胸部患者直立位至仰卧位图像配准与轮廓传播
Upright to supine image registration and contour propagation for thoracic patients
AI总结：
本文提出直立位与仰卧位胸部CT的可变形配准及轮廓传播框架，利用胸廓感兴趣区提升质量，并结合TotalSegmentator验证，实现可靠的跨体位靶区传播。
摘要：
目前，直立定位与成像系统的可用性重新激发了人们对直立治疗的兴趣。除降低成本外，直立定位还可能带来临床优势。直立与仰卧粒子治疗之间的比较可能因多个变量而产生偏差，例如两幅CT上靶区勾画的差异。我们提出了一种用于直立位与仰卧位CT配准及结构传播的方法，并研究了一种用于直立位图像的基于AI的勾画工具。来自Proton Therapy Collaboration Group注册库的六组配对4DCT由Northwestern Medicine Proton Centre提供。可变形图像配准（DIR）受到不同姿势下患者解剖结构差异的挑战，会在形变图像中产生伪影。为实现高质量轮廓传播，我们建议构建一个覆盖胸廓体积的感兴趣区以克服该问题。由于没有可用的靶区轮廓真值，配准质量分析（QA）在肺部结构上进行，并报告了Dice相似系数（DSC）和平均Hausdorff距离（AHD）。在仰卧位数据集上训练的TotalSegmentator工具被应用于直立位图像，针对肺部结构进行验证，并作为轮廓传播的额外比较。TotalSegmentator的QA结果为最大AHD为2 mm、最小DSC为0.94。将传播体积分别与人工勾画结构和AI结构进行比较，得到的平均AHD分别为1.5 mm和1.6 mm，平均DSC分别为0.95和0.94。所有AHD值均小于CT层间距。所开发的框架允许在直立位和仰卧位图像之间进行靶区传播，确定了比较胸部患者直立治疗与仰卧治疗的第一步，并使图像融合技术能够应用于直立治疗领域。
英文摘要：
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.
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