2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) 2012
DOI: 10.1109/nssmic.2012.6551777
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Total variation minimization for in-beam PET image reconstruction

Abstract: One of the challenging applications of positron emission tomography (PET) is in-beam PET, which is an in situ dose monitoring method for charged particle therapy. It is known that the activity of positron emitters produced through fragmentation reaction is generally low. Image reconstruction from low count data would require alternative algorithms to suppress noise in images, such as the maximum a posteriori (MAP) reconstruction algorithm, which is often used to improve an image reconstruction problem by intro… Show more

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Cited by 5 publications
(4 citation statements)
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“…The results from [13] indicate that if the MAP penalization parameter for TV is not selected carefully, the algorithm may not lead to a stable solution. This finding was also noted in the work of Kinouchi et al, in which they compared the performances of MLEM with MAP with TV prior on pencil beam [18]. In this paper, MAP-MRP was much less sensitive to the choice of the regularization parameter (see Figs.…”
Section: Discussionsupporting
confidence: 82%
See 2 more Smart Citations
“…The results from [13] indicate that if the MAP penalization parameter for TV is not selected carefully, the algorithm may not lead to a stable solution. This finding was also noted in the work of Kinouchi et al, in which they compared the performances of MLEM with MAP with TV prior on pencil beam [18]. In this paper, MAP-MRP was much less sensitive to the choice of the regularization parameter (see Figs.…”
Section: Discussionsupporting
confidence: 82%
“…The total activity was 5.9 MBq (1 kBq/cm 3 ) 60 s after the delivery of the last field. The dose delivered at the target area was about 2 Gy in total (similar to the dose delivered in [18]), a typical value for a daily fraction of the irradiation treatment. The full ring scanner simulation included 4.28 M true counts.…”
Section: B Realistic Phantom Simulationmentioning
confidence: 90%
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“…The amount of noise can be reduced by regularizing the reconstruction process. Regularization can be obtained by introducing a-priori knowledge on the expected voxel-based MLEM reconstruction (Kinouchi et al 2012) or, alternatively, by applying the MLEM formulation to basis functions that are no longer voxels but pre-defined regions (Lewitt 1992, Matej and Lewitt 1995, Reader and Zaidi 2007, De Bernardi et al 2010.…”
Section: Introductionmentioning
confidence: 99%