CT-based assessment of regional pulmonary blood flow parameters: an update

We have previously reported a deconvolution-based technique to recover regional microvascular transport characteristics from dynamic CT images. We have refined our deconvolution algorithm and used Monte Carlo simulations to estimate the error and confidence interval of the resulting regional microvascular mean transit time (MIT) measures. Random errors, assumed to be due to white noise in the imaging process, were superimposed upon known input (PA) and regional parenchymal time-intensity (blood flow) data. The resulting simulated data were then fit to gamma variate functions and processed via our deconvolution algorithm to provide microvascular MTT measures for the simulated curves. The magnitude of the noise used in the simulations was obtained by subtracting two consecutively acquired images (approximately 1.5 sec delay between the two images) from a dynamic imaging sequence of a supine dog imaged at FRC. A total of 35 simulations were performed for each of five sample locations spanning the dependent to nondependent extent of the lungs. Microvascular MTT ranged from 3.39 sec to 9.67 sec as sample locations were moved from dependent to nondependent areas of the lungs. The standard error associated with these measures ranged from plus or minus 0.03 sec in the dependent portion of the lungs to plus or minus 0.27 sec in the non-dependent area of the lungs.