Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.584816
Title: Quantitative ultrasound image analysis of the gastrocnemius muscle for injury evaluation (a pilot study)
Author: Alqahtani, Mahdi
Awarding Body: Cardiff University
Current Institution: Cardiff University
Date of Award: 2010
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Abstract:
The aim of this study is to develop a non-invasive method based on quantitative ultrasound image analysis for the evaluation of muscle injury. The method needs to be sufficiently sensitive to detect possible changes in the muscle in order to monitor muscle injury repair and assist in gauging efficacy of treatment modalities. The gastrocnemius muscle was used to develop the method as this muscle constitutes a typical site for muscle injury. A three dimensional ultrasound sweep was performed on the gastrocnemius muscle of 25 healthy subjects and 5 patients with injured muscle using a 3D linear array transducer. Four slices were extracted from the 3D data set from the middle part of the muscle at different sites. Texture parameters include gray level, variance, skewness, kurtosis, co-occurrence matrix; run length matrix, gradient, autoregressive (AR) model and wavelet transform were extracted from the images. The coefficient of variation (CV) and intra-class correlation coefficient (ICC) were calculated for each texture parameter and used to test repeatability and reproducibility. The effect of varying the gain and the dynamic range setting on the texture features were also investigated. Four texture parameters were then used to obtain a reference set for normal gastrocnemius muscle. The four parameters were tested to ensure that there was no effect from the varying depth or size of ROI. These parameters were then tested against abnormal muscle. The texture parameters AR model and gradient were found to be the most sensitive parameters for differentiating healthy muscle from injured muscle and may be used as a tool to monitor the healing process.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.584816  DOI: Not available
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