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Publication Detail
An Automated and High Precision Quantitative Analysis of the ACR Phantom
  • Publication Type:
    Conference
  • Authors:
    Markiewicz PJ, Da Costa-Luis C, Dickson J, Barnes A, Krokos G, MacKewn J, Clark T, Wimberley C, MacNaught G, Yaqub MM, Gispert JD, Hutton BF, Marsden P, Hammers A, Reader AJ, Ourselin S, Herholz K, Matthews JC, Barkhof F
  • Publication date:
    09/09/2022
  • Published proceedings:
    2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors, RTSD 2022
  • ISBN-13:
    9781665421133
  • Status:
    Published
  • Name of conference:
    2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
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.
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