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Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling.
Beirinckx, Quinten; Bladt, Piet; van der Plas, Merlijn C E; van Osch, Matthias J P; Jeurissen, Ben; den Dekker, Arnold J; Sijbers, Jan.
Afiliación
  • Beirinckx Q; imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium; µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium.
  • Bladt P; imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium; µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium.
  • van der Plas MCE; C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
  • van Osch MJP; C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
  • Jeurissen B; 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.
  • den Dekker AJ; imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium; µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium.
  • Sijbers J; imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium; µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium. Electronic address: jan.sijbers@uantwerpen.be.
Neuroimage ; 286: 120506, 2024 Feb 01.
Article en En | MEDLINE | ID: mdl-38185186
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Angiografía por Resonancia Magnética Tipo de estudio: Prognostic_studies Aspecto: Patient_preference Límite: Humans Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: Bélgica Pais de publicación: Estados Unidos
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Angiografía por Resonancia Magnética Tipo de estudio: Prognostic_studies Aspecto: Patient_preference Límite: Humans Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: Bélgica Pais de publicación: Estados Unidos