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Mathematical Imaging in Medicine and Neurosciences


This multidisciplinary group aims to construct new tools for Imaging and Image-derived Neurological data analysis, with focus on numerical methods and error estimation. These tool are meant to be applied in MRI, PET, SPECT imaging in clinical research or as a support for clinical practice.

People: S. De Marchi, D. Cecchin, D. Poggiali, C. Campi, F. Marchetti and P. Gallo.

Papers

  1. An automated PET/CT brain segmentation for regional amyloid semi-quantification
    submitted draft, with D. Poggiali, S. de Marchi, L Ruffini, P Caffarra, H. Barthel, O. Sabri, F. Bui, D. Cecchin.
  2. Lissajous sampling and spectral filtering in MPI applications: the reconstruction algorithm for reducing the Gibbs phenomenon
  3. F.Marchetti, S. De Marchi and W. Erb - IEEE Conference Publications - 2017 International Conference on Sampling Theory and Applications (SampTA), 580--584
  4. Spectral filtering for the reduction of the Gibbs phenomenon for polynomial approximation methods on Lissajous curves with applications in MPI
    F.Marchetti, S. De Marchi and W. Erb - Dolomites Res. Notes Approx. 10 (2017), 128--137
  5. Image Reconstruction from Scattered Radon Data by Weighted Positive Definite Kernel Functions
    draft, with A. Iske and G. Santin, Calcolo 55(2) (2018), https://doi.org/10.1007/s10092-018-0247-6.
  6. Kernel-based Image Reconstruction from Scattered Radon Data
    by S. De Marchi, A. Iske and A. Sironi
    Dolomites Res. Notes on Approx. Vol 9 (2016), special issue for the workshop "Kernel-based methods and function approximation", pp. 19-31.
    available as Hamburger Beitraege zur Angewandten Mathematik 2016-11
  7. Analytical and experimental FWHM of a gamma camera: theoretical and practical issues
    D. Cecchin, D. Poggiali, L. Riccardi, P. Turco, F. Bui, S. De Marchi, PeerJ (2015), https://doi.org/10.7717/peerj.722.

Posters

Software

Presentations

  1. D.Poggiali, Resolution of a Gamma camera: Analytical vs Experimental methods, CAE 2013, Pacengo del Garda VR.
  2. D. Poggiali The numb3rs 0f a Br4in, Istituto per le Applicazioni del Calcolo (IAC), CNR, Rome, 04/2016.
  3. D. Poggiali, MS lesion segmentation in MR image: a multithresholding approach, Ca' Foscari VE, 2014.
  4. D. Poggiali, Data Analysis in MRI and PET/MRI Neuroimaging pyconIE2016, Dublin, Ireland. video link

PhD/MD theses

Master theses

Degree theses