Non-linear fitting with joint spatial regularization in Arterial Spin Labeling

Oliver Maier, Stefan M Spann, Daniela Pinter, Thomas Gattringer, Nicole Hinteregger, Christian Enzinger, Josef Pfeuffer, Kristian Bredies, Rudolf Stollberger

Research output: Contribution to journalArticle

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 validated in three stages, 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.
Original languageEnglish
JournalarXiv.org e-Print archive
Publication statusSubmitted - 10 Sep 2020

Keywords

  • eess.IV
  • eess.SP
  • 92C55, 68U10, 94A12

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