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Updated: Aug 17, 2026
Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling
Published on: May 30, 2011
A signal processing model for arterial spin labeling functional MRI
1Center for Functional Magnetic Resonance Imaging and Department of Radiology, University of California-San Diego, La Jolla, CA 92093-0677, USA. ttliu@ucsd.edu
Abstract:
A model of the signal path in arterial spin labeling (ASL)-based functional magnetic resonance imaging (fMRI) is presented. Three subtraction-based methods for forming a perfusion estimate are considered and shown to be specific cases of a generalized estimate consisting of a modulator followed by a low pass filter. The performance of the methods is evaluated using the signal model. Contamination of the perfusion estimate by blood oxygenation level dependent contrast (BOLD) is minimized by using either sinc subtraction or surround subtraction for block design experiments and by using pair-wise subtraction for randomized event-related experiments. The subtraction methods all tend to decorrelate the 1/f type low frequency noise often observed in fMRI experiments. Sinc subtraction provides the flattest noise power spectrum at low frequencies, while pair-wise subtraction yields the narrowest autocorrelation function. The formation of BOLD estimates from the ASL data is also considered and perfusion weighting of the estimates is examined using the signal model.
Insights
This study models arterial spin labeling (ASL) functional MRI signals, presenting a generalized perfusion estimation method. Specific subtraction techniques effectively minimize blood oxygenation level dependent (BOLD) contamination and reduce low-frequency noise in fMRI data.
Area of Science:
- Neuroimaging
- Biophysics
Background:
- Arterial spin labeling (ASL) is a functional magnetic resonance imaging (fMRI) technique used to measure cerebral blood flow.
- Subtraction-based methods are commonly employed in ASL-fMRI to isolate perfusion signals.
- Blood oxygenation level dependent (BOLD) contrast can contaminate ASL perfusion estimates, affecting accuracy.
Purpose of the Study:
- To present a generalized signal path model for ASL-fMRI.
- To evaluate the performance of different subtraction-based perfusion estimation methods.
- To investigate strategies for minimizing BOLD contamination and low-frequency noise in ASL-fMRI.
Main Methods:
- Developed a generalized model for the ASL-fMRI signal path, incorporating a modulator and low-pass filter.
- Analyzed three specific subtraction methods: sinc subtraction, surround subtraction, and pair-wise subtraction.
- Evaluated the methods' ability to minimize BOLD contamination and decorrelate low-frequency noise (1/f noise).
Main Results:
- The generalized model encompasses the three considered subtraction methods as specific cases.
- Sinc subtraction and surround subtraction are effective for block design experiments, while pair-wise subtraction is optimal for event-related designs in minimizing BOLD contamination.
- All subtraction methods reduce low-frequency noise; sinc subtraction yields the flattest noise spectrum, and pair-wise subtraction results in the narrowest autocorrelation function.
Conclusions:
- The generalized model provides a unified framework for understanding ASL-fMRI signal processing.
- Optimized subtraction strategies can significantly improve the specificity and reduce noise in ASL-fMRI perfusion estimates.
- The study offers insights into optimizing ASL-fMRI for accurate BOLD and perfusion measurements.
