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)
The Siemens 3T MR-BrainPET scanner allows us to simultaneously acquire high-resolution MR and PET images thus giving a strong asset for studies of the human brain. Meanwhile, the system is routinely used for MR-PET studies with a variety of radiotra-cers, e.g. vendors' sinogram-based reconstruction, quantitative dynamic images are obtained. However, this(More)
Fast PET image reconstruction algorithms usually use a Line-of-Response (LOR) pre-processing step where the detected raw LOR data are interpolated either to evenly spaced sinogram projection bins or alternatively to a generic projection space as for example proposed by the PET Reconstruction Software Toolkit (PRESTO) [1]. In this way, speed-optimised,(More)
This work focuses on the study of simultaneous dynamic MR-PET acquisition in brain tumour patients. MR-based perfusion-weighted imaging (PWI) and PET [18F]-FET are dynamic methods, which allow to evaluate tumour metabolism in a quantitative way. In both methods, arterial input function (AIF) is necessary for quantification. However, the AIF estimation is a(More)
Dynamic PET provides temporal information about tracer uptake. However, each PET frame has usually low statistics, resulting in noisy images. The goal is to study effects of prior regularisation on dynamic PET data. Quantification and noise in image-domain and time-domain as well as impact on parametric images is assessed. Dynamic PET data for the Siemens(More)