Mathematical Imaging in Medicine and Neurosciences

Papers

1. A discrepancy principle for the Landweber iteration based on risk minimization
   F. Benvenuto, C. Campi - Applied Mathematics Letters 96 (2019) 1-6.
2. Automated Definition of Skeletal Disease Burden in Metastatic Prostate Carcinoma: A 3D Analysis of SPECT/CT Images
   F. Fiz, H. Dittmann, C. Campi, M. Weissinger, S. Sahbai, M. Reimold, A. Stenzi, M. Piana, G. Sambuceti, C. la Fougere - Cancers 11 (2019), 869
3. 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.
4. Lissajous sampling and spectral filtering in MPI applications: the reconstruction algorithm for reducing the Gibbs phenomenon
F. Marchetti, S. De Marchi and W. Erb - IEEE Conference Publications - 2017 International Conference on Sampling Theory and Applications (SampTA), 580--584
5. 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
6. Image Reconstruction from Scattered Radon Data by Weighted Positive Definite Kernel Functions
   draft, S. De Marchi, A. Iske and G. Santin, Calcolo 55(2) (2018), https://doi.org/10.1007/s10092-018-0247-6.
7. 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
8. 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.

Presentations

1. F. Marchetti, S. De Marchi, E. Perracchione, Reconstruction of Images with Discontinuities via Variably Scaled Discontinuous Kernels: Applications to MPI, MASCOT2018 - 15TH Meeting On Applied Scientific Computing And Tool Rome, Italia, 2-5/10/2018.
2. D. Poggiali, S. De Marchi, D. Cecchin, A Kinetic Neural Network Approach for Absolute Quantification and Change Detection in Positron Emission Tomography, MASCOT2018 - 15TH Meeting On Applied Scientific Computing And Tool Rome, Italia, 2-5/10/2018.
3. C. Campi, Pattern recognition in X-ray CT and PET images, MASCOT2018 - 15TH Meeting On Applied Scientific Computing And Tool Rome, Italia, 2-5/10/2018.
4. C. Campi, Segmentation of X-ray Computed Tomography images by means of Hough transform, Dipartimento di Scienze Fisiche, Informatiche e Matematiche, University of Modena and Reggio Emilia, 15/05/2018.
5. C. Campi, Generalized Hough transform for segmentation of X-ray Computed Tomography images, Dipartimento di Matematica, University of Roma La Sapienza, 15/01/2018.
6. C. Campi, Image processing for the investigation of glucose metabolism in patients of ALS, SIAM Conference on Imaging Science Bologna, Italia, 5-8/6/2018.
7. C. Campi, A novel machine learning approach to solar flare prediction, SPAN18, Padova, Italy, May 3 - May 4 2018
8. D. Poggiali, Interpolating the Image-Derived Input Function in PET/MRI: a statistical maximization approach, SPAN18, Padova, Italy, May 3 - May 4 2018
9. F. Marchetti, Polynomial and RBF approximation on Lissajous nodes in MPI applications, SPAN18, Padova, Italy, May 3 - May 4 2018
10. S. De Marchi, W. Erb, F. Marchetti, Lissajous sampling and adaptive spectral filtering for the reduction of the Gibbs phenomenon in Magnetic Particle Imaging (MPI), Bernried, 27 Feb. - 3 Mar. 2017
11. D. Poggiali The numb3rs 0f a Br4in, Istituto per le Applicazioni del Calcolo (IAC), CNR, Rome, 04/2016.
12. D. Poggiali, Data Analysis in MRI and PET/MRI Neuroimaging pyconIE2016, Dublin, Ireland. video link
13. S. De Marchi, Kernel-based Image Reconstruction from scattered Radon data by (anisotropic) positive definite functions, Torino, 5/2/2016
14. D. Poggiali, Resolution of a Gamma camera: Analytical vs Experimental methods, CAE 2013, Pacengo del Garda VR.
15. D. Poggiali, MS lesion segmentation in MR image: a multithresholding approach, Ca' Foscari VE, 2014.

Posters

• F. Marchetti, VSDKs for discontinuous functions DRWA18, Alba di Canazei (TN), Italy, Sep 10 - Sep 14 2018.
• F. Marchetti, Spectral filtering for Chebyshev reconstruction algorithms in Magnetic Particle Imaging IWMPI 2018, Hamburg, Germany, Mar 22 - Mar 24 2018
• D. Poggiali, V. Poretto, S. Franciotta, A. Riccardi, M. Margoni, M. Ermani, F. Causin, S. De Marchi, P. Gallo, Cerebellar cortical lesions and cognitive dysfunction in early relapsing remitting multiple sclerosis, ISRRM Italian Chapter, Padova 10-11/05/2018.
• D. Poggiali, D. Cecchin, A. Favaretto, A. Lazzarotto, M. Margoni, A. Riccardi, A.C. Frigo, P. Zucchetta, F. Causin, F. Bui, P. Gallo, 3T-PET/MRI discloses different metabolic states of cortical lesions in Multiple Sclerosis, ECTRIMS 2017
• F. Marchetti Lissajous sampling and spectral filtering in MPI applications: the reconstruction algorithm for reducing the Gibbs phenomenon, SampTA 2017, Tallinn, Estonia
• F. Marchetti Spectral filtering for the resolution of the Gibbs phenomenon in MPI applications, DWCAA16
• D.Poggiali, Resolution of a Gamma camera: Analytical vs Experimental methods , DRWA13, Canazei TN.

PhD/MD theses

• Metabolismo assoluto del 18F-fdg nell'emisfero controlaterale alla lesione neoplastica mediante PET/RM: studio pre e post chirurgico, candidate: Chiara Mezzato, University of Padua, A. Y. 2016-17.
• Postprocessing Neuroimaging methods in MRI and PET/MRI with applications to Multiple Sclerosis and other Neurological diseases.,
  Davide Poggiali (2017). Advisor: E. Pegoraro. Co-advisor: P. Gallo.

Master theses

• Medical image reconstruction using kernel based methods, candidate: Amos Sironi, University of Padua, A. Y. 2010-11.
• Radial basis functions networks for ODEs: application to diabetes and insulin therapy models, candidate: Giulia Antinori, University of Padua, A. Y. 2011-12.
• Reconstruction of medical images from Radon data in trasmission and emission tomography, candidate: Davide Poggiali, University of Padua, A. Y. 2011-12.
• Kernel-based medical image reconstruction, candidate: Maria Angela Narduzzo, University of Padua, A. Y. 2013-14.
• Kernel-based medical image reconstruction from Radon data, candidate: Silvia Guglielmo, University of Padua, A. Y. 2013-14.
• Medical Image Registration for motion detection and correction, candidate: Ada Passarini, University of Padua, A. Y. 2014-15.

Short Degree theses

• Confronto tra i metodi ART e SIRT per la ricostruzione di immagini tomografiche, candidate: Giulia Nalin, University of Padua, A. Y. 2013-14.
• Semi-quantificazione del 123I-DaTSCAN: metodiche a confronto, candidate: Chiara Morbiato, University of Padua, A. Y. 2017-18.

Software

• Resolution calculator (Scilab)
• Lesion Segmentation (Scilab, alpha)