Advanced quantification of myocardial perfusion

Ischemic heart disease remains a major global health concern with significant morbidity and mortality issues. Identifying areas of myocardial tissue at risk early on can help guide clinical management and develop appropriate treatment strategies to prevent myocardial infarction, thus improving patient outcomes. Using the latest cardiac magnetic resonance (CMR) imaging techniques, first-pass perfusion imaging allows for a very high spatial resolution, non-invasive and radiation free quantification of myocardial blood flow (MBF). True quantification of very high resolution perfusion images offers a unique capability to localize and measure subendocardial ischemia. A common technique for calculating MBF from dynamic contrast-enhanced cardiovascular MR (DCE-CMR) is to track a bolus of contrast agent and measure MBF using fully quantitative methods. These methods which are based on central volume principle deconvolve the changes in the concentration of the injected contrast agent in the tissue with the arterial input function (AIF). However deconvolution is inherently a difficult process and therefore numerically unstable with noise contaminated data. The purpose of this study is to enable high spatial resolution voxel-wise quantitative analysis of myocardial perfusion in DCE-CMR, in particular by finding the most favourable quantification algorithm in this context. Voxel-wise quantification has the potential to combine the advantage of visual analysis with the objective and reproducible evaluation made possibly by a true quantitative assessment. Four deconvolution algorithms – Fermi function modelling, deconvolution using B-spline basis, deconvolution using exponential basis and Auto-Regressive Moving Average modelling (ARMA) were tested to calculate voxel-wise perfusion estimates. The algorithms were developed on synthetic data and validated against a true gold-standard using a hardware perfusion phantom and an explanted perfused pig heart. The accuracy of each method was assessed for different levels of spatial resolution. Moreover robustness of each deconvolution algorithm to variation in perfusion modelling parameters was evaluated. Finally, voxel-wise analysis was used to generate high resolution perfusion maps on real data acquired from healthy volunteers and patients with coronary artery disease. Both simulations and maps in the hardware phantom, explanted pig heart data and patient studies showed that voxel-wise quantification of myocardium perfusion is feasible and can be used to detect abnormal regions with high sensitivity in identifying the tissue at risk. In general ARMA and the exponential method showed to be more accurate, on the other hand, Fermi model was the most robust method to noise with highest precision for voxel-wise analysis. Inevitably the choice of quantification method for data analysis boils down to a trade-off between accuracy and precision of the estimation. List of abbreviations AIF Arterial input function ANOVA Analysis of variance AR Auto regressive ARMA Autoregressive moving average BIBO Bounded input bounded output CA Contrast agent CAD coronary artery diseases CMR Cardiovascular magnetic resonance CNR Contrast to noise ratio CV Coefficient of variation CVD Cardiovascular disease DCE Dynamic contrast enhanced ECG Electrocardiography FRI Fraction of residual information GS Gold standard LAD left anterior descending artery LCX left circumflex artery LV Left ventricle MA Moving average MBF Myocardial blood flow MDCT Multi-detector computed tomography MPR Myocardial perfusion reserve MR Magnetic resonance MTT Mean transit time PET Positron emission tomography RCA Right coronary artery ROC Region of convergence ROI Region of interest RF Radio frequency STD Standard deviation SI Signal intensity SNR Signal to noise ratio SPECT Single photon emission computed tomography SVD Singular value decomposition TE Time of echo