Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Garyfallidis P"'
Autor:
Newlin, Nancy R., Schilling, Kurt, Koudoro, Serge, Chandio, Bramsh Qamar, Kanakaraj, Praitayini, Moyer, Daniel, Kelly, Claire E., Genc, Sila, Chen, Jian, Yang, Joseph Yuan-Mou, Wu, Ye, He, Yifei, Zhang, Jiawei, Zeng, Qingrun, Zhang, Fan, Adluru, Nagesh, Nath, Vishwesh, Pathak, Sudhir, Schneider, Walter, Gade, Anurag, Rathi, Yogesh, Hendriks, Tom, Vilanova, Anna, Chamberland, Maxime, Pieciak, Tomasz, Ciupek, Dominika, Vega, Antonio Tristán, Aja-Fernández, Santiago, Malawski, Maciej, Ouedraogo, Gani, Machnio, Julia, Ewert, Christian, Thompson, Paul M., Jahanshad, Neda, Garyfallidis, Eleftherios, Landman, Bennett A.
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructu
Externí odkaz:
http://arxiv.org/abs/2411.09618
Autor:
Taskin, Ekin, Haro, Juan Luis Villarreal, Girard, Gabriel, Rafael-Patiño, Jonathan, Garyfallidis, Eleftherios, Thiran, Jean-Philippe, Canales-Rodríguez, Erick Jorge
Constrained Spherical Deconvolution (CSD) is crucial for estimating white matter fiber orientations using diffusion MRI data. A relevant parameter in CSD is the maximum order $l_{max}$ used in the spherical harmonics series, influencing the angular r
Externí odkaz:
http://arxiv.org/abs/2408.12921
Autor:
Hayashi, Soichi, Caron, Bradley A., Heinsfeld, Anibal Sólon, Vinci-Booher, Sophia, McPherson, Brent, Bullock, Daniel N., Bertò, Giulia, Niso, Guiomar, Hanekamp, Sandra, Levitas, Daniel, Ray, Kimberly, MacKenzie, Anne, Kitchell, Lindsey, Leong, Josiah K., Nascimento-Silva, Filipi, Koudoro, Serge, Willis, Hanna, Jolly, Jasleen K., Pisner, Derek, Zuidema, Taylor R., Kurzawski, Jan W., Mikellidou, Kyriaki, Bussalb, Aurore, Rorden, Christopher, Victory, Conner, Bhatia, Dheeraj, Aydogan, Dogu Baran, Yeh, Fang-Cheng F., Delogu, Franco, Guaje, Javier, Veraart, Jelle, Bollman, Steffen, Stewart, Ashley, Fischer, Jeremy, Faskowitz, Joshua, Chaumon, Maximilien, Fabrega, Ricardo, Hunt, David, McKee, Shawn, Brown, Shawn T., Heyman, Stephanie, Iacovella, Vittorio, Mejia, Amanda F., Marinazzo, Daniele, Craddock, R. Cameron, Olivetti, Emanuele, Hanson, Jamie L., Avesani, Paolo, Garyfallidis, Eleftherios, Stanzione, Dan, Carson, James, Henschel, Robert, Hancock, David Y., Stewart, Craig A., Schnyer, David, Eke, Damian O., Poldrack, Russell A., George, Nathalie, Bridge, Holly, Sani, Ilaria, Freiwald, Winrich A., Puce, Aina, Port, Nicholas L., Pestilli, Franco
Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR
Externí odkaz:
http://arxiv.org/abs/2306.02183
Brain extraction is one of the first steps of pre-processing 3D brain MRI data and a prerequisite for any forthcoming brain imaging analyses. However, it is not a simple segmentation problem due to the complex structure of the brain and human head. A
Externí odkaz:
http://arxiv.org/abs/2206.02837
Diffusion MRI (dMRI) is the only non-invasive technique sensitive to tissue micro-architecture, which can, in turn, be used to reconstruct tissue microstructure and white matter pathways. The accuracy of such tasks is hampered by the low signal-to-no
Externí odkaz:
http://arxiv.org/abs/2203.01921
Publikováno v:
Communications Medicine, Vol 4, Iss 1, Pp 1-12 (2024)
Abstract Background Brain extraction is a computational necessity for researchers using brain imaging data. However, the complex structure of the interfaces between the brain, meninges and human skull have not allowed a highly robust solution to emer
Externí odkaz:
https://doaj.org/article/29cf9772ed43402e8356225280ced95f
Autor:
Garyfallidis, Eleftherios, Fadnavis, Shreyas, Park, Jong Sung, Chandio, Bramsh Qamar, Guaje, Javier, Koudoro, Serge, Anousheh, Nasim
Clustering is a fundamental problem in machine learning where distance-based approaches have dominated the field for many decades. This set of problems is often tackled by partitioning the data into K clusters where the number of clusters is chosen a
Externí odkaz:
http://arxiv.org/abs/2102.07028
Publikováno v:
Thirty-fourth Conference on Neural Information Processing Systems, 2020
Diffusion-weighted magnetic resonance imaging (DWI) is the only noninvasive method for quantifying microstructure and reconstructing white-matter pathways in the living human brain. Fluctuations from multiple sources create significant additive noise
Externí odkaz:
http://arxiv.org/abs/2011.01355
Autor:
Fadnavis, Shreyas, Farooq, Hamza, Afzali, Maryam, Lenglet, Christoph, Georgiou, Tryphon, Cheng, Hu, Newman, Sharlene, Ahmed, Shahnawaz, Henriques, Rafael Neto, Peterson, Eric, Koudoro, Serge, Rokem, Ariel, Garyfallidis, Eleftherios
Fitting multi-exponential models to Diffusion MRI (dMRI) data has always been challenging due to various underlying complexities. In this work, we introduce a novel and robust fitting framework for the standard two-compartment IVIM microstructural mo
Externí odkaz:
http://arxiv.org/abs/1910.00095
Autor:
Kurt G. Schilling, Shreyas Fadnavis, Joshua Batson, Mereze Visagie, Anna J.E. Combes, Samantha By, Colin D. McKnight, Francesca Bagnato, Eleftherios Garyfallidis, Bennett A. Landman, Seth A. Smith, Kristin P. O'Grady
Publikováno v:
NeuroImage, Vol 266, Iss , Pp 119826- (2023)
Quantitative diffusion MRI (dMRI) is a promising technique for evaluating the spinal cord in health and disease. However, low signal-to-noise ratio (SNR) can impede interpretation and quantification of these images. The purpose of this study is to ev
Externí odkaz:
https://doaj.org/article/f699cd7d04f54fd482bf0ae092a473cc