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

Arterial Spin Labeling (ASL) is a noninvasive magnetic resonance imaging (MRI) method for measuring cerebral blood flow (CBF). Recent studies (1) have shown great potential for ASL in clinical imaging. ASL is completely noninvasive, repeatable, quantitative, and has shown close correlations with other blood flow imaging techniques including dynamic susceptibility contrast MRI, computed tomography perfusion, positron emission tomography, and single photon emission computed tomography (2). While other imaging modalities use an exogenous contrast agent, ASL uses the water in the blood as an endogenous tracer to measure blood flow from a set of tagged (label) and untagged (control) images. The control and label image pairs are subtracted from each other to eliminate the static tissue signal while retaining the signal from the blood that has perfused into the imaging slices. Since the static tissue signal is much larger than the signal from the blood, instabilities between the control/label images resulting from patient motion, physiological conditions, hardware instabilities, and unexpected mid-scan interruptions by the technologist to communicate with the patient can lead to large subtraction errors. While these instabilities may occur in only a small percentage of the control/ label images, the effect can have a drastic and detrimental affect on the final perfusion-weighted image (PWI) obtained by averaging all of the individual PWIs together in the time series. These subtraction errors between the control and label images can manifest themselves in the individual PWIs as hyper- or hypoin-tense perfusion signal involving just a few slices or the entire brain volume. By selectively discarding these inconsistent images in the perfusion time series, the final averaged PWI can be improved with only a minor penalty in the signal-to-noise ratio (SNR). A number of techniques have been developed to improve CBF maps acquired with ASL: physiological noise reduction for functional perfusion experiments (3,4), applying a low pass filter to the subtracted control/ label images (5), static tissue background suppression methods (6), and selective averaging of control/label images based on motion criteria (7). While these methods have been shown to be of importance when examining the spectrum of the perfusion time series for functional MRI ASL studies, they may not be able to correct for dramatic signal variations caused by hardware instability, large and abrupt patient motion, and unexpected disruption of the steady state. One previous study (7) has shown that the quality of CBF maps can be improved by removing control/label pairs that were acquired during periods of motion exceeding 2 mm of translations and 1.5 degrees of rotations. The remaining control/label images were subtracted from each other and averaged to create a final PWI. The final PWIs for all cases were visually inspected. Those with severe residual artifacts were removed from the study for further analysis. For research studies with a dedicated staff team, visual inspection is certainly a viable method for postprocessing and improving perfusion image quality. However, as perfusion imaging becomes readily available for clinical applications, it is neither feasible nor practical to manually process each ASL case. An automated method is necessary for improving the perfusion image quality in a clinical environment. At our institution, a pulsed ASL (PASL) CBF map is acquired as part of the standard clinical MRI protocol for patients with an indication of vascular abnormalities (stroke, transient ischemic attack, or carotid stenosis), neoplasm, or seizure. Over 10,000 clinical CBF maps (8–10) with a rate of 15–20 cases per day have been acquired in a time period of 2 years using an automated image processing pipeline (11). This represents the largest experience with PASL in clinical practice to date. In this article we describe an automated postprocessing filtering procedure that effectively improves the overall quality and stability of the clinical perfusion measurement.