BAYESIAN IMAGE-RECONSTRUCTION FOR EMISSION TOMOGRAPHY BASED ON MEDIANROOT PRIOR

The aim of the present study was to investigate a new type of Bayesian one-step late reconstruction method which utilizes a median root prio r (MRP). The method favours images which have locally monotonous radio activity concentrations. The new reconstruction algorithm was applied to ideal simulated data, phantom data and some patient examinations wi th PET, The same projection data were reconstructed with filtered back -projection (FBP) and maximum likelihood-expectation maximization (ML- EM) methods for comparison, The MRP method provided good-quality image s with a similar resolution to the FBP method with a ramp filter, and at the same lime the noise properties were as good as with Hann-filter ed FBP images. The typical artefacts seen in FBP reconstructed images outside of the object were completely removed, as was the grainy noise inside the object, Quantitatively, the resulting average regional rad ioactivity concentrations in a large region of interest in images prod uced by the MRP method corresponded to the FBP and ML-EM results but s t the pixel by pixel level the MRP method proved to be the most accura te of the tested methods. Tn contrast to other iterative reconstructio n methods, e.g. ML-EMI the MRP method was not sensitive to the number of iterations nor to the adjustment of reconstruction parameters. Only the Bayes lan parameter beta had to be set. The proposed MRP method i s much more simple to calculate than the methods described previously, both with regard to the parameter settings and in terms of general us e. The new MRP reconstruction method was shown to produce high-quality quantitative emission images with only one parameter setting in addit ion to the number of iterations.