A fast, effective filtering method for improving clinical pulsed arterial spin labeling MRI.

ABSTRACT

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.