标题： 呼吸差异：通过术前与术中图像融合增强肺部热消融评估 显示英文标题

标题： Respiratory Differencing: Enhancing Pulmonary Thermal Ablation Evaluation Through Pre- and Intra-Operative Image Fusion

摘要： CT图像引导的热消融广泛用于肺癌治疗；然而，随访数据显示，医生对术中图像的主观评估常常高估了消融效果，可能导致治疗不完全。 为了解决这些挑战，我们开发了\textit{呼吸差异}，一种新型的术中CT图像辅助系统，旨在提高消融评估的效果。 该系统首先在术前CT图像中分割肿瘤区域，然后采用多阶段配准过程将其与相应的术中或术后图像对齐，补偿呼吸变形和治疗引起的改变。 该系统提供两种关键输出以帮助医生评估术中消融情况。 首先，通过从术中图像中减去配准后的术前图像生成差分图像，允许直接可视化和定量比较术前和术后差异。 这些差分图像使医生能够在肿瘤在术后图像中不再可见的情况下评估肿瘤和消融区的相对位置，从而改善消融效果的主观评估。 其次，系统提供一个定量指标来衡量肿瘤区域与治疗区之间的差异，提供消融整体效果的数值评估。 这一开创性系统补偿了复杂的肺部变形，并整合了术前和术中成像数据，增强了癌症消融治疗的质量控制。 一项涉及35个临床病例的后续研究显示，我们的系统在识别治疗期间或治疗后立即发现的欠消融病例方面显著优于传统的主观评估，凸显了其在改进临床决策和患者预后方面的潜力。 显示英文摘要

摘要： CT image-guided thermal ablation is widely used for lung cancer treatment; however, follow-up data indicate that physicians' subjective assessments of intraoperative images often overestimate the ablation effect, potentially leading to incomplete treatment. To address these challenges, we developed \textit{Respiratory Differencing}, a novel intraoperative CT image assistance system aimed at improving ablation evaluation. The system first segments tumor regions in preoperative CT images and then employs a multi-stage registration process to align these images with corresponding intraoperative or postoperative images, compensating for respiratory deformations and treatment-induced changes. This system provides two key outputs to help physicians evaluate intraoperative ablation. First, differential images are generated by subtracting the registered preoperative images from the intraoperative ones, allowing direct visualization and quantitative comparison of pre- and post-treatment differences. These differential images enable physicians to assess the relative positions of the tumor and ablation zones, even when the tumor is no longer visible in post-ablation images, thus improving the subjective evaluation of ablation effectiveness. Second, the system provides a quantitative metric that measures the discrepancies between the tumor area and the treatment zone, offering a numerical assessment of the overall efficacy of ablation.This pioneering system compensates for complex lung deformations and integrates pre- and intra-operative imaging data, enhancing quality control in cancer ablation treatments. A follow-up study involving 35 clinical cases demonstrated that our system significantly outperforms traditional subjective assessments in identifying under-ablation cases during or immediately after treatment, highlighting its potential to improve clinical decision-making and patient outcomes.