An adaptive deconvolution filter modifying the Wiener filter allowed accurate measurement of vascular characteristics from indicator-dilution data without prior knowledge of the noise frequency spectrum.
Characteristics of blood flow in tissue can be measured by administering an intravascular tracer and then deconvolving and analysing the resulting indicator-dilution curves. Existing deconvolution methods are not typically generalizable to a variety of tissues. The authors have developed a more general deconvolution method using simulated indicator-dilution data. This method involves filtering the Fourier transform of indicator-dilution data with a modification of the Wiener filter, an adaptive deconvolution filter. Unlike the Wiener filter, this adaptive filter requires no previous knowledge of the noise frequency spectrum; it is derived by varying the magnitude of the noise spectrum until the oscillations in the deconvolved data fall below an optimal value. The optimal value corresponds to the setting of the noise spectrum that allows the most accurate and precise measurement of vascular characteristics from deconvolved data. Vascular characteristics measured in brain tissues using this deconvolution method on actual indicator-dilution data were similar to established values. It should be possible to use this method on time-concentration data collected from a variety of tissues using a number of different tracer measurement techniques, thereby allowing the accurate characterization of vascular physiology.
Gobbel et al. (Tue,) conducted a other in Vascular physiology / Blood flow measurement. Adaptive deconvolution filter (modified Wiener filter) vs. Existing deconvolution methods was evaluated on Accuracy and precision of vascular characteristics measurement. An adaptive deconvolution filter modifying the Wiener filter allowed accurate measurement of vascular characteristics from indicator-dilution data without prior knowledge of the noise frequency spectrum.