REGIONAL TRANSIT-TIME ESTIMATION FROM IMAGE RESIDUE CURVES

Methods for estimating regional flow from digital angiography or dynam ic computed tomography images require determination of indicator mean transit time (tBAR) through a region-of-interest (ROI). We examine how the ROI kinematics and input dispersion influence the recovery of tBA R using a computer-simulated vessel network representing that which mi ght occur in a real organ. The network simulates flow through a large artery branching into two small arteries, each feeding a system of sma ller vessels intended to represent capillaries and small vessels below the resolution of the imaging system. The capillaries are drained by a similar system of veins. Concentration curves measured over the inle t to the network and microvascular ROI residue curves are simulated. W hen the area-height ratio of the microvascular ROI curve is used and a ll of the indicator is contained within the ROI for at least one time point, tBAR is recovered exactly. As the size of the ROI is reduced or the inlet concentration curve becomes more dispersed, the error in th e recovery of tBAR grows. By first deconvolving the inlet concentratio n curve from the microvascular ROI curve, and then calculating the are a-height ratio, tBAR is recovered accurately. If the inlet concentrati on curve becomes more dispersed between its measured site and the actu al inlet to the ROI, or if the flow distribution within the ROI is cha nged, the estimation of tBAR can be degraded. To put the simulations i n perspective relative to an example of image data, the methods were a pplied to microfocal x-ray angiography data obtained from a approximat ely 700 mum canine pulmonary artery and vein, the surrounding microvas culature and the inlet lobar arterial cannula.