Jürgen Scheins

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Use of iterative algorithms to reconstruct three-dimensional (3-D) positron emission tomography (PET) data requires the computation of the system probability matrix. The pure geometrical contribution can easily be approximated by the length-of-intersection (LOI) between lines-of-response (LOR) and individual voxels. However, more accurate geometrical(More)
A prototype of a new bimodal scanner was installed in our laboratory. This scanner combines magnetic resonance imaging (MRI) and positron emission tomography (PET) for brain studies. As the PET detector is located within the bore of the MRI scanner, simultaneous measurements become possible. The MR-component consists of a commercial 3T MRI scanner MAGNETOM(More)
For iterative, fully 3D positron emission tomography (PET) image reconstruction intrinsic symmetries can be used to significantly reduce the size of the system matrix. The precalculation and beneficial memory-resident storage of all nonzero system matrix elements is possible where sufficient compression exists. Thus, reconstruction times can be minimized(More)
In hybrid magnetic resonance-positron emission tomography (MR-PET) studies with the Siemens 3T MR-BrainPET scanner an instantaneous reduction of the PET sensitivity was observed during execution of certain MR sequences. This interference was investigated in detail with custom-made as well as standard clinical MR sequences. The radio-frequency pulses, the(More)
Positron Emission Tomography (PET) images are prone to motion artefacts due to the long acquisition time of PET measurements. Recently, simultaneous magnetic resonance imaging (MRI) and PET have become available in the first generation of Hybrid MR-PET scanners. In this work, the elimination of artefacts due to head motion in PET neuroimages is achieved by(More)
The Maximum Entropy criterion can be utilised for tomographic reconstruction of two-dimensional distributions from a set of one-dimensional projection profiles. In terms of entropy the reconstructed distributions represent the most probable solution which reproduces the experimental input data. Therefore the Maximum Entropy (MENT) algorithm is especially(More)
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