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now lets examine how pet images are formed and reconstructed to obtain the unique functional images that pet offers raw data must be reconstructed thanks to a variety of technological advancements pet image quality has docHubly improved in addition the criterion for determining a good pet image is totally subjective to the user multiple factors influence which iterative reconstruction algorithm rir to employ in pet imaging reconstruction options include choice of filter attenuation and blur weighting comparison by ratio and difference in the number of iterations while sharper filters and larger numbers of iterations offer higher resolution images they also increase noise smoother filters reduce the noise level but then the image resolution isnt as high lets examine a few reconstruction pitfalls that you should be aware of first algorithm tuning is delicate every change requires validation on a large data sample acquired on multiple scanners with multiple detection tasks second