Margarita Panagiotidou


PhD student in novel image reconstruction techniques with application to proton radiotherapy for optimisation of cancer treatment
BSc (Hons), MSc, DIC

My research project

University roles and responsibilities

  • PhD student in Novel image reconstruction techniques with application to proton radiotherapy for optimisation of cancer treatment
  • Teaching assistant/ Lab Demonstrator
  • Mentor in PGRs (Transitions Mentoring scheme)
  • Exam invigilator

    My qualifications

    Sept 2010 - Dec 2015
    BSc (Hons) Physics
    Aristotle University of Thessaloniki
    Oct 2016 - Oct 2017
    MSc Biomedical Engineering with Medical Physics
    DIC
    Imperial College London

    My teaching

    My publications

    Publications

    M Panagiotidou ; CA Collins-Fekete ; P Evans ; N Dikaios (2020). Proton Computed Tomography: A Case Study for Optimal Data Acquisition
    View abstract View full publication
    This study enables a comparison between two proposed algorithms in the literature for proton computed tomography (pCT) reconstruction; these are the analytical filtered back-projection (FBP) and the iterative algorithm of diagonally relaxed orthogonal projections (DROP) with total variation superiorization (TVS). The analytical model of the cubic spline path (CSP) has been implemented into DROP-TVS algorithm to account for scattering, using measurements of individual proton positions/trajectories and energy before and after traversing a selected phantom. Spatial resolution and relative stopping power (RSP) accuracy of the reconstructed images were used as comparison criteria. Parametric changes of projection angular ranges from 0 to 180 degrees and 0 to 360 degrees , as well as angular steps of 0.5 and 1 degrees have been investigated. Experimental data of the CT0404 "Sensitom" and the CTP528 "Line Pair" modules of the Catphan® 600 phantom were used. DROP-TVS appeared to provide better noise reduction compared to FBP while the resolution worsened with reduced projections. On the other hand, FBP resulted in degraded image quality with more noise and worse spatial resolution compared to that of the DROP-TVS scheme. Interplane artifacts were present with both algorithms which became more acute in FBP reconstructions and with a limited number of projections. Reduced number of projections had an adverse effect on both reconstruction methods. The study outcomes highlight the optimal acquisition for pCT reconstruction for superior image quality but there must always be a trade-off between noise and spatial resolution for optimal data acquisition.
    Margarita Panagiotidou ; Philip Evans ; Nikolaos Dikaios (2020). Integration of Proton Computed Tomography into the Open Source Software STIR
    View abstract View full publication
    Proton computed tomography (pCT) offers unique image formation attributes, with a potential for increasing accuracy of treatment planning in proton beam therapy. To maximize the potential of pCT it is necessary to develop advanced reconstruction algorithms that can accurately recover relative proton stopping power maps. This study aims to integrate pCT into STIR (Software for Tomographic Image Reconstruction), a popular Multi-Platform Object-Oriented framework for reconstruction in tomographic imaging to benefit from its software infrastructure. Open source STIR library is currently suitable for reconstructing and manipulating data from Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT), which are based on cylindrical scanner geometries. Although pCT has a noncylindrical geometry, STIR provides the framework for single event detection and modelling of the proton interactions. This initial implementation includes classes and functions with new features such as general proton scanner geometry, binning of list mode proton data into sinograms and uses analytical reconstruction algorithms already available in STIR. The structure of the new implemented features is discussed. Future work will include additional components to establish STIR as a potential toolkit for pCT image reconstruction.