Pawel Markiewicz

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Direct reconstruction of parametric images from raw photon counts has been shown to improve the quantitative analysis of dynamic positron emission tomography (PET) data. However it suffers from subject motion which is inevitable during the typical acquisition time of 1-2 hours. In this work we propose a framework to jointly estimate subject head motion and(More)
The combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) offers unique possibilities. In this paper we aim to exploit the high spatial resolution of MRI to enhance the reconstruction of simultaneously acquired PET data. We propose a new prior to incorporate structural side information into a maximum a posteriori(More)
Positron emission tomography (PET) reconstruction is an ill-posed inverse problem which typically involves fitting a high-dimensional forward model of the imaging process to noisy, and sometimes undersampled photon emission data. To improve the image quality, prior information derived from anatomical images of the same subject has been previously used in(More)
The purpose of this work is to provide a software solution which would enable very fast list-mode data processing offered by the massively parallel graphics processor units (GPU) while dealing efficiently with the common bottleneck of data transfers between hard disk storage, host (CPU) memory and device (GPU) memory. This software is dedicated to the(More)
Simultaneous PET and MR acquisitions have now become possible with the new hybrid Biograph Molecular MR (mMR) scanner from Siemens. The purpose of this work is to create a platform for mMR 3D and 4D PET image reconstruction which would be freely accessible to the community as well as fully adjustable in order to obtain optimal images for a given research(More)
In this paper we propose a novel algorithm for jointly performing data based motion correction and direct parametric reconstruction of dynamic PET data. We derive a closed form update for the penalised likelihood maximisation which greatly enhances the algorithm's computational efficiency for practical use. Our algorithm achieves sub-voxel motion correction(More)
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