CT is the principal imaging modality used for the pre-operative 3D planning and assessment of total hip arthroplasty (THA). The image quality offered by CT has a radiation penalty to the patient. Higher than necessary radiation exposure is of particular concern when imaging young patients and women of childbearing age, due to the greater risk of radiation-induced cancer in this group. A harmonised low-dose CT protocol is needed, evidenced by the huge variability in the 17 protocols reviewed. The majority of the protocols were incomplete, leading to uncertainty among radiographers when performing the scans. Only three protocols (20%) were optimised for both 'field of view' and image acquisition parameters. 10 protocols (60%) were optimised for 'field of view' only. These protocols included imaging of the relevant landmarks in the bony pelvis in addition to the knees - the reference for femoral anteversion. CT parameters, including the scanner kilovoltage (kV), milliamperage-time product (mAs) and slice thickness, must be optimised with a 'field of view' that includes the relevant bony landmarks. The recommended kV and mAs values were very wide ranging from 100 to 150 and from 100 to 250, respectively. The large variability that exists amongst the CT protocols illustrates the need for a more consistent low-dose CT protocol for the planning of THA. This must provide an optimal balance between image quality and radiation dose to the patient. Current CT scanners do not allow for measurements of functional pelvic orientation and additional upright imaging modalities are needed to augment them.
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http://dx.doi.org/10.1530/EOR-22-0141 | DOI Listing |
Chem Soc Rev
January 2025
Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
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LIGHT Community, Physics Department, Imperial College London SW7 2AZ, UK.
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January 2025
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January 2025
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Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental toughness on athlete engagement, this study utilizes the relevant methods of machine learning to construct a prediction model, so as to find the intrinsic connection between them.
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