Musculoskeletal ultrasound (US) is an imaging modality with the potential to revolutionise the way we practise rheumatology. Ultrasound has been shown to be more sensitive and valid when compared to conventional imaging methods and clinical examination in the assessment of inflammatory arthropathies (lA). However, major limitations to its widespread use have been its perceived poor reliability and unknown diagnostic discriminative ability. Image acquisition is a key point for improving reliability. A systematic literature review was performed and a survey of experts was obtained in order to understand the important confounding factors in image acquisition. The effect of two of these factors on synovitis detection was then examined. Non-steroidal anti-inflammatory drugs (NSAIDs) usage was found to significantly mask both grey scale (GS) and power Doppler (PD) signal and resultedinlower assessment of disease activity. Change in the joint position during scanning was found to significantly alter US findings in patients with lA. Scanning the hands in a flat position and the knees in a 30° position provided the highest GS and PD scores suggesting that they may be the optimal scanning positions. The ability of US to discriminate rheumatoid arthritis (RA) from other diseases by detecting bone erosions was explored by examining the specificity of US detected bone erosion. The overall presence of US detected erosions was not a specific fmding for RA. However, erosions in certain anatomical sites were specific for RA. In conclusion, this thesis has demonstrated that certain factors. including concurrent NSAID intake and joint position can affect US image acquisition. Ultrasound does / IV have the ability to discriminate RA from other diseases by specifically detecting bone erosions in target joints. Standardisation of the US confounding factors and better understanding of the specificity of common US findings may enhance the role of US in the assessment of IA.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:574628 |
| Date | January 2011 |
| Creators | Zayat, Ahmed Salem |
| Publisher | University of Leeds |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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