Zhiyuan Zhang1,2, Timothy Macaulay3, and Lirong Yan1
1USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, 2Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, 3Division of Biokinesiology and Physical Therapy, Ostrow School of Dentistry, University of Southern California, Los Angeles, CA, United States
1USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, 2Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, 3Division of Biokinesiology and Physical Therapy, Ostrow School of Dentistry, University of Southern California, Los Angeles, CA, United States
Multi-delay
ASL with PLDs optimized by modeling ATT as normal distribution provides more
accurate CBF and ATT quantifications compared to that with evenly spaced PLDs
or optimal PLDs based on uniform ATT distribution.
Figure 2. a) Three ATT distribution profiles used for
PLD optimization b) and generated optimal PLDs for each ATT distribution along
with evenly spaced PLDs .
Figure 3. The estimated CBF and ATT maps and corresponding
error maps with the four ASL protocols at SNR of 15dB from a subject. Ground
truth was CBF map and ATT map calculated from the in vivo ASL scan,
respectively.
