An Object-Oriented Library for 3D PET Reconstruction Using Parallel Computing

Labbé, Claire; Thielemans, Kris; Belluzzo, D.; Bettinardi, Valentino; Gilardi, Maria Carla; Hague, D. S.; Jacobson, M.; Kaiser, Stefan; Levkovitz, R; Margalit, Tamar; Mitra, Gautam; Morel, C.; Spinks, T.J.; Valente, Patrick; Zaidi, Habib; Zverovich, Alexey

doi:10.1007/978-3-642-60125-5_50

Cited by 18 publications

(16 citation statements)

References 11 publications

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“…All the reconstructions used in this study were implemented using the object-oriented software library PARAPET/STIR [20], [21].…”

Section: Methodsmentioning

confidence: 99%

“…Consider the simple case of an object that is constant in the neighborhood of the pixel of interest and 0 elsewhere, and for simplicity take and constant in the neighborhood of the pixel. Under these conditions, (15) can be approximated by (19) with (20) where we used (16) and (18). Consider now the case that the filter is a shift-invariant smoothing operation.…”

Section: B Dependency Of Llir On the Surrounding Activity For Emsmentioning

confidence: 99%

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Object Dependency of Resolution in Reconstruction Algorithms With Interiteration Filtering Applied to PET Data

Mustafovic

Thielemans

2004

IEEE Trans. Med. Imaging

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In this paper, we study the resolution properties of those algorithms where a filtering step is applied after every iteration. As concrete examples we take filtered preconditioned gradient descent algorithms for the Poisson log likelihood for PET emission data. For nonlinear estimators, resolution can be characterized in terms of the linearized local impulse response (LLIR). We provide analytic approximations for the LLIR for the class of algorithms mentioned above. Our expressions clearly show that when interiteration filtering (with linear filters) is used, the resolution properties are, in most cases, spatially varying, object dependent and asymmetric. These nonuniformities are solely due to the interaction between the filtering step and the Poisson noise model. This situation is similar to penalized likelihood reconstructions as studied previously in the literature. In contrast, nonregularized and postfiltered maximum-likelihood expectation maximization (MLEM) produce images with nearly "perfect" uniform resolution when convergence is reached. We use the analytic expressions for the LLIR to propose three different approaches to obtain nearly object independent and uniform resolution. Two of them are based on calculating filter coefficients on a pixel basis, whereas the third one chooses an appropriate preconditioner. These three approaches are tested on simulated data for the filtered MLEM algorithm or the filtered separable paraboloidal surrogates algorithm. The evaluation confirms that images obtained using our proposed regularization methods have nearly object independent and uniform resolution.

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“…All the reconstructions used in this study were implemented using the object-oriented software library PARAPET/STIR [20], [21].…”

Section: Methodsmentioning

confidence: 99%

Section: B Dependency Of Llir On the Surrounding Activity For Emsmentioning

confidence: 99%

Object Dependency of Resolution in Reconstruction Algorithms With Interiteration Filtering Applied to PET Data

Mustafovic

Thielemans

2004

IEEE Trans. Med. Imaging

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“…The algorithm is based on Software for Tomographic Image Reconstruction (STIR), 1 an object-oriented Open Source software consisting of classes, functions and utilities for 3D PET image reconstruction [16].…”

Section: Image Reconstructionmentioning

confidence: 99%

Image reconstruction for the ClearPET™ Neuro

Weber

Morel

Simon

et al. 2006

Nuclear Instruments and Methods in Physics Research Section A:

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“…General software packages, e.g. STIR [1], usually use sinograms as interface to the implemented reconstruction algorithms. However, in case of irregular scanner structures the sinogram format provides no optimal geometrical data description.…”

Section: Introductionmentioning

confidence: 99%

Virtual scanner geometry for iterative 3D PET reconstruction adaptable to complex and irregular scanner topologies

Scheins

Axer

Pietrzyk

et al. 2007

2007 IEEE Nuclear Science Symposium Conference Record

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We have developed a new method to transform any kind of 3D PET scanner data to a regular cylinder structure which consists of virtual crystals arranged in regular rings without any gaps. In this way, the 3D projection data can be stored without introducing geometrical mismatches with respect to the assigned projectors of the iterative reconstruction. In addition, the physical crystal sizes of the scanner can be taken into accout during the data conversion procedure. The conversion of all measured Lines-of-Response (LOR) to a virtual, regular cylinder geometry allows to formalise the geometrical transcription while simultaneously minimising the loss of information, e.g. arc corrections can be omitted completely. Finally, the regularity of the converted projection data allows a very fast and accurate implementation of the reconstruction algorithm which is based on a memory-resident compressed system response matrix. The geometrical accuracy of the applied projectors depends only on the user-defined granularity of the virtual geometry which can be adjusted as a compromise between operating expense and precision. The ClearPET Neuro Scanner [2] with its rotating gantry and the crystal double-layer design has been used for a first application of the new universal 3D reconstruction technique. The presented results indicate the high potential of the new conversion method in terms of achieveable contrast-noise ratio and resolution.

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An Object-Oriented Library for 3D PET Reconstruction Using Parallel Computing

Cited by 18 publications

References 11 publications

Object Dependency of Resolution in Reconstruction Algorithms With Interiteration Filtering Applied to PET Data

Object Dependency of Resolution in Reconstruction Algorithms With Interiteration Filtering Applied to PET Data

Image reconstruction for the ClearPET™ Neuro

Virtual scanner geometry for iterative 3D PET reconstruction adaptable to complex and irregular scanner topologies

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