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dc.contributor.authorvan Herk, Marcel
dc.contributor.authorPrice, Gareth J
dc.contributor.authorClough, Abigael
dc.contributor.authorSanders, J
dc.contributor.authorFaivre-Finn, Corinne
dc.contributor.authorEccles, Cynthia L
dc.contributor.authorAznar, Marianne Camille
dc.date.accessioned2021-09-30T11:55:58Z
dc.date.available2021-09-30T11:55:58Z
dc.date.issued2021en
dc.identifier.citationvan Herk M, Price G, Clough A, Sanders J, Faivre-Finn C, Eccles C, et al. Efficient Visualisation of Changes in CBCT During Radiotherapy to Detect Lung Infections. Medical Physics. 2021;48(6).en
dc.identifier.urihttp://hdl.handle.net/10541/624576
dc.description.abstractPurpose: Cancer patients are vulnerable to COVID-19 and other infections. Daily CBCT is common for image-guided radiotherapy. To protect patients and staff lung infections must be detected timely, which is possible on daily thoracic CBCT. However, it is time consuming to inspect all slices of a thoracic CBCT, especially for longitudinal changes. Here we develop and optimise a tool for rapid detection lung of infections by visual comparison of daily images projected in 2D. Methods: All daily thoracic CBCTs are processed overnight by a pipeline containing registration, cropping to lungs, filtration, and maximum intensity projection. Parameters were optimised visually using 150 cases. A visual timeline of the treatment is presented in a report and animation. Images are reviewed each morning by a radiographer who flags issues to a clinical oncologist to determine if further action is required. The tool was retrospectively evaluated in 285 patients treated between January and June 2020 and prospectively implemented in a clinical workflow and departmental quality system in July 2020. Results: Rigid registration outperformed deformable registration, the latter being affected by lung lesions. Optimal cropping was the planning CT lungs - 3mm, balancing sensitivity for peripheral lesions and rejection of pleural effusion changes. Blurring (σ=1cm) in AP direction suppressed healthy lung structures while maintaining infection contrast. Report generation took ~60s per case, and inspection ~12s. To date, over 400 patients were prospectively evaluated and 4 asymptomatic patients were diagnosed with COVID-19 based on CBCT imaging changes. A further patient was identified as having a non COVID-19 lung infection on CBCT. Conclusion: We developed and validated a near real-time tool to identify lung density changes on CBCT indicative of COVID-19 or other lung infections. CBCT imaging provides a unique opportunity to study temporal progression of lung infections and protect patients and staff.en
dc.language.isoenen
dc.titleEfficient visualisation of changes in CBCT during radiotherapy to detect lung infectionsen
dc.typeOtheren
dc.contributor.departmentUniv Manchester, Manchester, Lancs, Englanden
dc.identifier.journalMedical Physicsen
dc.description.noteen]


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