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Updated: Jun 23, 2026
Construction and Application of Cerebral Functional Region-Based Cerebral Blood Flow Atlas Using Magnetic Resonance Imaging-Arterial Spin Labeling
Published on: May 31, 2024
A fast, effective filtering method for improving clinical pulsed arterial spin labeling MRI
Huan Tan1, Joseph A Maldjian, Jeffrey M Pollock
1Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157, USA. htan@wfubmc.edu
Purpose:
To evaluate the effectiveness of a fully automated postprocessing filter algorithm in pulsed arterial spin labeling (PASL) MRI perfusion images in a large clinical population.
Materials And Methods:
A mean and standard deviation-based filter was implemented to remove outliers in the set of perfusion-weighted images (control - label) before being averaged and scaled to quantitative cerebral blood flow (CBF) maps. Filtered and unfiltered CBF maps from 200 randomly selected clinical cases were assessed by four blinded raters to evaluate the effectiveness of the filter.
Results:
The filter salvaged many studies deemed uninterpretable as a result of motion artifacts, transient gradient, and/or radiofrequency instabilities, and unexpected disruption of data acquisition by the technologist to communicate with the patient. The filtered CBF maps contained significantly (P < 0.05) fewer artifacts and were more interpretable than unfiltered CBF maps as determined by one-tail paired t-test.
Conclusion:
Variations in MR perfusion signal related to patient motion, system instability, or disruption of the steady state can introduce artifacts in the CBF maps that can be significantly reduced by postprocessing filtering. Diagnostic quality of the clinical perfusion images can be improved by performing selective averaging without a significant loss in perfusion signal-to-noise ratio.
Insights
An automated filter algorithm significantly improves the diagnostic quality of pulsed arterial spin labeling (PASL) MRI perfusion images by reducing artifacts. This enhances interpretation of cerebral blood flow (CBF) maps in clinical populations.
Area of Science:
- Medical Imaging
- Neuroimaging
- Radiology
Background:
- Pulsed arterial spin labeling (PASL) MRI is crucial for assessing cerebral blood flow (CBF).
- Image artifacts in PASL can compromise diagnostic accuracy.
- Automated postprocessing methods are needed to improve image quality.
Purpose of the Study:
- To evaluate an automated postprocessing filter for PASL MRI.
- To assess its effectiveness in a large clinical cohort.
- To determine if it reduces artifacts and improves interpretability of CBF maps.
Main Methods:
- A mean and standard deviation-based filter was developed to remove outliers in PASL perfusion-weighted images.
- Filtered and unfiltered quantitative cerebral blood flow (CBF) maps from 200 clinical cases were compared.
- Four blinded raters assessed the interpretability and artifact levels of the CBF maps.
Main Results:
- The automated filter successfully salvaged studies with motion artifacts, system instabilities, and acquisition disruptions.
- Filtered CBF maps demonstrated significantly fewer artifacts compared to unfiltered maps (P < 0.05).
- Rater assessments confirmed improved interpretability of filtered PASL MRI perfusion images.
Conclusions:
- Automated postprocessing filtering effectively reduces artifacts in PASL CBF maps caused by patient motion and system instability.
- This technique enhances the diagnostic quality of clinical perfusion imaging.
- Selective averaging via filtering improves image quality without significant loss of perfusion signal-to-noise ratio.
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