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An Automated and High Precision Quantitative Analysis of the ACR Phantom

Markiewicz, PJ; Da Costa-Luis, C; Dickson, J; Barnes, A; Krokos, G; MacKewn, J; Clark, T; ... Barkhof, F; + view all (2022) An Automated and High Precision Quantitative Analysis of the ACR Phantom. In: 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE: Piscataway, NJ, USA. Green open access

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Abstract

A novel phantom-imaging platform for automated and high precision imaging of the American College of Radiology (ACR) PET phantom is proposed. The platform facilitates the generation of an accurate μ-map for PET/MR systems with a robust alignment based on two-stage image registration using specifically designed PET templates. The automated analysis of PET images uses a set of granular composite volume of interest (VOI) templates in a 0.5 mm resolution grid for sampling of the system response to the insert step functions. The impact of the activity outside the field of view (FOV) was evaluated using two acquisitions of 30 minutes each, with and without the activity outside the FOV. Iterative image reconstruction was employed with and without modelled shift-invariant point spread function (PSF) and varying ordered subsets expectation maximisation (OSEM) iterations. Uncertainty analysis of all image-derived statistics was performed using bootstrap resampling of the list-mode data. We found that the activity outside the FOV can adversely affect the imaging planes close to the edge of the axial FOV, reducing the contrast, background uniformity and overall quantitative accuracy. The PSF had a positive impact on contrast recovery (although it slows convergence). The proposed platform may be helpful in a more informative evaluation of PET systems and image reconstruction methods.

Type: Proceedings paper
Title: An Automated and High Precision Quantitative Analysis of the ACR Phantom
Event: 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
Dates: 16 Oct 2021 - 23 Oct 2021
ISBN-13: 9781665421133
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/NSS/MIC44867.2021.9875631
Publisher version: http://dx.doi.org/10.1109/nss/mic44867.2021.987563...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Image registration, Uncertainty, Image resolution, Statistical analysis,Image edge detection, Phantoms, Radiology
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10187344
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