Robust, Accurate and Automated Normalization of 3D Arterial Spin Labeling Brain Images
Weiying Dai 1 and David Alsop 1
Radiology, Beth Israel Deaconess Medical
Center & Harvard Medical School, Boston, MA, United
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.
This abstract and the presentation materials are available to members only;
a login is required.