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General Information

Full Name Simone Saitta, PhD
Position Assistant Professor in Cardiovascular Image Analysis
Affiliation Amsterdam UMC & University of Amsterdam — qurAI group & CARA Lab
Email s.saitta@amsterdamumc.nl
Languages Italian (native), English (full professional; TOEFL iBT 107)

Profile

  • I develop data-driven methods to make cardiovascular imaging more quantitative and objective. My work brings together deep learning, computational modeling, physics, and image analysis to support diagnosis, treatment planning, and image-guided cardiovascular procedures.

Experience

  • 2024 - now
    Assistant Professor in Cardiovascular Image Analysis
    Amsterdam UMC — Dept. of Biomedical Engineering & Physics, The Netherlands
    • Manager of the CARA Lab (a joint initiative of Amsterdam UMC, Radboudumc and Abbott Vascular) — development and clinical validation of AI systems that analyse intracoronary optical coherence tomography (OCT) in real time during procedures in the catheterization laboratory.
    • Lead an independent research line on Marfan syndrome, combining medical imaging, biomechanics and machine learning to quantify a patient's risk of aortic-disease progression.
    • Deep learning for coronary plaque characterisation and detection of thin-cap fibroatheromas, and geometric deep learning as a fast surrogate for blood-flow simulation.
    • Visiting Researcher, Diagnostic Image Analysis Group (DIAG), Radboudumc.
  • 2023 - 2024
    Junior Assistant Professor / Postdoctoral Research Fellow
    Politecnico di Milano, Italy
    • Graph neural networks as fast surrogates for cardiovascular biomechanics, turning simulations that normally take hours into near-instant predictions.
    • Co-supervised two doctoral candidates.
  • 2019 - 2023
    Doctoral Researcher in Bioengineering
    Politecnico di Milano, Italy
    • Deep neural networks for automatic 3D segmentation of medical images (CT, MRI and 3D transesophageal echocardiography).
    • Mixed-reality system for neurosurgical guidance (MICCAI 2022; led to a filed patent).
    • Unsupervised reconstruction of moving 3D blood-flow fields from MRI (implicit neural representations).
    • 4D variational data assimilation for cerebral-aneurysm haemodynamics.
  • 2022
    Visiting Doctoral Researcher
    University of Cambridge (DAMTP) & Cambridge Image Analysis Group, UK
    • Inverse problems in imaging — unsupervised denoising and super-resolution of flow-encoded MRI (host: Prof. Carola-Bibiane Schönlieb).
  • 2018 - 2019
    Research Engineer
    National University of Singapore
    • Multi-view 4D image registration for fetal heart echocardiography (host: Prof. C.H. Yap).
  • 2018
    Research Assistant
    Imperial College London, UK
    • Non-invasive pressure estimation from MRI; computational fluid dynamics of aortic dissection (host: Prof. X.Y. Xu).

Education

  • 2023
    PhD in Bioengineering (cum laude)
    Politecnico di Milano, Italy
    • Thesis: Data-driven approaches for quantification of complex biomarkers from medical imaging.
    • External reviewers: Prof. Jelmer Wolterink (University of Twente) and Prof. Sara Moccia (Scuola Superiore Sant'Anna).
  • 2018
    MSc in Biomedical Engineering — Biomechanics and Biomaterials (cum laude)
    Politecnico di Milano, Italy
    • Thesis on non-invasive assessment of aortic coarctation from 4D-flow MRI; final year at Imperial College London.
  • 2015
    BSc in Biomedical Engineering
    Politecnico di Milano, Italy

Grants and Awards

  • 2023
    McKusick Fellowship Research Grant
    The Marfan Foundation — $100,000
    • Principal Investigator. Competitive international fellowship on machine learning for markers of aortic dissection in Marfan patients.
  • 2024
    Editor's Choice article
    Medical & Biological Engineering & Computing
    • Riva, Saitta, et al. — left-ventricular diastolic vortex-ring characterisation in ischaemic cardiomyopathy.
  • 2022
    Switch2Product startup competition
    Politecnico di Milano — €30,000
    • Core team member. IPSE-XR — a deep-learning and mixed-reality solution to support neurosurgery.
  • 2023
    PhD awarded cum laude
    Politecnico di Milano

Patents

  • 2023
    Method and system for the improvement of a virtual surgical procedure
    • International (PCT) filing 2023; US national phase 2026 (App. 19/102,976).
  • 2023
    System and method for the morpho-functional evaluation of the thoracic vascular district
    • National filing.

Supervision

  • 2024
    Pascalle Wijntjes (postdoc) — Neural surrogates for risk stratification in Marfan syndrome · Amsterdam UMC
  • 2023 - 27
    Ruben van der Waerden (PhD) — AI-based assessment of intracoronary OCT · Radboudumc
  • 2023 - 27
    Thijs Luttikholt (PhD) — Detecting rupture-prone plaque features from OCT · Radboudumc
  • 2023 - 27
    Pierandrea Cancian (PhD) — Detection and correction of imaging artefacts in OCT · Amsterdam UMC
  • 2023 - 27
    Joske van der Zande (PhD) — OCT-guided coronary revascularisation · Radboudumc
  • 2025 - 29
    Leah Heil (PhD) — AI for 3D coronary-artery-tree modelling and OCT co-registration · Radboudumc
  • 2024 - 28
    Xiaojin Gu (PhD) — AI optimisation of coronary stent placement · Amsterdam UMC
  • 2025 - 29
    Ioanna Gogou (PhD) — Non-invasive functional coronary assessment from CT angiography · Amsterdam UMC
  • 2021 - 25
    Guido Nannini (PhD) — Deep learning for coronary-artery-disease risk assessment · Politecnico di Milano
  • 2021 - 25
    Riccardo Munafò (PhD) — Deep learning for 3D echocardiography analysis · Politecnico di Milano
  • MSc & BSc theses
    • Co-supervised six MSc theses (physics-informed neural networks for blood flow; statistical shape modelling of the aorta; mixed-reality neurosurgery; heart-valve imaging) and several BSc theses on deep-learning segmentation.

Publications

  • 33 peer-reviewed publications — journal articles and papers at MICCAI, IEEE ISBI and SPIE Medical Imaging — with 751 citations. Full list on the publications page and on Google Scholar.
  • Selected: AI-based identification of rupture-prone coronary plaques (thin-cap fibroatheromas) and clinical outcomes: the PECTUS-AI study. European Heart Journal (2025).
  • Deep-learning measurement of plaque burden from OCT, validated against intravascular ultrasound. JACC: Cardiovascular Interventions (2025).
  • World of Forms: deformable geometric templates for one-shot 3D surface reconstruction of coronary arteries from CT angiography. Medical Image Analysis (2025).
  • Implicit neural representations for unsupervised denoising and super-resolution of 4D-flow MRI. Computer Methods and Programs in Biomedicine (2024).
  • A CT-based deep-learning system for automatic aortic-root anatomy assessment to plan transcatheter valve implantation (TAVI). Computers in Biology and Medicine (2023).
  • Mixed reality and deep learning for external ventricular drain placement in emergency neurosurgery. MICCAI (2022).
  • Invited book chapter (in preparation) — in Model Validation and Uncertainty Quantification in Biomechanics, Vol. 9 of Elsevier's Biomechanics of Living Organs series.

Invited Talks

  • 2025
    Keynote — Deep learning for image analysis in interventional cardiology
    COLIBRI Focus Workshop on Computational Medicine, University of Graz (one of four keynote speakers)
  • 2023
    Invited talk — Implicit neural representations for 4D-flow MRI
    International Congress on Industrial and Applied Mathematics (ICIAM)
  • 2022
    Invited talk — Haemodynamic assessment in abdominal aortic aneurysm: imaging, simulation and data assimilation
    GIMC–SIMAI
  • Podium presentations
    European Society of Biomechanics (2023), ECCOMAS (2022), European Association of Cardiovascular Imaging (2021)

Academic Service

  • Guest Editor, Computers in Biology and Medicine — special issue on cardiovascular digital twins (2025); co-authored the accompanying editorial.
  • Organiser of a special session at the 19th Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE), Vancouver, 2024.
  • Invited external opponent at a doctoral half-time review, Karolinska Institutet (Sweden); regular reviewer of PhD and MSc theses and doctoral-defence committee member.
  • Peer reviewer for IEEE Transactions on Medical Imaging, Medical Image Analysis, Computers in Biology and Medicine, Computer Methods and Programs in Biomedicine, and the MICCAI and IEEE ISBI conferences.

Teaching

  • 2020 - 24
    Teaching Assistant — Biomechanics (3rd-year BSc core course)
    Politecnico di Milano — tutorials, group projects and examinations
  • 2019
    Tutor — Computer Science I (C language)
    Politecnico di Milano — tutorials
  • 2027
    Course teaching at the University of Amsterdam / Amsterdam UMC

Open-source Software

  • flow4D — open-source Python toolkit for processing 4D-flow MRI and generating patient-specific inputs for blood-flow simulation; adopted internationally by cardiovascular-modelling groups.
  • INRs-4DFlowMRI — reference implementation of implicit neural representations for 4D-flow MRI (published in Computer Methods and Programs in Biomedicine).

Technical Skills

  • Programming: Python (expert), MATLAB, C; C++ (working knowledge).
  • Machine learning: PyTorch; geometric deep learning, implicit neural representations, self- and semi-supervised learning, generative models.
  • Medical imaging: ITK/SimpleITK, VTK, elastix, 3D Slicer, DICOM pipelines; CT, MRI, 4D-flow MRI, 3D/4D echocardiography, intracoronary OCT.
  • Scientific computing: FEniCS (physics-constrained optimisation, adjoint solvers), inverse problems, data assimilation.
  • Flow simulation: Ansys, Star-CCM+, OpenFOAM, SimVascular, CRIMSON.
  • Infrastructure: Docker, Singularity, GPU-accelerated high-performance computing, Git.

Research Interests

  • Cardiovascular image analysis
    • Coronary OCT and CT angiography
    • Deep learning for segmentation and characterization
    • Image-guided cardiac interventions
  • Machine learning
    • Geometric and graph deep learning
    • Implicit neural representations
    • Surrogate modelling
  • Computational modelling
    • Computational fluid dynamics
    • Data assimilation

Other Interests

  • Football (soccer) ⚽, Vipassana meditation 🧘