Premium
JEDI: J oint E stimation D iffusion I maging of macroscopic and microscopic tissue properties
Author(s) -
Frank Lawrence R.,
Zahneisen Benjamin,
Galinsky Vitaly L.
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28141
Subject(s) - anisotropy , diffusion mri , computer science , diffusion imaging , entropy (arrow of time) , sensitivity (control systems) , fractional anisotropy , anisotropic diffusion , white matter , statistical physics , tractography , brain tissue , physics , artificial intelligence , algorithm , nuclear magnetic resonance , optics , neuroscience , psychology , magnetic resonance imaging , medicine , quantum mechanics , electronic engineering , radiology , engineering
Purpose A new method for enhancing the sensitivity of diffusion MRI (dMRI) by combining the data from single (sPFG) and double (dPFG) pulsed field gradient experiments is presented. Methods This method uses our JESTER framework to combine microscopic anisotropy information from dFPG experiments using a new method called diffusion tensor subspace imaging (DiTSI) to augment the macroscopic anisotropy information from sPFG data analyzed using our guided by entropy spectrum pathways method. This new method, called joint estimation diffusion imaging (JEDI), combines the sensitivity to macroscopic diffusion anisotropy of sPFG with the sensitivity to microscopic diffusion anisotropy of dPFG methods. Results Its ability to produce significantly more detailed anisotropy maps and more complete fiber tracts than existing methods within both brain white matter (WM) and gray matter (GM) is demonstrated on normal human subjects on data collected using a novel fast, robust, and clinically feasible sPFG/dPFG acquisition. Conclusions The potential utility of this method is suggested by an initial demonstration of its ability to mitigate the problem of gyral bias. The capability of more completely characterizing the tissue structure and connectivity throughout the entire brain has broad implications for the utility and scope of dMRI in a wide range of research and clinical applications.