Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Maier, O; Spann, SM; Pinter, D; Gattringer, T; Hinteregger, N; Thallinger, GG; Enzinger, C; Pfeuffer, J; Bredies, K; Stollberger, R.
Non-linear fitting with joint spatial regularization in arterial spin labeling.
Med Image Anal. 2021; 71:102067-102067
Doi: 10.1016/j.media.2021.102067
[OPEN ACCESS]
Web of Science
PubMed
FullText
FullText_MUG
- Co-authors Med Uni Graz
- Enzinger Christian
- Gattringer Thomas
- Pinter Daniela Theresia
- Stranner Nicole
- Altmetrics:
- Dimensions Citations:
- Plum Analytics:
- Scite (citation analytics):
- Abstract:
- 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. Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.
- Find related publications in this database (Keywords)
- Quantitative ASL
- Non-linear fitting
- Quantitative mapping
- Stroke