Pixel-wise quantification of myocardial perfusion using spatial Tikhonov regularization

TY - JOUR

T1 - Pixel-wise quantification of myocardial perfusion using spatial Tikhonov regularization

AU - Lehnert, Judith

AU - Wübbeler, Gerd

AU - Kolbitsch, Christoph

AU - Chiribiri, Amedeo

AU - Coquelin, Loïc

AU - Ebrard, Géraldine

AU - Smith, Nadia

AU - Schaeffter, Tobias

AU - Elster, Clemens

PY - 2018/10/29

Y1 - 2018/10/29

N2 - 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.

AB - 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.

U2 - 10.1088/1361-6560/aae758

DO - 10.1088/1361-6560/aae758

M3 - Article

C2 - 30372423

SN - 0031-9155

VL - 63

SP - 215017

JO - Physics in Medicine and Biology

JF - Physics in Medicine and Biology

IS - 21

ER -