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Abstract #3481

Assessment of the repeatability and stability of NODDI diffusion modelling using phantom and in vivo acquisitions.

Mattia Ricchi1,2,3, Aaron Axford3, Jordan McGing3, Ayaka Shinozaki3,4, Kylie Yeung3,5, Sarah Birkhozeler3, Rebecca Mills3, Fulvio Zaccagna6,7, Andrew Lewis3, Oliver Rider3, Damian J. Tyler3,4, Claudia Testa2,8, and James T. Grist3,4,9
1Department of Computer Science, University of Pisa, Pisa, Italy, 2INFN, Division of Bologna, Bologna, Italy, 3Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, United Kingdom, 4Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom, 5Department of Oncology, University of Oxford, Oxford, United Kingdom, 6Department of Radiology, Cambridge University Hospitals, Cambridge, United Kingdom, 7Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom, 8Department of Physics and Astronomy, University of Bologna, Bologna, Italy, 9Department of Radiology, Oxford University Hospitals, Oxford, United Kingdom

Synopsis

Keywords: Diffusion Modeling, Microstructure, NODDI model, time stability and consistency, single centre

Motivation: The NODDI diffusion-MRI model shows promising results in characterising brain microstructure and capturing neurological disease-related changes. However, the NODDI model lacks validation, limiting its clinical application.

Goal(s): The goal is to validate the diffusion MRI NODDI model, assessing its consistency over time and addressing the need for robust methods in clinical research.

Approach: By scanning several times phantoms simulating brain-restricted diffusion and healthy volunteers with the same acquisition protocol, we meticulously assess NODDI's stability over time and in the presence of magnetic gradient coil heating.

Results: The study confirms the NODDI model's exceptional consistency and stability, establishing its credibility for future clinical applications.

Impact: The study confirms the reliability and stability of the NODDI model in assessing brain microstructure over time. This has significant implications for monitoring neurological disease progression and may lead to standardised MRI calibration protocols for collaborative research and clinical applications.

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Keywords