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Improved identification of abdominal aortic aneurysm using the Kernelized Expectation Maximization algorithm.
Deidda, Daniel; Akerele, Mercy I; Aykroyd, Robert G; Dweck, Marc R; Ferreira, Kelley; Forsythe, Rachael O; Heetun, Warda; Newby, David E; Syed, Maaz; Tsoumpas, Charalampos.
Affiliation
  • Deidda D; National Physical Laboratory, Teddington, UK.
  • Akerele MI; Biomedical Imaging Science Department, University of Leeds, Leeds, UK.
  • Aykroyd RG; Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
  • Dweck MR; Department of Statistics, University of Leeds, Leeds, UK.
  • Ferreira K; Edinburgh Imaging Facility, Queen's Medical Research Institute, Edinburgh, UK.
  • Forsythe RO; British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
  • Heetun W; National Physical Laboratory, Teddington, UK.
  • Newby DE; Edinburgh Imaging Facility, Queen's Medical Research Institute, Edinburgh, UK.
  • Syed M; British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
  • Tsoumpas C; National Physical Laboratory, Teddington, UK.
Philos Trans A Math Phys Eng Sci ; 379(2200): 20200201, 2021 Jun 28.
Article in En | MEDLINE | ID: mdl-33966459
ABSTRACT
Abdominal aortic aneurysm (AAA) monitoring and risk of rupture is currently assumed to be correlated with the aneurysm diameter. Aneurysm growth, however, has been demonstrated to be unpredictable. Using PET to measure uptake of [18F]-NaF in calcified lesions of the abdominal aorta has been shown to be useful for identifying AAA and to predict its growth. The PET low spatial resolution, however, can affect the accuracy of the diagnosis. Advanced edge-preserving reconstruction algorithms can overcome this issue. The kernel method has been demonstrated to provide noise suppression while retaining emission and edge information. Nevertheless, these findings were obtained using simulations, phantoms and a limited amount of patient data. In this study, the authors aim to investigate the usefulness of the anatomically guided kernelized expectation maximization (KEM) and the hybrid KEM (HKEM) methods and to judge the statistical significance of the related improvements. Sixty-one datasets of patients with AAA and 11 from control patients were reconstructed with ordered subsets expectation maximization (OSEM), HKEM and KEM and the analysis was carried out using the target-to-blood-pool ratio, and a series of statistical tests. The results show that all algorithms have similar diagnostic power, but HKEM and KEM can significantly recover uptake of lesions and improve the accuracy of the diagnosis by up to 22% compared to OSEM. The same improvements are likely to be obtained in clinical applications based on the quantification of small lesions, like for example cancer. This article is part of the theme issue 'Synergistic tomographic image reconstruction part 1'.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Image Interpretation, Computer-Assisted / Aortic Aneurysm, Abdominal / Positron Emission Tomography Computed Tomography Type of study: Diagnostic_studies / Evaluation_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Aged / Aged80 / Humans Language: En Journal: Philos Trans A Math Phys Eng Sci Journal subject: BIOFISICA / ENGENHARIA BIOMEDICA Year: 2021 Document type: Article Affiliation country: Reino Unido

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Image Interpretation, Computer-Assisted / Aortic Aneurysm, Abdominal / Positron Emission Tomography Computed Tomography Type of study: Diagnostic_studies / Evaluation_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Aged / Aged80 / Humans Language: En Journal: Philos Trans A Math Phys Eng Sci Journal subject: BIOFISICA / ENGENHARIA BIOMEDICA Year: 2021 Document type: Article Affiliation country: Reino Unido