Regional transit time estimation from image residue curves

Methods for estimating regional flow from digital angiography or dynamic computed tomography images require determination of indicator mean transit time (\(\bar t\)) through a region-of-interest (ROI). We examine how the ROI kinematics and input dispersion influence the recovery of\(\bar t\) using a computer-simulated vessel network representing that which might occur in a real organ. The network simulates flow through a large artery branching into two small arteries, each feeding a system of smaller 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 inlet to the network and microvascular ROI residue curves are simulated. When the area-height ratio of the microvascular ROI curve is used and all of the indicator is contained within the ROI for at least one time point,\(\bar t\) is recovered exactly. As the size of the ROI is reduced or the inlet concentration curve becomes more dispresed, the error in the recovery of\(\bar t\) grows. By first deconvolving the inlet concentration curve from the microvascular ROI curve, and then calculating the area-height ratio,\(\bar t\) is recovered accurately. If the inlet concentration curve becomes more dispersed between its measured site and the actual inlet to the ROI, or if the flow distribution within the ROI is changed, the estimation of\(\bar t\) can be degraded. To put the simulations in perspective relative to an example of image data, the methods were applied to microfocal x-ray angiography data obtained from a ⊃700 μm canine pulmonary artery and vein, the surrounding microvasculature and the inlet lobar arterial cannula.