Non-invasive quantification of physiological processes with dynamic PET using blind deconvolution

Abstract

Dynamic positron emission tomography (PET) has opened the possibility of quantifying physiological processes within the human body. On performing dynamic PET studies, the tracer concentration in blood plasma has to be measured, and acts as the input function for tracer kinetic modelling. In this paper, we propose an approach to estimate physiological parameters for dynamic PET studies without the need of taking blood samples. The proposed approach comprises two major steps. First, a wavelet denoising technique is used to filter the noise appeared in the projections. The denoised projections are then used to reconstruct the dynamic images using filtered backprojection. Second, an eigen-vector based blind deconvolution technique is applied to the reconstructed dynamic images to estimate the physiological parameters. To demonstrate the performance of the proposed approach, we carried out a Monte Carlo simulation using the fluoro-deoxy-2-glucose model, as applied to tomographic studies of human brain. The results demonstrate that the proposed approach can estimate the physiological parameters with an accuracy comparable to that of invasive approach which requires the tracer concentration in plasma to be measured.