Minimum number of scans for collagen fibre direction estimation using Magic Angle Directional Imaging (MADI) with a priori information
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Published version
Author(s)
Lanz, Harry
Ristic, Mihailo
Chappell, Karyn
McGinley, John
Type
Journal Article
Abstract
Tissues such as tendons, ligaments, articular cartilage, and menisci contain significant amounts of organised collagen which gives rise to the Magic Angle effect during magnetic resonance imaging (MRI). The MR intensity response of these tissues is dependent on the angle between the main field, B0, and the direction of the collagen fibres. Our previous work showed that by acquiring scans at as few as 7–9 different field orientations, depending on signal to noise ratio (SNR), the tissue microstructure can be deduced from the intensity variations across the set of scans. Previously our Magic Angle Directional Imaging (MADI) technique used rigid registration and manual final alignment, and did not assume any knowledge of the target anatomy being scanned. In the present work, fully automatic soft registration is incorporated into the MADI workflow and a priori knowledge of the target anatomy is used to reduce the required number of scans. Simulation studies were performed to assess how many scans are theoretically necessary. These findings were then applied to MRI data from a caprine knee specimen. Simulations suggested that using 3 scans might be sufficient, but in practice 4 scans were necessary to achieve high accuracy. 5 scans only offered marginal gains over 4 scans. A 15 scan dataset was used as a gold standard for quantitative voxel-to-voxel comparison of computed fibre directions, qualitative comparison of collagen tractography plots are also presented. The results are also encouraging at low SNR values, showing robustness of the method and applicability at low field.
Date Issued
2023-03
Date Acceptance
2022-12-14
Citation
Array, 2023, 17, pp.1-10
ISSN
2590-0056
Publisher
Elsevier BV
Start Page
1
End Page
10
Journal / Book Title
Array
Volume
17
Copyright Statement
2590-0056/© 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.sciencedirect.com/science/article/pii/S2590005622001060
Publication Status
Published
Article Number
100273
Date Publish Online
2023-12-23