<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_raon-2018-0013_s_005_w2aab2b8c10b1b7b1aab1c16b1Aa"><h3>Background</h3><p>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.</p></sec><sec id="j_raon-2018-0013_s_006_w2aab2b8c10b1b7b1aab1c16b2Aa"><h3>Materials and methods</h3><p>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.</p></sec><sec id="j_raon-2018-0013_s_007_w2aab2b8c10b1b7b1aab1c16b3Aa"><h3>Results</h3><p>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.</p></sec><sec id="j_raon-2018-0013_s_008_w2aab2b8c10b1b7b1aab1c16b4Aa"><h3>Conclusions</h3><p>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.</p></sec></abstract>

BackgroundIn 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 methodsCode 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.ResultsWe 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.ConclusionsTo 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.

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

Budapest University of Technology and Economics

Radiology and Oncology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

BackgroundIn emission tomography maximum likelihood expectation maximization reconstruction technique has replaced the analytical approaches in several...

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

Article Category: Research Article

Publié en ligne: 24 mars 2018

Pages: 337 - 345

Reçu: 28 août 2017

Accepté: 22 févr. 2018

DOI: https://doi.org/10.2478/raon-2018-0013

© 2018 Vencel Somai, David Legrady, Gabor Tolnai, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.