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

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.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/3481
Primary Topic
MRI in cancer diagnosis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Kylie Yeung, Damian J. Tyler, Oliver J. Rider, Fulvio Zaccagna et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
MRI in cancer diagnosis
article

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

Kylie Yeung, Damian J. Tyler, Oliver J. Rider, Fulvio Zaccagna, Jordan McGing, James T. Grist, Ayaka Shinozaki, Andrew Lewis, Claudia M. Testa, Aaron Axford, Rebecca Mills, Mattia Ricchi, Sarah Birkhozeler
article en

Abstract

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.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 63%
MRI in cancer diagnosis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.