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

arXiv: https://arxiv.org/abs/2408.04299 · HTML: https://mlsanity.com/paper/2408.04299/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem](https://mlsanity.com/paper/2001.11767/) (2020)
- [PhysRFANet: Physics-Guided Neural Network for Real-Time Prediction of Thermal Effect During Radiofrequency Ablation Treatment](https://mlsanity.com/paper/2312.13947/) (2023)

## Abstract (arXiv)

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 textitRespiratory 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.