Arterial spin labeling (ASL) is a powerful tool for the noninvasive assessment of tissue perfusion. In the human heart, however, measuring myocardial perfusion (MBF) is challenging due to strong physiological noise. Steady-pulsed ASL (spASL) under free-breathing had been proposed to improve sensitivity. To improve robustness against respiratory motion, efficiency and spatial resolution, we present an optimization of the postprocessing algorithm by way of a dedicated motion correction (Moco).

The spASL sequence was implemented on a Siemens 3T Verio system, based on an ECG-triggered bSSFP acquisition combined with a sliceselective labeling module. ASL was performed at each cardiac cycle to drive tissue magnetization into a perfusion-dependent steady-state. The spASL acquisition was repeated 128 times to accumulate data and a customized 2-step Moco was carried out. First, global correction in a large ROI was performed by spatially shifting every image and minimizing signal difference with a reference. Then, a more precise regional correction based on contour correlation was used.

When performing global correction, MBF mapping was possible. Fig.1 shows a comparison between two maps in a volunteer without and with Moco. Better homogeneity and delineation of the myocardium can be seen. Using the 2nd described step before carrying out the quantification in myocardial regions led to improved signal stability. Without Moco, signal was found stable when including up to 30% of all images. With Moco, the signal reached a plateau around 50% and remained stable until 80% of included images.

With this new approach, a greater portion (80% versus 30% formerly) of the acquired data can be used for regional perfusion assessment. This work is also a step towards calculation of myocardial perfusion maps with high spatial resolution using ASL.