A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging

John K. Berry · NeuroImage · 2015

Summary

This work proposes a novel acquisition and reconstruction scheme for obtaining high spatial resolution dMRI images using multiple low resolution (LR) images, which is effective in reducing acquisition time while improving the signal-to-noise ratio (SNR).

Type: article
Published: 2015-10-23
Cited by: 72
References: 62
Access: Open access
DOI: https://doi.org/10.1016/j.neuroimage.2015.10.061
PubMed: https://pubmed.ncbi.nlm.nih.gov/26505296/
PMC: https://www.ncbi.nlm.nih.gov/pmc/articles/4691422/
OpenAlex: https://openalex.org/W26505296
Semantic Scholar: https://api.semanticscholar.org/CorpusID:20032569

Keywords

Industrial relations, Process (computing), Field (mathematics), Power (physics), Public relations

References

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Investigation into Deep Brain Stimulation Lead Designs: A Patient-Specific Simulation StudyFabiola Alonso, Malcolm Latorre, Nathanael Göransson — 2016
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Efficient Global Spatial-Angular Sparse Coding for Diffusion MRI with Separable DictionariesEvan Schwab, René Víctor Valqui Vidal, Nicolas Charon — 2016
A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume from Orthogonal Anisotropic Resolution ScansYuanyuan Jia, Ali Gholipour, Zhongshi He — 2017
Image formation in diffusion MRI: A review of recent technical developmentsWenchuan Wu, Karla L. Miller — 2017
Incorporation of Prior Knowledge of Signal Behavior Into the Reconstruction to Accelerate the Acquisition of Diffusion MRI DataJuan Abascal, Manuel Desco, Juan Parra‐Robles — 2018
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A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging

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