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Updated: Jun 2, 2026

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Published on: May 7, 2019

A diffusion gradient optimization framework for spinal cord diffusion tensor imaging.

Shantanu Majumdar¹, David C Zhu, Satish S Udpa

¹Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA. majumd10@msu.edu

Magnetic Resonance Imaging

|May 10, 2011

Summary

This summary is machine-generated.

Optimizing diffusion gradient directions in diffusion tensor imaging (DTI) reduces parameter estimation uncertainty, especially for low signal-to-noise ratio spinal cord DTI. This D-optimality approach enhances accuracy in brain stem and cervical spinal cord imaging.

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Last Updated: Jun 2, 2026

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

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Published on: May 7, 2019

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Published on: April 7, 2015

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Area of Science:

Medical Imaging
Biophysics
Computational Neuroscience

Background:

Diffusion Tensor Imaging (DTI) is crucial for visualizing white matter tracts.
Estimating DTI model parameters is challenging due to low signal-to-noise ratio (SNR) in spinal cord imaging.
Prior structural information can improve DTI parameter estimation accuracy.

Purpose of the Study:

To develop and validate a D-optimality based gradient optimization scheme for DTI.
To reduce uncertainty in diffusion model parameter estimation for spinal cord and brain stem white matter.
To improve the reliability of DTI in low SNR environments.

Main Methods:

Implemented a gradient optimization scheme using D-optimality criteria.
Utilized a simplified four-parameter axisymmetric DTI model.
Assessed performance through simulations and experimental validation on cervical spinal cord and brain stem data.

Main Results:

The D-optimality scheme significantly reduced the variance of diffusion model parameter estimates.
Minimized the Rician Cramer-Rao lower bound for parameter estimation uncertainty.
Demonstrated effectiveness in both simulated and real DTI data for spinal cord and brain stem.

Conclusions:

Optimized diffusion gradient selection via D-optimality effectively reduces parameter estimation uncertainty in DTI.
This method is particularly beneficial for challenging DTI applications like spinal cord imaging.
The approach enhances the precision and reliability of white matter tract analysis.