Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls

Rahmim, Arman; Qi, Jinyi; Sossi, Vesna

doi:10.1118/1.4800806

Cited by 299 publications

(241 citation statements)

References 161 publications

(169 reference statements)

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“…The blurring due to the positron range effect can be approached in different ways [5]. One common way is to describe the detected data as the ideal integral of activity (i.e.…”

Section: Introductionmentioning

confidence: 99%

PET iterative reconstruction incorporating an efficient positron range correction method

et al. 2016

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“…The blurring due to the positron range effect can be approached in different ways [5]. One common way is to describe the detected data as the ideal integral of activity (i.e.…”

Section: Introductionmentioning

confidence: 99%

PET iterative reconstruction incorporating an efficient positron range correction method

et al. 2016

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“…Resolution modeling and compensation techniques have been proposed for over two decades to account for the limitations in localization of radio-tracer distributions. 3 To support the claim that "resolution modeling enhances PET imaging," it is important to clarify the term "enhances." In general, image restoration attempts to improve the image signal and/or reduce noise.…”

Section: Opening Statementmentioning

confidence: 99%

“…3 Unlike postreconstruction partial volume correction (PVC) methods, RM models resolution-degrading phenomena within the reconstruction. It is a natural approach as one aims to design the system matrix to faithfully reproduce the true probabilities of detection, and is an attractive alternative to a range of PVC methods that make simplifying assumptions.…”

Section: Opening Statementmentioning

confidence: 99%

Resolution modeling enhances PET imaging

Alessio

Rahmim

Orton³

2013

Medical Physics

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“…In this case the resolution modelling is done in image space [13], but it can be also modelled in sinogram space or not modelled at all. The benefits and drawbacks of resolution modelling have been discussed in [14], [15]. A more accurate model for the system matrix could be obtained with Monte Carlo simulations, for example using GATE.…”

Section: Methodsmentioning

confidence: 99%

Composite system modelling for high accuracy brain PET image reconstruction using GATE

Belzunce

Reader

2016

2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/R

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Abstract-High resolution and good quantification is needed in specific regions of the brain in a number of PET brain imaging applications. An improvement in the spatial resolution and in the quantification of the tracer uptake can be achieved by using statistical reconstruction methods with an accurate model of the scanner acquisition process. This model is represented by a system response matrix and needs to include all the factors that contribute to the degradation of the reconstructed images. Monte Carlo simulations are the best method to model the complex physical processes involved in PET, but they have an extremely high computational cost and the system matrix needs to be recomputed for every new scan. Furthermore, for 3D PET the system matrix can have billions of elements, therefore at present it is impossible to store in memory during the iterative reconstruction. Consequently, on-the-fly Monte Carlo modelling of the system matrix has been previously proposed by other authors, where a Monte Carlo simulation is used in the forward projector and a simpler analytic model in the backprojector. In this work, we propose a different approach, where a composite system matrix is used, with a complete Monte Carlo model computed with GATE for a small subregion of the field of view and a simpler analytic model for the voxels outside that region. We evaluated the feasibility of the method using 2D simulations of a striatum phantom and a brain phantom. For each case, a Monte Carlo system matrix was generated with GATE for a subregion centred in the striatum. The brain simulations were reconstructed using the proposed method and compared with the standard reconstruction used clinically, with and without resolution modelling. For the striatum phantom, the use of a GATE system matrix showed an improvement of the reconstructed image, where a better definition of the structures in the striatum region was observed. For the case of the brain phantom, where the composite system matrix is used, an improvement was also observed but more limited compared with the pure GATE system matrix.

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Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls

Cited by 299 publications

References 161 publications

PET iterative reconstruction incorporating an efficient positron range correction method

PET iterative reconstruction incorporating an efficient positron range correction method

Resolution modeling enhances PET imaging

Composite system modelling for high accuracy brain PET image reconstruction using GATE

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