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dc.contributor.authorEiben, B.
dc.contributor.authorChandy, E.
dc.contributor.authorAbravan, Azadeh
dc.contributor.authorRompokos, V.
dc.contributor.authorGrimes, H.
dc.contributor.authorD'Souza, D.
dc.contributor.authorPoynter, A.
dc.contributor.authorvan Herk, Marcel
dc.contributor.authorMcClelland, J. R.
dc.date.accessioned2022-01-11T11:59:53Z
dc.date.available2022-01-11T11:59:53Z
dc.date.issued2021en
dc.identifier.citationEiben B, Chandy E, Abravan A, Rompokos V, Grimes H, D'Souza D, et al. Probabilistic lung tumour target definition from 4DCT data: A motion model based approach. Radiotherapy and Oncology. 2021;161:S731-S3.en
dc.identifier.urihttp://hdl.handle.net/10541/624899
dc.description.abstractPurpose or Objective Respiratory motion is one of the largest sources of uncertainty in RT for thoracic and upper abdominal tumours. 4DCT is commonly used to define an ITV which encompasses the GTV’s shape and motion, however, this modality is prone to artefacts due to irregular breathing. Furthermore, GTV positions as seen in the sorted 4DCT images only represent its position for one or two breath cycles, i.e. when the tumour is in the field of view (FOV) of the scanner detector making it susceptible to outliers resulting in potential geometrical miss. Instead, we propose to use a motion model based target definition. We utilise our unified motion modelling and image registration framework that – instead of generating a fixed number of phase (or amplitude) images – uses the unsorted 4dCT data to generate a single, motion-compensated-reconstruction (MCR) image and a motion model that describes the subject’s internal motion as function of their breathing trace. Hence, a GTV outlined on the MCR can be animated over the entire 4DCT acquisition using the model, from which a probabilistic ITV can be calculated. This makes the target definition less susceptible to outlier breath cycles and irregularities. Materials and Methods 4DCT data from three lung-cancer patients was unsorted according to each slice's time stamp and aligned with the breathing trace, then used to fit a motion model and generate an MCR. An ITV was contoured on the 4DCT data (phase images and maximum-intensity projection) following standard clinical practice (ITV-4DCT), and a corresponding GTV on the MCR (GTV-MCR). The GTV-MCR was transformed by the motion model to predict its position for every time point of the breathing trace. For each voxel, the probability that it belonged to the GTV-MCR was calculated, and this was used to form probabilistic ITVs encompassing all voxels with a probability >10%, >5%, >1%, and >0% (ITV-10%, ITV-5%, ITV-1%, ITV-0%). Results The MCR image quality is visually superior to the standard 4DCT phase images in terms of sharpness and shows no sorting artefacts (fig 1). Fig 2 shows a coronal slice and a close-up of the model generated GTV-MCR probability map and the contour of ITV-4DCT for all three patients. For patient 1 and 2 the ITV-4DCT is smaller than all probabilistic ITVs, and for patient 3 ITV-4DCT is similar in size to ITV-5%, (39.7 and 40.0ccm respectively) but is a different shape to ITV-5% Conclusion We have demonstrated the feasibility of generating probabilistic ITVs from a motion model built on unsorted 4DCT data and the subject’s breathing trace. Our initial results indicate that the widely used ITV-4DCT may not well represent the actual GTV motion, and that the model generated probabilistic ITVs provide a flexible solution for defining more robust and suitable motion encompassing targets.en
dc.language.isoenen
dc.titleProbabilistic lung tumour target definition from 4DCT data: A motion model based approachen
dc.typeMeetings and Proceedingsen
dc.contributor.departmentUniversity College London, Department of Medical Physics and Biomedical Engineering, London,en
dc.identifier.journalRadiotherapy and Oncologyen
dc.description.noteen]


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