Stanislas Rapacchi is an MRI physicist and Associate Researcher with 11 years of experience designing and implementing advanced cardiovascular MR techniques across clinical and research settings. He combines deep hands-on expertise in Siemens sequence programming (C++/IDEA), image reconstruction and processing (Matlab, Python, C++), and statistical analysis (R) to translate biophysics and MR sequence innovations from bench to bedside. A tenured CNRS team leader and current CHUV researcher, he focuses on cardiac MRI for metabolic diseases and pioneering low-field cardiac–pulmonary imaging, while mentoring multidisciplinary teams. An active supporter of open-source science, he has contributed to the Gadgetron reconstruction framework to broaden platform compatibility and reconstruction capabilities.
10 years of coding experience
Electrical and Electronics Engineering, Electrical and Electronics Engineering at Concordia University
Master, Medical imaging, physics, Signal processing, image processing, Master, Medical imaging, physics, Signal processing, image processing at Université Claude Bernard Lyon 1
Bachelor of Science (BS), Electrical, Electronics and Communications Engineering, Bachelor of Science (BS), Electrical, Electronics and Communications Engineering at Ecole Normale Supérieure de Cachan
Classe préparatoire aux Grandes Ecoles (CPGE), MPSI, PSI*, Physics, Classe préparatoire aux Grandes Ecoles (CPGE), MPSI, PSI*, Physics at Lycée Champollion
Gadgetron - Medical Image Reconstruction Framework
Role in this project:
Back-end Developer
Contributions:9 commits, 3 PRs, 6 comments in 2 years 8 months
Contributions summary:Stanislas contributed to the Gadgetron framework, primarily focusing on modifications and additions to support the compilation and functionality of the software. Changes include adjustments for Mac OS X compilation, integration with Matlab toolboxes, and the addition of a GenericReconCartesianFFTGadget. These updates demonstrate a focus on extending the framework's capabilities, particularly for processing and analyzing medical image data.
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