Abstract
PurposeHigh-resolution myocardial perfusion analysis allows for preserving spatial information with excellent sensitivity for subendocardial ischemia detection. However, it suffers from low signal-to-noise ratio. Commonly, spatial averaging is used to increase signal-to-noise ratio. This bears the risk of losing information about the extent, localization and transmurality of ischemia. This study investigates spatial-averaging effects on perfusion-estimates accuracy.MethodsPerfusion data were obtained from patients and healthy volunteers. Spatial averaging was performed on voxel-based data in transmural and angular direction to reduce resolution to 50, 20, and 10% of its original value. Fit quality assessment method is used to measure the fraction of modeled information and remaining unmodeled information in the residuals.ResultsFraction of modeled information decreased in patients as resolution reduced. This decrease was more evident for Fermi and exponential in transmural direction. Fermi and exponential showed significant difference at 50% resolution (Fermi P < 0.001, exponential P =0.0014). No significant differences were observed for autoregressive-moving-average model (P = 0.081). At full resolution, autoregressive-moving-average model has the lowest fraction of residual information (0.3). Differences were observed comparing ischemic regions perfusion-estimates coefficient of variation at transmural and angular direction.ConclusionAngular averaging preserves more information compared to transmural averaging. Reducing resolution level below 50% at transmural and 20% at angular direction results in losing information about transmural perfusion differences. Maximum voxel size of 2 × 2 mm2 is necessary to avoid loss of physiological information due to spatial averaging. Magn Reson Med 73:1623–1631, 2015. © 2014 The Authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance.
Highlights
Dynamic contrast enhanced cardiovascular magnetic resonance is increasingly becoming a routine clinical tool to explore the presence and extent of myocardial ischemia [1,2,3,4,5].Perfusion CMR permits tracking of temporal variations in contrast agent concentrations and deriving physiological flow parameters within the tissue, such as myocardial blood flow (MBF) estimation using deconvolution methods [6]
At full resolution (FR) a higher Coefficient of variation (CV) was observed as there were more voxels in the ischemic region
CV dropped by reduction in resolution levels due the lower number of voxels and the smaller extent of MBF values
Summary
Dynamic contrast enhanced cardiovascular magnetic resonance (perfusion CMR) is increasingly becoming a routine clinical tool to explore the presence and extent of myocardial ischemia [1,2,3,4,5].Perfusion CMR permits tracking of temporal variations in contrast agent concentrations and deriving physiological flow parameters within the tissue, such as myocardial blood flow (MBF) estimation using deconvolution methods [6]. Dynamic contrast enhanced cardiovascular magnetic resonance (perfusion CMR) is increasingly becoming a routine clinical tool to explore the presence and extent of myocardial ischemia [1,2,3,4,5]. It is imperative to take into account the poor signal-to-noise ratio (SNR) of high-resolution voxelbased data which might result in inaccuracies in quantitative voxel-wise perfusion CMR analysis. To address the latter issue and obtain higher accuracy, myocardial voxels can be grouped to increase the region of interest (ROI) size (spatial averaging). The downside of this approach will be the reduction of spatial resolution and potential information loss on the localization, extent and transmurality of myocardial ischemia
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