Diffusion MRI (dMRI) is used to characterize the directionalityand microstructural properties of brain white matter (WM)by measuring the diffusivity of water molecules. In clinicalpractice the number of dMRI samples that can be obtained islimited, and one often uses short scanning protocols that acquirejust 32 to 64 different gradient directions using a singlegradient strength (b-value). Such 'single shell' scanning protocolsrestrict one to use methods that have assumptions onthe radial decay of the dMRI signal over different b-values,which introduces estimation biases. In this work we show,that by simply spreading the same number of samples overmultiple b-values (i.e. multi-shell) we can accurately estimateboth the WM directionality using 3D-SHORE and characterizethe radially dependent diffusion microstructure measuresusing MAP-MRI. We validate our approach by undersamplingboth noisy synthetic and human brain data of the HumanConnectome Project, proving this approach is well-suited forclinical applications.

Using 3D-SHORE and MAP-MRI to obtain both tractography and microstructural contrasts from clinical DMRI acquisitions

Zucchelli, Mauro;MENEGAZ, Gloria;
2015-01-01

Abstract

Diffusion MRI (dMRI) is used to characterize the directionalityand microstructural properties of brain white matter (WM)by measuring the diffusivity of water molecules. In clinicalpractice the number of dMRI samples that can be obtained islimited, and one often uses short scanning protocols that acquirejust 32 to 64 different gradient directions using a singlegradient strength (b-value). Such 'single shell' scanning protocolsrestrict one to use methods that have assumptions onthe radial decay of the dMRI signal over different b-values,which introduces estimation biases. In this work we show,that by simply spreading the same number of samples overmultiple b-values (i.e. multi-shell) we can accurately estimateboth the WM directionality using 3D-SHORE and characterizethe radially dependent diffusion microstructure measuresusing MAP-MRI. We validate our approach by undersamplingboth noisy synthetic and human brain data of the HumanConnectome Project, proving this approach is well-suited forclinical applications.
2015
Diffusion MRI
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/878215
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