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Mark S Graham

Showing results (1-10 of 41) with videos related to

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Information Processing in Medical Imaging : Proceedings of the ... Conference|July 30, 2015
A Simulation Framework for Quantitative Validation of Artefact Correction in Diffusion MRIMark S Graham, Ivana Drobnjak, Hui Zhang
Neuroimage|June 9, 2018
A supervised learning approach for diffusion MRI quality control with minimal training dataMark S Graham, Ivana Drobnjak, Hui Zhang
Neuroimage|November 10, 2015
Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniquesMark S Graham, Ivana Drobnjak, Hui Zhang
Plos One|October 3, 2017
Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRIMark S Graham, Ivana Drobnjak, Mark Jenkinson, et al.
Neuroimage|July 10, 2016
Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR imagesJesper L R Andersson, Mark S Graham, Enikő Zsoldos, et al.
Translational Vision Science & Technology|May 19, 2021
Evaluation of Automated Multiclass Fluid Segmentation in Optical Coherence Tomography Images Using the Pegasus Fluid Segmentation AlgorithmsLouise Terry, Sameer Trikha, Kanwal K Bhatia, et al.
Journal of Wildlife Diseases|January 29, 2011
The role of predation in disease control: a comparison of selective and nonselective removal on prion disease dynamics in deerMargaret A Wild, N Thompson Hobbs, Mark S Graham, et al.
Neuroimage|December 27, 2017
Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional dataJesper L R Andersson, Mark S Graham, Ivana Drobnjak, et al.
Neuroimage|March 13, 2017
Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movementJesper L R Andersson, Mark S Graham, Ivana Drobnjak, et al.
Retina (Philadelphia, Pa.)|October 5, 2019
DISEASE CLASSIFICATION OF MACULAR OPTICAL COHERENCE TOMOGRAPHY SCANS USING DEEP LEARNING SOFTWARE: Validation on Independent, Multicenter DataKanwal K Bhatia, Mark S Graham, Louise Terry, et al.
Pageof 5

Showing results (1-10 of 41) with videos related to

Sort By:
Pageof 5
Information Processing in Medical Imaging : Proceedings of the ... Conference|July 30, 2015
A Simulation Framework for Quantitative Validation of Artefact Correction in Diffusion MRIMark S Graham, Ivana Drobnjak, Hui Zhang
Neuroimage|June 9, 2018
A supervised learning approach for diffusion MRI quality control with minimal training dataMark S Graham, Ivana Drobnjak, Hui Zhang
Neuroimage|November 10, 2015
Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniquesMark S Graham, Ivana Drobnjak, Hui Zhang
Plos One|October 3, 2017
Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRIMark S Graham, Ivana Drobnjak, Mark Jenkinson, et al.
Neuroimage|July 10, 2016
Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR imagesJesper L R Andersson, Mark S Graham, Enikő Zsoldos, et al.
Translational Vision Science & Technology|May 19, 2021
Evaluation of Automated Multiclass Fluid Segmentation in Optical Coherence Tomography Images Using the Pegasus Fluid Segmentation AlgorithmsLouise Terry, Sameer Trikha, Kanwal K Bhatia, et al.
Journal of Wildlife Diseases|January 29, 2011
The role of predation in disease control: a comparison of selective and nonselective removal on prion disease dynamics in deerMargaret A Wild, N Thompson Hobbs, Mark S Graham, et al.
Neuroimage|December 27, 2017
Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional dataJesper L R Andersson, Mark S Graham, Ivana Drobnjak, et al.
Neuroimage|March 13, 2017
Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movementJesper L R Andersson, Mark S Graham, Ivana Drobnjak, et al.
Retina (Philadelphia, Pa.)|October 5, 2019
DISEASE CLASSIFICATION OF MACULAR OPTICAL COHERENCE TOMOGRAPHY SCANS USING DEEP LEARNING SOFTWARE: Validation on Independent, Multicenter DataKanwal K Bhatia, Mark S Graham, Louise Terry, et al.
Pageof 5