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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

Motion-induced phase error estimation and correction in 3D diffusion tensor imaging.

Anh T Van¹, Diego Hernando, Bradley P Sutton

¹Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, USA.

IEEE Transactions on Medical Imaging

|June 10, 2011

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This study introduces a new method to correct motion-induced phase errors in 3D diffusion tensor imaging, improving image quality for high-resolution MRI scans. The technique effectively reduces artifacts caused by patient movement during data acquisition.

Area of Science:

Magnetic Resonance Imaging (MRI)
Medical Physics
Biomedical Engineering

Background:

Multishot diffusion-weighted imaging (DWI) is crucial for high-resolution MRI but susceptible to artifacts from object motion during data acquisition.
Motion during different acquisition shots leads to phase inconsistencies, causing significant image distortions and blurring (T2*).
Existing motion correction methods can be computationally intensive or trajectory-dependent.

Purpose of the Study:

To develop and validate a robust method for estimating and correcting motion-induced phase errors in 3D multishot diffusion tensor imaging (DTI).
To address phase inconsistencies caused by object motion in high-resolution MRI experiments.
To provide an effective and computationally efficient solution for motion artifact reduction in DTI.

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Main Methods:

Proposed a maximum likelihood estimation (MLE) framework for robust and unbiased estimation of motion-induced phase errors.
Implemented a k-space correction strategy to mitigate these phase errors in 3D multishot DTI.
Extended the method to accommodate multichannel data acquired with phased-array coils.
Validated the approach using both simulated and in vivo MRI data.

Main Results:

The proposed MLE-based error estimation approaches the Cramer-Rao lower bound, demonstrating high accuracy and robustness.
The k-space correction effectively removes motion-induced phase errors for rigid body motion, irrespective of the k-space trajectory.
Performance is comparable to more computationally demanding 3D iterative nonlinear phase error correction methods.
The method successfully handles multichannel data, further enhancing its applicability.

Conclusions:

The developed MLE and k-space correction method offers an effective and robust solution for motion artifact reduction in 3D multishot DTI.
This technique improves image quality in high-resolution diffusion-weighted MRI by addressing phase inconsistencies.
The method provides a valuable tool for advanced MRI applications requiring precise motion correction, including multichannel acquisitions.