-Montalbán, L. Martí-Bonmatí, Á. Alberich-Bayarri, G. García-Martí, R. Pérez, A. Ferrer (2015)
Automatic individual arterial input functions calculated from PCA outperform manual and population‐averaged approaches for the pharmacokinetic modeling of DCE‐MR imagesJournal of Magnetic Resonance Imaging, 42
-
Shengyu Fan, Yueyan Bian, Erling Wang, Yan Kang, Danny Wang, Qi Yang, X. Ji (2019)
An Automatic Estimation of Arterial Input Function Based on Multi-Stream 3D CNNFrontiers in Neuroinformatics, 13
-
S. Nejad-Davarani, H. Bagher-Ebadian, J. Ewing, D. Noll, T. Mikkelsen, M. Chopp, Q. Jiang (2017)
An extended vascular model for less biased estimation of permeability parameters in DCE‐T1 imagesNMR in Biomedicine, 30
-
S. Abdullah, J. Pialat, M. Wiart, F. Duboeuf, J. Mabrut, B. Bancel, A. Rode, C. Ducerf, J. Baulieux, Y. Berthezène (2008)
Characterization of hepatocellular carcinoma and colorectal liver metastasis by means of perfusion MRIJournal of Magnetic Resonance Imaging, 28
-
D. Kovář, M. Lewis, G. Karczmar (1998)
A new method for imaging perfusion and contrast extraction fraction: Input functions derived from reference tissuesJournal of Magnetic Resonance Imaging, 8
-
F. Calamante, Morten Mørup, L. Hansen (2004)
Defining a local arterial input function for perfusion MRI using independent component analysisMagnetic Resonance in Medicine, 52
-
Yang Zhang, J. Chen, Kai-Ting Chang, V. Park, Min Kim, Siwa Chan, P. Chang, D. Chow, A. Luk, Tiffany Kwong, M. Su (2019)
Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.Academic radiology
-
S. Kim, M. Freed, Ana Leite, Jin Zhang, C. Seuss, L. Moy (2017)
Separation of benign and malignant breast lesions using dynamic contrast enhanced MRI in a biopsy cohortJournal of Magnetic Resonance Imaging, 45
-
K. Mouridsen, S. Christensen, Louise Gyldensted, L. Østergaard (2006)
Automatic selection of arterial input function using cluster analysisMagnetic Resonance in Medicine, 55
-
D. Posada, T. Buckley (2004)
Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests.Systematic biology, 53 5
-
J. O’Connor, A. Jackson, Geoff Parker, G. Jayson (2007)
DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agentsBritish Journal of Cancer, 96
-
Geoff Parker, C. Roberts, A. MacDonald, G. Buonaccorsi, S. Cheung, D. Buckley, A. Jackson, Y. Watson, K. Davies, G. Jayson (2006)
Experimentally‐derived functional form for a population‐averaged high‐temporal‐resolution arterial input function for dynamic contrast‐enhanced MRIMagnetic Resonance in Medicine, 56
-
A. Oto, Cheng Yang, A. Kayhan, M. Tretiakova, T. Antic, C. Schmid-Tannwald, S. Eggener, G. Karczmar, W. Stadler (2011)
Diffusion-weighted and dynamic contrast-enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis.AJR. American journal of roentgenology, 197 6
-
Yingxuan Zhu, Ming-Ching Chang, Sandeep Gupta (2011)
Automated determination of arterial input function for DCE-MRI of the prostate, 7963
-
T. Yankeelov, J. Luci, M. Lepage, Rui Li, L. Debusk, P. Lin, Ronald Price, John Gore (2005)
Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model.Magnetic resonance imaging, 23 4
-
D. Peruzzo, A. Bertoldo, F. Zanderigo, C. Cobelli (2011)
Automatic selection of arterial input function on dynamic contrast-enhanced MR imagesComputer methods and programs in biomedicine, 104 3
-
T. Koopman, R. Martens, C. Lavini, M. Yaqub, J. Castelijns, R. Boellaard, J. Marcus (2020)
Repeatability of arterial input functions and kinetic parameters in muscle obtained by dynamic contrast enhanced MR imaging of the head and neck.Magnetic resonance imaging
-
A. Oto, A. Kayhan, Yulei Jiang, M. Tretiakova, Cheng Yang, T. Antic, F. Dahi, A. Shalhav, G. Karczmar, W. Stadler (2010)
Prostate cancer: differentiation of central gland cancer from benign prostatic hyperplasia by using diffusion-weighted and dynamic contrast-enhanced MR imaging.Radiology, 257 3
-
Li Feng, Qiuting Wen, Chenchan Huang, A. Tong, Fang Liu, H. Chandarana (2019)
GRASP‐Pro: imProving GRASP DCE‐MRI through self‐calibrating subspace‐modeling and contrast phase automationMagnetic Resonance in Medicine, 83
-
J. Drouin-Ouellet, S. Sawiak, G. Cisbani, Marie Lagacé, Wei-Li Kuan, M. Saint‐Pierre, R. Dury, Wael Alata, I. St-Amour, S. Mason, F. Calon, S. Lacroix, P. Gowland, S. Francis, R. Barker, F. Cicchetti (2015)
Cerebrovascular and blood–brain barrier impairments in Huntington's disease: Potential implications for its pathophysiologyAnnals of Neurology, 78
-
M. Greer, J. Shih, Nathan Lay, T. Barrett, Leonardo Bittencourt, S. Borofsky, I. Kabakus, Y. Law, Jamie Marko, H. Shebel, Francesca Mertan, M. Merino, B. Wood, P. Pinto, R. Summers, P. Choyke, B. Turkbey (2017)
Validation of the Dominant Sequence Paradigm and Role of Dynamic Contrast-enhanced Imaging in PI-RADS Version 2.Radiology, 285 3
-
Kazuki Shimada, T. Nagasaka, M. Shidahara, Y. Machida, H. Tamura (2012)
In vivo measurement of longitudinal relaxation time of human blood by inversion-recovery fast gradient-echo MR imaging at 3T.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 11 4
-
Ibnul Mia, Melanie Le, C. Arendt, D. Brand, Sina Bremekamp, T. D'angelo, V. Puntmann, E. Nagel (2020)
Quantitative perfusion-CMR is significantly influenced by the placement of the arterial input functionThe International Journal of Cardiovascular Imaging, 37
-
A. Padhani, C. Hayes, S. Landau, M. Leach (2002)
Reproducibility of quantitative dynamic MRI of normal human tissuesNMR in Biomedicine, 15
-
P. Tofts, G. Brix, D. Buckley, J. Evelhoch, E. Henderson, M. Knopp, H. Larsson, Ting-Yim Lee, N. Mayr, G. Parker, R. Port, June Taylor, R. Weisskoff (1999)
Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbolsJournal of Magnetic Resonance Imaging, 10
-
G. Parker, A. Jackson, J. Waterton, D. Buckley (2003)
Automated Arterial Input Function Extraction for T1-Weighted DCE-MRI -
Chenyi Zeng, Lin Gu, Zhenzhong Liu, Shen Zhao (2020)
Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRIFrontiers in Neuroinformatics, 14
-
Mariko Goto, K. Sakai, H. Yokota, M. Kiba, Mariko Yoshida, H. Imai, E. Weiland, I. Yokota, Kei Yamada (2018)
Diagnostic performance of initial enhancement analysis using ultra-fast dynamic contrast-enhanced MRI for breast lesionsEuropean Radiology, 29
-
Wei Huang, Yiyi Chen, Andrey Fedorov, Xia Li, G. Jajamovich, D. Malyarenko, M. Aryal, P. LaViolette, Matthew Oborski, F. O’Sullivan, R. Abramson, K. Jafari-Khouzani, Aneela Afzal, A. Tudorica, Brendan Moloney, Sandeep Gupta, C. Besa, Jayashree Kalpathy-Cramer, J. Mountz, C. Laymon, M. Muzi, Paul Kinahan, K. Schmainda, Yue Cao, T. Chenevert, B. Taouli, T. Yankeelov, F. Fennessy, Xin Li (2016)
The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis ChallengeTomography, 2
-
Kaiming He, X. Zhang, Shaoqing Ren, Jian Sun (2015)
Deep Residual Learning for Image Recognition2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
-
S. Sourbron, D. Buckley (2012)
Tracer kinetic modelling in MRI: estimating perfusion and capillary permeabilityPhysics in Medicine and Biology, 57
-
J. Fessler, B. Sutton (2003)
Nonuniform fast Fourier transforms using min-max interpolationIEEE Trans. Signal Process., 51
-
Cory Lorenz, T. Benner, P. Chen, C. Lopez, H. Ay, M. Zhu, N. Menezes, H. Aronen, J. Karonen, Yawu Liu, J. Nuutinen, A. Sorensen (2006)
Automated perfusion‐weighted MRI using localized arterial input functionsJournal of Magnetic Resonance Imaging, 24
-
F. Ziayee, A. Müller-Lutz, J. Gross, M. Quentin, T. Ullrich, P. Heusch, C. Arsov, R. Rabenalt, P. Albers, G. Antoch, H. Wittsack, L. Schimmöller (2018)
Influence of arterial input function (AIF) on quantitative prostate dynamic contrast-enhanced (DCE) MRI and zonal prostate anatomy.Magnetic resonance imaging, 53
-
S. Nejad-Davarani, H. Bagher-Ebadian, J. Ewing, D. Noll, T. Mikkelsen, M. Chopp, Q. Jiang (2017)
A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue levelNMR in Biomedicine, 30
-
Y. Benjamini, Y. Hochberg (1995)
Controlling the false discovery rate: a practical and powerful approach to multiple testingJournal of the royal statistical society series b-methodological, 57
-
B. Taouli, R. Johnson, Cristina Hajdu, Marcel Oei, M. Merad, H. Yee, H. Rusinek (2013)
Hepatocellular carcinoma: perfusion quantification with dynamic contrast-enhanced MRI.AJR. American journal of roentgenology, 201 4
-
R. Khouli, K. Macura, M. Jacobs, Tarek Khalil, I. Kamel, A. Dwyer, D. Bluemke (2009)
Dynamic contrast-enhanced MRI of the breast: quantitative method for kinetic curve type assessment.AJR. American journal of roentgenology, 193 4
-
L. Heacock, Amy Melsaether, S. Heller, Yiming Gao, Kristine Pysarenko, J. Babb, S. Kim, L. Moy (2016)
Evaluation of a known breast cancer using an abbreviated breast MRI protocol: Correlation of imaging characteristics and pathology with lesion detection and conspicuity.European journal of radiology, 85 4
-
X. Wu, Guirong Liu (2007)
Application of independent component analysis to dynamic contrast-enhanced imaging for assessment of cerebral blood perfusionMedical image analysis, 11 3
-
L. Leong, E. Gombos, J. Jagadeesan, S. Fook-Chong (2015)
MRI kinetics with volumetric analysis in correlation with hormonal receptor subtypes and histologic grade of invasive breast cancers.AJR. American journal of roentgenology, 204 3
- Publisher
- Wiley
- Copyright
- © 2022 International Society for Magnetic Resonance in Medicine
- ISSN
- 0740-3194
- eISSN
- 1522-2594
- DOI
- 10.1002/mrm.29148
- Publisher site
- See Article on Publisher Site
Abstract
Journal
Magnetic Resonance in Medicine – Wiley
Published: May 1, 2022
Keywords: arterial input function; breast cancer; capillary input function; deep learning; dynamic contrast enhanced MRI