S
Sebastiano Barbieri
Researcher at University of New South Wales
Publications - 61
Citations - 1017
Sebastiano Barbieri is an academic researcher from University of New South Wales. The author has contributed to research in topics: Medicine & Diffusion MRI. The author has an hindex of 14, co-authored 49 publications receiving 678 citations. Previous affiliations of Sebastiano Barbieri include Simon Fraser University & University of Bern.
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Journal ArticleDOI
Diffusion-weighted imaging outside the brain: Consensus statement from an ISMRM-sponsored workshop.
Bachir Taouli,Ambros J. Beer,Thomas L. Chenevert,David J. Collins,Constance D. Lehman,Celso Matos,Anwar R. Padhani,Andrew B. Rosenkrantz,Amita Shukla-Dave,Eric E. Sigmund,Lawrence Tanenbaum,Harriet C. Thoeny,Isabelle Thomassin-Naggara,Sebastiano Barbieri,Idoia Corcuera-Solano,Matthew R. Orton,Savannah C. Partridge,Dow-Mu Koh +17 more
TL;DR: In this article, the authors summarize the most up-to-date information on DWI acquisition and clinical applications outside the brain, as discussed in an ISMRM-sponsored symposium held in April 2015.
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Impact of the calculation algorithm on biexponential fitting of diffusion-weighted MRI in upper abdominal organs
TL;DR: To compare the variability, precision, and accuracy of six different algorithms for computing intravoxel‐incoherent‐motion‐related parameters in upper abdominal organs, Levenberg–Marquardt, Trust‐Region, Fixed‐Dp, Segmented‐Unconstrained, Se segmented‐Constrained and Bayesian‐Probability are used.
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Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI
Sebastiano Barbieri,Oliver J. Gurney-Champion,Oliver J. Gurney-Champion,Remy Klaassen,Harriet C. Thoeny +4 more
TL;DR: In this article, a prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion-weighted MRI (DW-MRI) data and evaluates its performance.
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Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk
Sebastiano Barbieri,James Kemp,Oscar Perez-Concha,Sradha Kotwal,Martin Gallagher,Martin Gallagher,Angus Ritchie,Louisa Jorm +7 more
TL;DR: In this paper, the authors compared different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU) using publicly available electronic medical record data (MIMIC-III).
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Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients.
Oliver J. Gurney-Champion,Remy Klaassen,Martijn Froeling,Martijn Froeling,Sebastiano Barbieri,Jaap Stoker,Marc R. W. Engelbrecht,Johanna W. Wilmink,Marc G. Besselink,Arjan Bel,Hanneke W. M. van Laarhoven,Aart J. Nederveen +11 more
TL;DR: The best performing IVIM fit algorithm was IVM-Bayesian-lin, but an easier to implement least squares fit with fixed D* performs similarly in pancreatic cancer patients.