Separation of macrovascular signal in multi-inversion time arterial spin labelling MRI

Arterial spin labeling (ASL) provides a noninvasive method to measure brain perfusion and is becoming an increasingly viable alternative to more invasive MR methods due to improvements in acquisition, such as the use of a three-dimensional GRASE readout. A potential source of error in ASL measurements is signal arising from intravascular blood that is destined for more distal tissue. This is typically suppressed using diffusion gradients in many ASL sequences. However, several problems exist with this approach, such as the choice of cutoff velocity and gradient direction and incompatibility with certain readout modules. An alternative approach is to explicitly model the intravascular signal. This study exploits this approach by using multi-inversion time ASL data with a recently developed model-fitting method. The method employed permits the intravascular contribution to be discarded in voxels where there is no support in the data for its inclusion, thereby addressing the issue of overfitting. It is shown by comparing data with and without flow suppression, and by comparing the intravascular contribution in GRASE ASL data to MR angiographic images, that the model-fitting approach can provide a viable alternative to flow suppression in ASL where suppression is either not feasible or not desirable.