Procrustes analysis of muscle fascicle shapes based on DTI fibre tracking
Diffusion Tensor Imaging (DTI) is a technique developed from Magnetic Resonance Imaging (MRI), which uses a mathematical form diffusion tensor to measure the movement of water molecules in biological tissues in vivo. By performing fibre tracking using diffusion tensor data, we can study the micro-structure of biological tissues in a non-invasive way. Skeletal muscle plays a significant role in force and power generation that contribute to maintaining body postures and to controlling its movements. DTI fibre tracking may re-construct the skeletal muscle in a fascicle level. Procrustes analysis is a landmark-based method for studying the shapes of objects. In this paper, we explore using Generalised Procrustes Analysis to study the fascicle shapes that we have collected in medial gastrocnemius muscles from 6 healthy adults by using DTI technology. This is an innovated attempt of using Procrustes analysis to find the trend of the changes of fascicle shape when foot is in plantarflexion and dorsiflexion, by clustering method.
History
School
- Science
Department
- Mathematical Sciences
- Computer Science
Published in
Medical Image Understanding and Analysis: 26th Annual Conference, MIUA 2022, Cambridge, UK, July 27–29, 2022, ProceedingsPages
172-186Source
Annual Conference on Medical Image Understanding and Analysis (MIUA 2022)Publisher
SpringerVersion
- AM (Accepted Manuscript)
Rights holder
© The Author(s)Publisher statement
This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-12053-4_13. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-termsAcceptance date
2022-07-09Publication date
2022-07-25Copyright date
2022ISBN
978-3-031-12052-7; 978-3-031-12053-4ISSN
0302-9743eISSN
1611-3349Publisher version
Book series
Lecture Notes in Computer Science (LNCS, volume 13413)Language
- en