Daniele Perlo

Daniele Perlo is a Postdoctoral Fellow at Luxembourg Institute of Health specializing in Brain Imaging & Neuro Epidemiology. His research bridges artificial intelligence and medical imaging to advance neuro-oncology diagnostics, with emphasis on brain tumor hypoxia detection and MRI analysis.

His primary research interests include Artificial Intelligence, Deep Learning, Medical Imaging, and Neuroimaging, with specific focus on hypoxia detection in brain tumors using multiparametric MRI. He develops AI models for medical image synthesis, artifact correction, and tumor characterization, translating computational methods into clinical applications for improved cancer diagnostics.

Analysis of Perlo's 15 recent publications (2021-2024) reveals a concentrated research trajectory in AI-driven medical imaging. Key themes include brain tumor hypoxia prediction through MRI-PET synthesis, MRI artifact correction via k-space deep learning, and creation of specialized medical datasets (Unitobrain, Unitochest, Unitopatho). His work demonstrates increasing emphasis on practical clinical implementations, particularly in neuro-oncology and multi-organ cancer imaging, leveraging large-scale deep learning frameworks.

Perlo participated as a key contributor in the "Pseudo PET" research project (2022-2024), collaborating with principal investigators Keunen and Niclou to develop deep learning methods for synthesizing pseudo-PET images from MRI data to assess brain tumor hypoxia without radiation exposure. This HPC-intensive project utilized multiple GPUs for medical image processing in neuro-oncology applications.

He operates within the Brain Imaging & Neuro Epidemiology research group at Luxembourg Institute of Health, focusing on AI applications for neuroimaging analysis and epidemiological studies of brain disorders through advanced computational techniques.