Shahabedin Nabavi (PhD)
Lecturer in Computer Science
Research in Medical Image Computing and Image-guided Radiotherpy
Medical Image Computing
Image-guided Radiotherapy
AI for Healthcare
About
I am a researcher and university lecturer specializing in artificial intelligence, medical image computing, and cardiovascular MRI. I completed my Ph.D. in AI at Shahid Beheshti University with collaborative research at the University of Manchester. My work focuses on deep learning for CMR reconstruction, image quality assessment, radiotherapy optimization, and AI-driven clinical decision support, alongside extensive teaching experience across AI, machine learning, and medical imaging.
Highlights
- 2025: Award — CMRxRecon 2025 Challenge, MICCAI 2025
- 2025: Best Paper Award — AIMIN2025
- 2025: Talk — Deep Learning in CMR Image Reconstruction
Research
CMR Reconstruction
AdaptCMR: spectrally guided mixture‑of‑experts for efficient multi‑contrast reconstruction. Parameter‑efficient decoders with view/contrast specialization.
CMR Registration
FractMorph: A Fractional Fourier-Based Multi-Domain Transformer for Deformable Image Registration
IQA & Domain Adaptation
Robust CMR IQA and adaptation from simulated to real‑world artifacts (motion, Gibbs, aliasing).
DL for dose distribution prediction in Radiation Therapy
A cascade transformer-based model for 3D dose distribution prediction in head and neck cancer radiotherapy
Selected Publications
Medical imaging and computational image analysis in COVID-19 diagnosis: A review — Computers in Biology and Medicine, 2021
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A Generalised Deep Meta-Learning Model for Automated Quality Control of Cardiovascular Magnetic Resonance Images — Computer Methods and Programs in Biomedicine, 2023
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Automated cardiac coverage assessment in cardiovascular magnetic resonance imaging using an explainable recurrent 3D dual‐domain convolutional network — Medical Physics, 2024
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Complete list on Google Scholar.
Talks
- Deep Learning in CMR Image Reconstruction (2025)
- Machine Learning for Automated Radiation Therapy Treatment Planning: Theory and Applications (2023)
- Cardiovascular Magnetic Resonance Image Quality Assessment using Deep Learning (2022)
Teaching
- Computer Vision with Deep Learning
- Medical Image Analysis
- Introduction to AI and ML
Supervision & Mentorship
- Shayan Kebriti — 3D mesh generation for in‑silico trials
- Ali Shokri — Combination of adversarial generative networks and diffusion models to reduce noise in low-dose CT imaging
- Rana Aminipour — Difficult airway identification using facial image analysis in patients under general anesthesia
- Mahan Veisi — Efficient multi‑contrast CMR reconstruction
- Mohammad Hashemi — Deformable Image Registration for 4D CT lung imaging
- Narges Ghassemi — Chaotic Convolutional LSTM Network for Respiratory Motion Prediction
- Kian Anvari — CMR image denoising using physics-informed deep neural networks
- Narges Razizadeh — Motion correction in CMR imaging using a physics-informed deep learning model
- Mahdie Dolatabadi — Predicting pulmonary fibrosis progression using deep learning
- Tara Gheshlaghi — Automated radiotherapy treatment planning using adversarial generative networks
- Hossein Simchi — Domain adaptation for multi-class artefact detection in MR imaging
Service
- Peer reviewer: IEEE TMI, CIBM, Scientific Reports, MICCAI, ...
Contact
Email: s_nabavi@sbu.ac.ir
Affiliation: Faculty of Computer Science and Engineering, Shahid Beheshti University
News
- Nov 2025 — Preparing ISMRM 2026 abstract on AdaptCMR.
- ...