Dr Alessandro Perelli

aperelli001@dundee.ac.uk

Organisation:

University of Dundee - School of Science and Engineering

Department:

Biomedical Engineering

Homepage:

https://www.dundee.ac.uk/people/alessandro-perelli

Keywords:

Artificial Intelligence, CT, Deep Learning, Image processing, Machine Learning, Novel imaging methods, Oncologic Imaging, Radiology

Interests:

My main research interests are primarily related to computational imaging, and machine learning, in particular for low-dose poly-energetic 3D/4D and spectral X-ray Computed Tomography (CT) reconstruction.
My particular specialism and passionate interests span to randomized linear algebra, stochastic iterative algorithms and variational inference models.
I am dealing with all aspects of the image reconstruction, from the theoretical mathematical modelling, through the algorithm development, to the handling and processing of experimental CT data together with physical modeling such as scattering.
Personal Website

Key Publications:

1) A. Perelli, S. Alfonso Garcia, A. Bousse, J-P. Tasu, N. Efthimiadis, D. Visvikis, “Multi-channel convolutional analysis operator learning for dual-energy CT reconstruction”, Physics in Medicine and Biology, vol. 67, n. 5, 2022 (DOI Link).2) A. Perelli, M. Andersen, “Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition”, Philosophical Transactions of Royal Society A, vol. 379, n. 2200, p. 20200191, 2021 (DOI Link). 3) A. Perelli, M. Lexa, A. Can, M. Davies, “Compressive Computed Tomography Reconstruction through Denoising Approximate Message Passing”, SIAM Journal on Imaging Sciences, vol. 13, n. 4, pp. 1860-1897, 2020 (DOI Link). 4) J. Mason, A. Perelli, W. Nailon, M. Davies, “Quantitative cone-beam CT reconstruction with polyenergetic scatter model fusion”, Physics in Medicine and Biology, 63(22), 2018 (DOI Link).5) G. Hannak, A. Perelli, N. Goertz, G. Matz, M. Davies, “Performance Analysis of Approximate Message Passing for Distributed Compressed Sensing”, IEEE Selected Topics in Signal Processing, vol. 12, no. 5, pp. 857-870, 2018 (DOI Link).6) J. Mason, A. Perelli, W. Nailon, M. Davies, “Polyquant CT: Electron and Mass Density Reconstruction from a Single Polyenergetic Source”, Physics in Medicine and Biology, vol. 62, n. 22, 2017 (DOI Link).

Full list of publications: Google Scholar Link

Collaborators:

Dr. Alexandre Bousse, French National Institute of Health and Medicine (INSERM), Brest, France.
Prof. Martin S. Andersen, Computer Science, Technical University of Denmark (DTU).
Dr. Bill Nailon, Department of Oncology Physics at the Edinburgh Cancer Centre.
Prof. Matthias Ehrhardt, Department of Applied Mathematics, University of Bath.
Prof. Carola Schoenlieb, Department of Applied Mathematics, University of Cambridge.