Pixel-wise quantification of myocardial perfusion using spatial Tikhonov regularization

Judith Lehnert, Gerd Wübbeler, Christoph Kolbitsch, Amedeo Chiribiri, Loïc Coquelin, Géraldine Ebrard, Nadia Smith, Tobias Schaeffter, Clemens Elster

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
185 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)215017
JournalPhysics in Medicine and Biology
Volume63
Issue number21
Early online date10 Oct 2018
DOIs
Publication statusPublished - 29 Oct 2018

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