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

TL;DR

The paper tackles the problem that intraoperative assessments of pulmonary ablation are prone to overestimating treatment effectiveness due to respiratory lung motion. It introduces Respiratory Differencing, an intraoperative CT image fusion framework that aligns preoperative tumor segmentation with intra-/postoperative images via a two-stage rigid and non-rigid registration (utilizing Unet-based segmentation and deedsBCV), producing differential images and the Ablation Effectiveness Scale (AES). AES quantifies how well the tumor region is covered by the ablation and controls over- versus under-ablation using a defined set of ratios $CR_1$, $CR_2$, and $ER$ with a threshold $\lambda$, enabling objective quality control. In a 35-patient clinical study, the system demonstrated superior correlation with long-term outcomes (Spearman $\rho$ up to 0.809) and correctly identified under-ablation cases that physicians initially missed, suggesting potential to improve decision-making and patient outcomes in lung tumor ablation. Overall, the work provides a practical, image-guided, quantitative framework for improving ablation evaluation and minimizing residual disease.

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.

Figures (5)

• Figure 1: The overall framework for Respiratory Differencing and AES quantitative assessment.
• Figure 2: Respiratory Differencing ablation therapy evaluation (a) Under-ablation; (b) Average-ablation; (c) Over-ablation; $T$: Tumor; $B$: Actual ablation; $A$: Planned ablation.
• Figure 3: HU distribution comparison between the preoperative tumor area and postoperative ablation treatment area.
• Figure 4: Registration results after tumor segmentation and corresponding HSV pseudo-color images on the real clinical data. The HSV pseudo-color images represent the difference between the registered moved images and the original postoperative images. Preoperative tumor masks after registration are remapped onto the HSV images and displayed in yellow, while the postoperative treatment area boundaries are depicted in blue. Red regions in these HSV images represent significant changes in HU values within the ablation area before and after the procedure. The image depicts a Respiratory Differencing example of an unsuccessful ablation procedure of a patient, which has been confirmed by medical records that this patient underwent surgical intervention for the tumor again after 3 months. It can be evidently observed that the ablation area (blue) did not completely encompass the tumor (yellow).
• Figure 5: Illustration of the two special cases. (a1) and (b1) represent the preoperative CT images of two cases, with the tumor areas indicated in red. (a2) and (b2) represent the corresponding postoperative CT images, where the green areas denote the treatment regions and the red areas represent the registered tumor areas.