Pixel-wise quantification of myocardial perfusion using spatial Tikhonov regularization. (29th October 2018)

• Record Type: Journal Article
• Authors: Lehnert, Judith; Wübbeler, Gerd; Kolbitsch, Christoph; Chiribiri, Amedeo; Coquelin, Loïc; Ebrard, Géraldine; Smith, Nadia; Schaeffter, Tobias; Elster, Clemens
• Is Part Of: Physics in medicine & biology. Volume 63:Number 21(2018:Nov.)
• Publication Date: 2018-10-29
• Subjects: cardiovascular magnetic resonance -- dynamic contrast-enhanced magnetic resonance imaging -- myocardial perfusion -- perfusion quantification -- spatial resolution -- Fermi method -- Tikhonov regularization; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aae758
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Quantification of myocardial perfusion by contrast-enhanced cardiovascular magnetic resonance imaging (CMR) aims for an observer independent and reproducible risk assessment of cardiovascular disease. Currently, the data used for the pixel-wise analysis of cardiac perfusion are either filtered prior to a fitting procedure, which inherently reduces the spatial resolution of data; or all pixels are considered without any regularization or prior filtering, which yields an unstable fit in the presence of low signal-to-noise ratio. Here, we propose a new pixel-wise analysis based on spatial Tikhonov regularization which exploits the spatial smoothness of the data and ensures accurate quantification even for images with low signal-to-noise ratio. The regularization parameter is determined automatically by an L-curve criterion. We study the performance of our method on a numerical phantom and demonstrate that the method reduces significantly the root-mean square error in the perfusion estimate compared to a non-regularized fit. In patient data our method allows us to recover the myocardial perfusion and to distinguish between healthy and ischemic regions.