Stochastic modeling for magnetic resonance quantification of myocardial blood flow

Quantification of myocardial blood flow is useful for determining the functional severity of coronary artery lesions. With advances in MR imaging it has become possible to assess myocardial perfusion and blood flow in a non-invasive manner by rapid serial imaging following injection of contrast agent. To date most approaches reported in the literature relied mostly on deriving relative indices of myocardial perfusion directly from the measured signal intensity curves. The central volume principle on the other hand states that it is possible to derive absolute myocardial blood flow from the tissue impulse response. Because of the sensitivity involved in deconvolution due to noise in measured data, conventional methods are suboptimal, hence, we propose to use stochastic time series modeling techniques like ARMA to obtain a robust impulse response estimate. It is shown that these methods when applied for the optimal estimation of the transfer function give accurate estimates of myocardial blood flow. The most significant advantage of this approach, compared with compartmental tracer kinetic models, is the use of a minimum set of prior assumptions on data. The bottleneck in assessing myocardial blood flow, does not lie in the MRI acquisition, but rather in the effort or time for post processing. It is anticipated that the very limited requirements for user input and interaction will be of significant advantage for the clinical application of these methods. The proposed methods are validated by comparison with mean blood flow measurements obtained from radio-isotope labeled microspheres.