1 June 1991 New algorithm and an efficient parallel implementation of the expectation maximization technique in PET (positron emission tomography) imaging
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Abstract
Algorithms that are used in image reconstruction fall broadly into one of two categories [1-2]. First category is based on Fourier methods to reconstruct a function from its line integrals on certain regions. Second category is based on probabilistic reconstruction methods which take into account the stochastic nature of the problem and hence yield more accurate results under a broad range of parameters and data. In most general terms image reconstruction methods take the data that is passed through a general filtering mechanism and reconstruct the original image. In medical applications the amount of data that should be processed for this purpose is usually prohibitive for certain methods due to storage and time limitations. In this paper we will describe methods to help overcome these difficulties using various techniques. Positron emission tomography (PET) is a medical diagnostic procedure [3-4] that enables physicians to visually evaluate the metabolic activity in various organs. Rather than generating 'static' pictures as in X-rays, PET introduces low levels of positron emitting radioactive material in the organ under study, and levels of absorption on various parts of the organ is measured by the PET scanner [5]. The type of biochemical and the radioactive material used depends on the organ to be studied. It is known that the brain uses glucose as a primary energy source and therefore glucose 'injected' with radioactive material is used for brain studies, for the study of heart deoxyglucose and palmitic acid injected with the radioactive material is used. Depending on the met.abolization of the injected material one can generate a 'dynamic' picture of the organ. For example if the brain is studied, the patient's psychosomatic condition at the time of the study will be different than at a different time and condition. This hopefully will reveal valuable information on the patient's condition and the effect of various treatments (schizophrenia, etc.). These is a broad literature on PET imaging and the interested reader is referred to [4-5] and the references there.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Cagatay Buyukkoc, Cagatay Buyukkoc, G. Persiano, G. Persiano, } "New algorithm and an efficient parallel implementation of the expectation maximization technique in PET (positron emission tomography) imaging", Proc. SPIE 1452, Image Processing Algorithms and Techniques II, (1 June 1991); doi: 10.1117/12.45381; https://doi.org/10.1117/12.45381
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