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Pulmonary Structural MRI using Free-Breathing, Self-Gated Ultra-short Echo Time Imaging

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A robust ultrashort TE (UTE) imaging method with corrected k-space trajectory by using parametric multiple function

Masahiro Takizawa¹, Hikaru Hanada, Kuniharu Oka

¹MRI System Division, Hitachi Medical Corporation, Chiba, Japan. takizawa@rd.hitachi-medical.co.jp

IEEE Transactions on Medical Imaging

|November 13, 2012

Summary

Ultra-short TE (UTE) imaging requires precise k-space data. A new method corrects gradient errors, improving image quality for faster, robust MRI scans.

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Area of Science:

Magnetic Resonance Imaging (MRI)
Biomedical Engineering

Background:

Ultra-short TE (UTE) sequences enable visualization of tissues with very short T2 decay times.
UTE sequences are highly sensitive to k-space trajectory errors caused by gradient system imperfections.

Purpose of the Study:

To develop a simple, robust, and time-efficient method for correcting k-space errors in UTE sequences for clinical applications.
To address image degradation caused by gradient system imperfections like gradient delay and waveform distortion.

Main Methods:

Simulations were used to demonstrate image degradation due to k-space errors, identifying gradient time delays as the dominant cause.
A pre-scan calibration comparing half and full echo signals was employed to correct gradient time delays.
A one-time calibration of gradient waveform approximations, using multiple exponential functions, was integrated into image reconstruction.

Main Results:

Uncontrolled gradient time delays were confirmed as the primary source of image degradation in UTE sequences.
The pre-scan calibration effectively corrected gradient time delay errors, significantly improving image quality.
Gradient waveform approximation further enhanced image quality during reconstruction.

Conclusions:

A robust and time-efficient correction method for UTE sequences has been developed.
The method combines a short pre-scan for gradient time delay estimation with gradient waveform approximation for improved image reconstruction.
This approach is suitable for clinical MRI examinations with time limitations.