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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Updated: May 4, 2026

Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

Published on: May 30, 2011

Automated flow quantification for spin labeling MR imaging.

Taichiro Shiodera¹, Shuhei Nitta, Tomoyuki Takeguchi

¹Corporate Research and Development Center, Toshiba Corporation, 1, Komukai-Toshiba-cho, Saiwai-ku, Kawasaki, 212-0001, Japan, taichiro.shiodera@toshiba.co.jp.

Magma (New York, N.Y.)

|January 1, 2014

Summary

This summary is machine-generated.

This study introduces an automated method using Time-Spatial Labeling Inversion Pulse (Time-SLIP) MRI to accurately quantify slow and complex fluid flows. The technique shows high accuracy in phantom and volunteer studies for reliable diagnostic support.

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Area of Science:

Medical Imaging
Fluid Dynamics
Biomedical Engineering

Background:

The Time-Spatial Labeling Inversion Pulse (Time-SLIP) technique offers a contrast-agent-free approach for regional fluid flow analysis.
Accurate quantification of slow and complex fluid dynamics remains a challenge in medical imaging.

Purpose of the Study:

To develop and validate an automated method for quantifying slow and complex fluid flows using the Time-SLIP MRI technique.
To assess the accuracy and efficiency of the proposed automated quantification method.

Main Methods:

Utilized Time-SLIP MRI sequences (half-Fourier FSE and bSSFP) on a 1.5-T scanner.
Implemented image processing for automatic detection of labeled fluid regions.
Calculated flow velocity via regression fitting of regional positions, validated in water phantoms and volunteer CSF studies.

Main Results:

Achieved high correlation coefficients (r² > 0.999 for FSE, r² > 0.998 for bSSFP) in constant flow phantom experiments.
Demonstrated accurate quantification with negligible error in non-constant flow studies, even with velocity variations.
Obtained a high correlation coefficient (r² = 0.9383) compared to manual annotation in volunteer cerebrospinal fluid flow measurements.

Conclusions:

The automated Time-SLIP method accurately quantifies slow and complex fluid velocities.
The technique is rapid and shows potential as a valuable tool in diagnostic support systems for fluid flow analysis.