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Fast and memory‐efficient Monte Carlo‐based image reconstruction for whole‐body PET
Author(s) -
Zhang Long,
Staelens Steven,
Van Holen Roel,
De Beenhouwer Jan,
Verhaeghe Jeroen,
Kawrakow Iwan,
Vandenberghe Stefaan
Publication year - 2010
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3455287
Subject(s) - scanner , monte carlo method , image quality , computer science , iterative reconstruction , matrix (chemical analysis) , algorithm , pet ct , noise (video) , computational science , computer vision , artificial intelligence , positron emission tomography , image (mathematics) , mathematics , nuclear medicine , materials science , composite material , medicine , statistics
Purpose Several studies have shown the benefit of an accurate system modeling using Monte Carlo techniques. For state‐of‐the‐art whole‐body positron emission tomography (PET) scanners, Monte Carlo‐based image reconstruction is associated with a significant computational cost to calculate the system matrix as well as a large memory capacity to store it. In this article, the authors present a simulation‐reconstruction framework to solve these problems on the Philips Gemini GS PET scanner. Methods A fast, realistic system matrix simulation module was developed using egs_pet, which is an efficient PET simulation code based on EGSnrc. The generated system matrix was then used in a rotator‐based ordered subset expectation maximization (OS‐EM) algorithm, which exploits the rotational symmetry of a cylindrical PET scanner. The system matrix was further compressed by using sparse storage techniques. Results The system matrix simulation took five days on 50 cores of Xeon 2.66 GHz, resulting in a system matrix of 2.01 GB. The entire system matrix could be stored in the main memory of a standard personal computer. The image quality in terms of contrast‐noise trade‐offs was considerably improved compared to a standard OS‐EM algorithm. The image quality was also compared to the clinical software on the scanner using routine parameter settings. The contrast recovery coefficient of small hot spheres and cold spheres was significantly improved. Conclusions The results indicated that the proposed framework could be used for this PET scanner with improved image quality. This method could also be applied to other state‐of‐the‐art whole‐body PET scanners and preclinical PET scanners with a similar shape.