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Singular value decomposition analysis of back projection operator of maximum likelihood expectation maximization PET image reconstruction.

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Legrady, David 
Tolnai, Gabor 


Background In emission tomography maximum likelihood expectation maximization reconstruction technique has replaced the analytical approaches in several applications. The most important drawback of this iterative method is its linear rate of convergence and the corresponding computational burden. Therefore, simplifications are usually required in the Monte Carlo simulation of the back projection step. In order to overcome these problems, a reconstruction code has been developed with graphical processing unit based Monte Carlo engine which enabled full physical modelling in the back projection. Materials and methods Code performance was evaluated with simulations on two geometries. One is a sophisticated scanner geometry which consists of a dodecagon with inscribed circle radius of 8.7 cm, packed on each side with an array of 39 × 81 LYSO detector pixels of 1.17 mm sided squares, similar to a Mediso nanoScan PET/CT scanner. The other, simplified geometry contains a 38,4mm long interval as a voxel space, detector pixels are assigned in two parallel sections each containing 81 crystals of a size 1.17×1.17 mm. Results We have demonstrated that full Monte Carlo modelling in the back projection step leads to material dependent inhomogeneities in the reconstructed image. The reasons behind this apparently anomalous behaviour was analysed in the simplified system by means of singular value decomposition and explained by different speed of convergence. Conclusions To still take advantage of the higher noise stability of the full physical modelling, a new filtering technique is proposed for convergence acceleration. Some theoretical considerations for the practical implementation and for further development are also presented.



PET, convergence speed, maximum likelihood expectation maximization reconstruction, positron range, singular value decomposition, transport Monte Carlo, Algorithms, Computer Simulation, Image Processing, Computer-Assisted, Likelihood Functions, Monte Carlo Method, Phantoms, Imaging, Positron-Emission Tomography

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Radiol Oncol

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Walter de Gruyter GmbH