TY - JOUR
T1 - Non-linear fitting with joint spatial regularization in Arterial Spin Labeling
AU - Maier, Oliver
AU - Spann, Stefan M
AU - Pinter, Daniela
AU - Gattringer, Thomas
AU - Hinteregger, Nicole
AU - Thallinger, Gerhard G
AU - Enzinger, Christian
AU - Pfeuffer, Josef
AU - Bredies, Kristian
AU - Stollberger, Rudolf
N1 - 18 pages, 8 figures, submitted to Medical Image Analysis
PY - 2021/7
Y1 - 2021/7
N2 - Multi-Delay single-shot arterial spin labeling (ASL) imaging provides accurate cerebral blood flow (CBF) and, in addition, arterial transit time (ATT) maps but the inherent low SNR can be challenging. Especially standard fitting using non-linear least squares often fails in regions with poor SNR, resulting in noisy estimates of the quantitative maps. State-of-the-art fitting techniques improve the SNR by incorporating prior knowledge in the estimation process which typically leads to spatial blurring. To this end, we propose a new estimation method with a joint spatial total generalized variation regularization on CBF and ATT. This joint regularization approach utilizes shared spatial features across maps to enhance sharpness and simultaneously improves noise suppression in the final estimates. The proposed method is evaluated at three levels, first on synthetic phantom data including pathologies, followed by in vivo acquisitions of healthy volunteers, and finally on patient data following an ischemic stroke. The quantitative estimates are compared to two reference methods, non-linear least squares fitting and a state-of-the-art ASL quantification algorithm based on Bayesian inference. The proposed joint regularization approach outperforms the reference implementations, substantially increasing the SNR in CBF and ATT while maintaining sharpness and quantitative accuracy in the estimates.
AB - Multi-Delay single-shot arterial spin labeling (ASL) imaging provides accurate cerebral blood flow (CBF) and, in addition, arterial transit time (ATT) maps but the inherent low SNR can be challenging. Especially standard fitting using non-linear least squares often fails in regions with poor SNR, resulting in noisy estimates of the quantitative maps. State-of-the-art fitting techniques improve the SNR by incorporating prior knowledge in the estimation process which typically leads to spatial blurring. To this end, we propose a new estimation method with a joint spatial total generalized variation regularization on CBF and ATT. This joint regularization approach utilizes shared spatial features across maps to enhance sharpness and simultaneously improves noise suppression in the final estimates. The proposed method is evaluated at three levels, first on synthetic phantom data including pathologies, followed by in vivo acquisitions of healthy volunteers, and finally on patient data following an ischemic stroke. The quantitative estimates are compared to two reference methods, non-linear least squares fitting and a state-of-the-art ASL quantification algorithm based on Bayesian inference. The proposed joint regularization approach outperforms the reference implementations, substantially increasing the SNR in CBF and ATT while maintaining sharpness and quantitative accuracy in the estimates.
KW - Bayes Theorem
KW - Brain/diagnostic imaging
KW - Cerebrovascular Circulation
KW - Humans
KW - Magnetic Resonance Imaging
KW - Reproducibility of Results
KW - Spin Labels
UR - http://www.scopus.com/inward/record.url?scp=85105691901&partnerID=8YFLogxK
U2 - 10.1016/j.media.2021.102067
DO - 10.1016/j.media.2021.102067
M3 - Article
C2 - 33930830
VL - 71
JO - Medical Image Analysis
JF - Medical Image Analysis
SN - 1361-8415
M1 - 102067
ER -