Perelli, Alessandro


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Personal profile


Dr Alessandro Perelli is a Lecturer in Biomedical Engineering at the University of Dundee. 

His main research area is medical image computing with a focus on the development of new deep learning methods for efficient acquisition, reconstruction and analysis of data in clinical applications such as low-dose spectral X-ray Computed Tomography (CT) and fast Magnetic Resonance Imaging (MRI) acquisition.

Previously, he held the following research positions:

  • Research scientist in 2021 at the Laboratory of Medical Imaging at INSERM (France) working on multimodal and spectral Computed Tomographic applications.
  • Marie Curie H. C. Ørsted COFUND Postdoctoral Fellowship from 2018 to 2020 at the Technical University of Denmark on a project for deep and randomized imaging in tomography.
  • Research Associate at the University of Edinburgh (UK) from 2014 to 2018 working on image processing techniques for solving computational imaging problems.

Research interests

Dr Alessandro Perelli aims to solve problems in cutting-edge technologies such 3D/4D CT and spectral CT for medical applications which are becoming pervasive both in the academic and industrial communities.

His research interests and achievements are the following:

  • Develop new deep learning-based algorithms for medical, biological imaging. Key applications include magnetic resonance imaging, computerized tomography, optical microscopy.
  • Exploit optimization and artificial intelligence to analyse imaging data and predict clinical diseases. This includes methods such as image reconstruction, automatic segmentation and classification.
  • Study the trade-off between clinical accuracy and computation in CT and MRI medical applications.
  • Develop deep learning methods for cutting edge technologies in low-dose photon counting detector spectral CT which can potentially enable material decomposition, automatic tumour detection and shape analysis.
  • Enable highly accurate imaging with fast MRI acquisition and temporal reconstruction in application such as cardiac imaging.

More technical information on the works is available here


Dr Alessandro Perelli proposed several new research advancements in medical imaging, disseminating all the results within 40 publications in high quality journals as IEEE Journal of Selected Topics in Signal Processing, Physics in Medicine & Biology and conferences (Google Scholar Link).

He was involved in an industrial project on X-ray CT for explosives detection with dual energy CT in collaboration with GE Global Research.

He collaborated with clinical academics with the Edinburgh Cancer Centre on CT imaging for image-guided radiotherapy treatment.

He is recipient of the following fellowships and grants:
  • Accreditation Associate Fellowship of the HEA (2022)
  • COFUND fellowship under Marie Curie Actions grant no. 713683 (2018 – 2020)
  • Dr.phil Ragna Rask-Nielsen Visit Research Grant (2019)
  • NVIDIA GPU Grant (2016)
  • Italian Ministry of Education PhD scholarship (2010 – 2014)

He was awarded different travel grant such as IMA Workshop on Computational Imaging - Minneapolis (US), IEEE International Ultrasound Symposium (US).

He had invited presentations at the Synergistic Reconstruction Symposium, Chester (2019), British Applied Mathematics Colloquium, University of Oxford (2016), European Signal Conference, EUSIPCO (2015).

He conducted research visits at the University of Bath, University of Cambridge (2019) and University of Leeds (2013).


Dr Alessandro Perelli is the module lead for:

  • Deep Learning for Medical Imaging (4th year Biomedical Engineering)
  • Medical Image Processing and Analysis (MSc Medical Imaging)
  • Digital Image Processing (Joint Educational Program - NEU China)

Additionally, he supports the following modules:

  • Biomedical Signal and Image Processing (3rd year Biomedical Engineering)
  • Advanced Biomedical Imaging Technologies (MSc program)

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being

Education/Academic qualification

Doctor of Philosophy, Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications, University of Bologna

1 Jan 201131 Dec 2013

Award Date: 9 May 2014

External positions


1 Dec 20209 Sept 2021

H.C. Ørsted COFUND Postdoctoral Fellow, Technical University of Denmark

1 Dec 201830 Nov 2020

Postdoctoral Research Associate, University of Edinburgh

1 Sept 201630 Nov 2018

Research Associate, University of Edinburgh

1 Jul 201431 Aug 2016


  • QA75 Electronic computers. Computer science
  • Deep learning
  • Image processing
  • Computer vision
  • TK Electrical engineering. Electronics Nuclear engineering
  • Biomedical image analysis
  • Computed tomography
  • Image reconstruction


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