Robust, Accurate and Automated Normalization of 3D Arterial Spin Labeling Brain Images

Arterial spin labeling (ASL) has proven to be useful tool both in clinical and research applications. Transforming ASL images of multiple subjects to a common space is first critical step for any statistical inference. However, the low SNR of ASL and bright voxels outside the brain complicate accurate and automated normalization. Here, we propose a robust and automated normalization procedure by taking advantage of the recent advancements of T1 based normalization. The normalization method is very robust and accurate and has been tested in all 146 subjects scanned in a representative elderly cohort.

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