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Neuroimage
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March 1, 2016
Removing inter-subject technical variability in magnetic resonance imaging studies
Jean-Philippe Fortin, Elizabeth M Sweeney, John Muschelli, et al.
Clinical Imaging
|
October 1, 2021
Diagnostic accuracy of shuttle CT angiography (CTA) and helical CTA in the diagnosis of vasospasm
Natasha M Smith, Elizabeth M Sweeney, Ajay Gupta, et al.
Statistics in Medicine
|
May 6, 2015
Scan-stratified case-control sampling for modeling blood-brain barrier integrity in multiple sclerosis
Gina-Maria Pomann, Elizabeth M Sweeney, Daniel S Reich, et al.
Neuroimage. Clinical
|
March 10, 2017
PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT
John Muschelli, Elizabeth M Sweeney, Natalie L Ullman, et al.
Journal of Neuroimaging : Official Journal of the American Society of Neuroimaging
|
March 9, 2022
Quantitative susceptibility mapping versus phase imaging to identify multiple sclerosis iron rim lesions with demyelination
Weiyuan Huang, Elizabeth M Sweeney, Ulrike W Kaunzner, et al.
Neuroimage. Clinical
|
August 24, 2016
PREVAIL: Predicting Recovery through Estimation and Visualization of Active and Incident Lesions
Jordan D Dworkin, Elizabeth M Sweeney, Matthew K Schindler, et al.
Pharmacoepidemiology and Drug Safety
|
July 6, 2021
Differential frequency in imaging-based outcome measurement: Bias in real-world oncology comparative-effectiveness studies
Blythe J S Adamson, Xinran Ma, Sandra D Griffith, et al.
Medicine
|
September 24, 2021
Predictors of acute deep venous thrombosis in patients hospitalized for COVID-19
Sadjad Riyahi, Stefanie J Hectors, Martin R Prince, et al.
Multiple Sclerosis (Houndmills, Basingstoke, England)
|
January 16, 2016
Clinical 3-tesla FLAIR* MRI improves diagnostic accuracy in multiple sclerosis
Ilena C George, Pascal Sati, Martina Absinta, et al.
Neuroimage. Clinical
|
March 5, 2022
QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps
Hang Zhang, Thanh D Nguyen, Jinwei Zhang, et al.
Page
of 4
Search research articles
Search
Showing results (1-10 of 34) with videos related to
Sort By:
Page
of 4
Neuroimage
|
March 1, 2016
Removing inter-subject technical variability in magnetic resonance imaging studies
Jean-Philippe Fortin, Elizabeth M Sweeney, John Muschelli, et al.
Clinical Imaging
|
October 1, 2021
Diagnostic accuracy of shuttle CT angiography (CTA) and helical CTA in the diagnosis of vasospasm
Natasha M Smith, Elizabeth M Sweeney, Ajay Gupta, et al.
Statistics in Medicine
|
May 6, 2015
Scan-stratified case-control sampling for modeling blood-brain barrier integrity in multiple sclerosis
Gina-Maria Pomann, Elizabeth M Sweeney, Daniel S Reich, et al.
Neuroimage. Clinical
|
March 10, 2017
PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT
John Muschelli, Elizabeth M Sweeney, Natalie L Ullman, et al.
Journal of Neuroimaging : Official Journal of the American Society of Neuroimaging
|
March 9, 2022
Quantitative susceptibility mapping versus phase imaging to identify multiple sclerosis iron rim lesions with demyelination
Weiyuan Huang, Elizabeth M Sweeney, Ulrike W Kaunzner, et al.
Neuroimage. Clinical
|
August 24, 2016
PREVAIL: Predicting Recovery through Estimation and Visualization of Active and Incident Lesions
Jordan D Dworkin, Elizabeth M Sweeney, Matthew K Schindler, et al.
Pharmacoepidemiology and Drug Safety
|
July 6, 2021
Differential frequency in imaging-based outcome measurement: Bias in real-world oncology comparative-effectiveness studies
Blythe J S Adamson, Xinran Ma, Sandra D Griffith, et al.
Medicine
|
September 24, 2021
Predictors of acute deep venous thrombosis in patients hospitalized for COVID-19
Sadjad Riyahi, Stefanie J Hectors, Martin R Prince, et al.
Multiple Sclerosis (Houndmills, Basingstoke, England)
|
January 16, 2016
Clinical 3-tesla FLAIR* MRI improves diagnostic accuracy in multiple sclerosis
Ilena C George, Pascal Sati, Martina Absinta, et al.
Neuroimage. Clinical
|
March 5, 2022
QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps
Hang Zhang, Thanh D Nguyen, Jinwei Zhang, et al.
Page
of 4