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Related Experiment Video

Updated: Mar 5, 2026

Blood Flow Imaging with Ultrafast Doppler

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

Tensor estimation for double-pulsed diffusional kurtosis imaging.

Calvin B Shaw^1,2, Edward S Hui³, Joseph A Helpern^1,2,4,5

¹Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA.

NMR in Biomedicine

|March 23, 2017

Summary

This summary is machine-generated.

A new method estimates six-dimensional diffusion and kurtosis tensors from double-pulsed diffusional kurtosis imaging (DP-DKI) data. This technique refines the analysis of advanced diffusion MRI (dMRI) signals for better brain imaging interpretation.

Keywords:

DKI MRI brain double diffusion encoding kurtosis least squares microscopic diffusion anisotropy tensor

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Last Updated: Mar 5, 2026

Blood Flow Imaging with Ultrafast Doppler

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Published on: October 14, 2020

Diffusion Imaging in the Rat Cervical Spinal Cord

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Published on: April 7, 2015

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Area of Science:

Magnetic Resonance Imaging
Biophysics
Computational Neuroscience

Background:

Diffusion Kurtosis Imaging (DKI) analyzes water diffusion in biological tissues.
Conventional DKI uses single diffusion encoding (SDE) and 3D tensors.
Double Diffusion Encoding (DDE) offers richer signal information but requires advanced analysis.

Purpose of the Study:

To develop and validate a method for estimating six-dimensional (6D) diffusion and kurtosis tensors from DP-DKI data.
To adapt existing DKI tensor estimation algorithms for the complexities of DP-DKI.
To provide a practical approach for analyzing DDE MRI data.

Main Methods:

Generalization of a standard DKI tensor estimation algorithm using weighted least squares (WLS) fitting.
Implementation of constraints to minimize unphysical parameter estimates, forming a quadratic programming problem.
Replacement of 3D tensors in conventional DKI with 6D tensors required for DP-DKI analysis.

Main Results:

A robust method for estimating 6D diffusion and kurtosis tensors from DP-DKI data was successfully developed.
The algorithm effectively condenses DDE MRI measurements into well-defined mathematical constructs.
Demonstration using brain data from healthy volunteers, including parametric maps of tensor-derived rotational invariants.

Conclusions:

The proposed method offers a practical approach for estimating 6D tensors in DP-DKI.
This facilitates the interpretation and application of advanced DDE MRI techniques.
The technique holds potential for enhancing the understanding of microstructural tissue properties through dMRI.