I have a background in electronics engineering and artificial intelligence, with a professional interest in signal processing, generative modeling, representation learning, and (medical) imaging systems. Currently, I am doing my PhD in computational MRI working on accelerating multi-contrast MR scans, for e.g. by leveraging redundant information across them.
Following is a short overview of my publications. For a more complete account, visit my Google Scholar or LinkedIn.
First-Author Journal Publications
- Rao, van Osch, Pezzotti, de Bresser, van Buchem, Beljaards, Meineke, de Weerdt, Lu, Doneva, Staring. “A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling.” (arXiv 2024) (GitHub)
First-Author Conference Papers
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Jabarimani, Rao, Ercan, Dong, Pezzotti, Doneva, Weerdt, van Osch, Staring, Nagtegaal. “Accelerated FLAIR Imaging at 0.6T using T2W-guided Multi-contrast Deep Learning-based Reconstruction using a Zero-shot Approach.” (ISMRM 2025)
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Rao, Beljaards, van Osch, Doneva, Meineke, Schülke, Pezzotti, de Weerdt, Staring. “Guided Multicontrast Reconstruction based on the Decomposition of Content and Style.” (ISMRM 2024)
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Rao, Meineke, Pezzotti, Staring, van Osch, Doneva. “Analysis of the Discretization Error vs. Estimation Time Tradeoff of MRF Dictionary Matching and the Advantage of the Neural Net-based Approach.” (ISMRM 2023)
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Traverso, Rao, Briassoulli, Dekker, de Ruysscher, van Elmpt. “Generating synthetic hypoxia images from FDG-PET using Generative Adversarial Networks (GANs).” (Radiotherapy and Oncology 2022)
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Rao, Pai, Hadzic, Zhovannik, Bontempi, Dekker, Teuwen, Traverso. “Oropharyngeal Tumour Segmentation using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge.” (Springer LNCS 2021)
Collaborations
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Ilıcak, Rao, Najac, Lena, Imre, Galve, Alonso, Webb, Staring. “Physics-Informed Deep Unrolled Network for Portable MR Image Reconstruction.” (arXiv 2025)
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Gao, Mody, Rao, Dankers, Staring. “On factors that influence deep learning-based dose prediction of head and neck tumors.” (Physics in Medicine & Biology 2025)
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Mody, Chaves-de-Plaza, Rao, Astrenidou, de Ridder, Hoekstra, Hildebrandt, Staring. “Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation.” (Melba 2024)
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Beljaards, Pezzotti, Rao, Doneva, van Osch, Staring. “AI‐based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models.” (Medical Physics 2024)
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Pai, Hadzic, Rao, Zhovannik, Dekker, Traverso, Asteriadis, Hortal. “Frequency-domain-based structure losses for cyclegan-based cone-beam computed tomography translation.” (Sensors 2023)
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Oreiller et al. “Head and neck tumor segmentation in PET/CT: The HECKTOR challenge.” (Medical Image Analysis 2022)