Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling
Quinten Beirinckx,
Piet Bladt,
Merlijn C.E. van der Plas,
Matthias J.P. van Osch,
Ben Jeurissen,
Arnold J. den Dekker,
Jan Sijbers
Affiliations
Quinten Beirinckx
imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
Piet Bladt
imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
Merlijn C.E. van der Plas
C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
Matthias J.P. van Osch
C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
Ben Jeurissen
imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium; Lab for Equilibrium Investigations and Aerospace, Department of Physics, University of Antwerp, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
Arnold J. den Dekker
imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
Jan Sijbers
imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium; Correspondence to: imec-Vision Lab, University of Antwerp, Universiteitsplein 1, B-2610 Antwerpen, Belgium.
Arterial spin labeling (ASL) is a promising, non-invasive perfusion magnetic resonance imaging technique for quantifying cerebral blood flow (CBF). Unfortunately, ASL suffers from an inherently low signal-to-noise ratio (SNR) and spatial resolution, undermining its potential. Increasing spatial resolution without significantly sacrificing SNR or scan time represents a critical challenge towards routine clinical use. In this work, we propose a model-based super-resolution reconstruction (SRR) method with joint motion estimation that breaks the traditional SNR/resolution/scan-time trade-off. From a set of differently oriented 2D multi-slice pseudo-continuous ASL images with a low through-plane resolution, 3D-isotropic, high resolution, quantitative CBF maps are estimated using a Bayesian approach. Experiments on both synthetic whole brain phantom data, and on in vivo brain data, show that the proposed SRR Bayesian estimation framework outperforms state-of-the-art ASL quantification.
