Upright to supine image registration and contour propagation for thoracic patients

A renewed interest in upright therapy is currently driven by the availability of upright positioning and imaging systems. Aside from reduced cost, upright positioning possibly provides clinical advantages. The comparison between upright and supine particle therapy treatments can be biased through multiple variables, such as differences in the target contouring on the two CTs. We present a method for upright and supine CT registration and structures propagation, and the investigation of an AI-based contouring tool for upright images. Six paired 4DCTs from Proton Therapy Collaboration Group registry were available from the Northwestern Medicine Proton Centre. Deformable image registration (DIR) is challenged by the different patient anatomy between postures, causing artefacts in the warped images. To achieve high quality contour propagation, we propose the construction of a region of interest covering the ribcage volume to overcome this problem. As no target contour ground truth was available, the registration quality analysis (QA) was performed on lung structures, for which dice score coefficient (DSC) and average Hausdorff distance (AHD) is reported. The TotalSegmentator tool, trained on supine dataset, was applied on upright images, verified against lung structures and used as additional comparison for contour propagation. The TotalSegmentator QA results in a maximum AHD of 2mm and a minimum DSC of 0.94. An average AHD of 1.5mm and 1.6mm, and an average DSC of 0.95 and 0.94 were obtained comparing the propagated volumes to manually contoured and AI structures, respectively. All AHD values are smaller than the CT slice distances. The developed framework allows for target propagation between upright and supine images, defining the first step to compare upright and supine therapy of thoracic patients and enabling the application of image fusion techniques in the upright therapy field.