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

TL;DR

Respiratory Differencing is a novel intraoperative CT image system that improves lung cancer ablation evaluation by compensating for respiratory deformations and providing differential and quantitative assessments.

Contribution

The paper introduces a new image fusion system that enhances intraoperative ablation evaluation through advanced registration and differential imaging techniques.

Findings

System significantly outperforms traditional assessments in clinical cases.

Enables visualization of tumor and ablation zones despite respiratory motion.

Provides quantitative metrics for ablation efficacy.

Abstract

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.