Kavli Affiliate: Feng Wang
| First 5 Authors: Wan Li, Wei Li, Moheng Rong, Yutao Rao, Hui Tang
| Summary:
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.
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