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Updated: May 4, 2026

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.
Objective:
The Time-Spatial Labeling Inversion Pulse (Time-SLIP) technique enables tracing of regional fluid flows without the use of contrast medium. The objective of this study is to quantify automatically slow and complex fluid flows using the Time-SLIP technique.
Materials And Methods:
Series images were acquired with a 1.5-T MRI scanner using the Time-SLIP technique with half-Fourier fast spin-echo (FSE) and balanced steady-state free precession (bSSFP) sequences. In this method, labeled fluid regions in images were automatically detected based on image processing techniques for a given point. The flow velocity of the labeled fluid region was calculated using regression fitting for the region's position. To evaluate our method, constant and non-constant laminar flows in a water phantom were studied. In addition, volunteer experiments were conducted to quantify the flow of cerebrospinal fluid.
Results:
In the constant flow experiments the correlation factor r (2) between the flow velocity calculated from our method and the laminar peak velocity calculated from the volumetric flow rate was 0.9992 for the FSE sequence and 0.9982 for the bSSFP sequence. In the non-constant flow study, the flow velocity was calculated accurately for any period inversion time even when the flow velocity was changed, and the quantification error was negligible. In the volunteer experiments, r (2) between the flow velocity calculated by the proposed method and that obtained by manual annotation was 0.9383.
Conclusion:
The experimental results showed that our proposed method can quickly and accurately provide information on flow velocities especially for slower and complex flows. Our method is, therefore, expected to be useful in diagnostic support systems.
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