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Accuracy of collagen fibre estimation under noise using directional MR imaging
In tissues containing significant amounts of organised collagen, such as tendons, ligaments, menisci and articular cartilage, MR imaging exhibits a strong signal intensity variation caused by the angle between the collagen fibres and the magnetic field. By obtaining scans at different field orientat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721590/ https://www.ncbi.nlm.nih.gov/pubmed/33069034 http://dx.doi.org/10.1016/j.compmedimag.2020.101796 |
Sumario: | In tissues containing significant amounts of organised collagen, such as tendons, ligaments, menisci and articular cartilage, MR imaging exhibits a strong signal intensity variation caused by the angle between the collagen fibres and the magnetic field. By obtaining scans at different field orientations it is possible to determine the unknown fibre orientations and to deduce the underlying tissue microstructure. Our previous work demonstrated how this method can detect ligament injuries and maturity-related changes in collagen fibre structures. Practical application in human diagnostics will demand minimisation of scanning time and likely use of open low-field scanners that can allow re-orienting of the main field. This paper analyses the performance of collage fibre estimation for various image SNR values, and in relation to key parameters including number of scanning directions and parameters of the reconstruction algorithm. The analysis involved Monte Carlo simulation studies which provided benchmark performance measures, and studies using MR images of caprine knee samples with increasing levels of synthetic added noise. Tractography plots in the form of streamlines were performed, and an Alignment Index (AI) was employed as a measure of the detected orientation distribution. The results are highly encouraging, showing high accuracy and robustness even for low image SNR values. |
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