Deep Learning Tissue Ablation Planning
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Solution Overview
Problem
Conventional methods for planning tissue ablation procedures are imprecise as they do not accurately account for patient-specific anatomical characteristics and the interactions between multiple medical instruments, leading to variations in the actual ablation zone compared to estimated zones.
Innovation Solution
A method using a neural network trained with preoperative and postoperative images from similar procedures to predict the ablation region, allowing for patient-specific planning without in vivo measurements, and considering the impact of multiple instruments on the ablation zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional planning methods use manufacturer data and simple modelings, then the planning process is simple and quick, but the precision of ablation zone estimation deteriorates
Solution Approach 1:
The neural network is trained in advance using a large dataset of preoperative images, treatment parameters, and actual ablation zones from multiple patients. This preliminary training phase enables the network to learn complex patterns and relationships, which are then applied during actual surgical planning without requiring complex real-time computations
Solution Approach 2:
The invention uses deep learning to create a virtual copy of the patient's anatomy from preoperative images, allowing the simulation and prediction of ablation zones without physical intervention. The neural network learns from copied data from multiple patients to predict outcomes for the current patient
2Measurement precision
If conventional methods assume identical ablation zones for same parameters, then planning is simplified, but accuracy deteriorates due to ignoring patient-specific anatomy
Solution Approach 1:
The neural network analyzes the specific local characteristics of each patient's anatomy visible in their preoperative images, such as tissue density variations, organ boundaries, and anatomical structures. This enables customized ablation zone predictions tailored to each patient's unique anatomical features rather than applying generic models
Solution Approach 2:
The system processes multiple varying parameters including different imaging modalities (CT, MRI), various treatment parameters (power, duration, frequency), and anatomical characteristics. The neural network learns how these parameters interact and change across different patients to improve prediction accuracy
3Measurement precision
If multiple treatments are planned using union of individual ablation zones, then planning is simplified, but precision deteriorates due to ignoring treatment interactions
Solution Approach 1:
The neural network simultaneously processes multiple treatment plans and their interactions in a unified prediction framework. Instead of separately calculating individual ablation zones and taking their union, the network merges the information from multiple instruments and treatments to predict the actual combined ablation zone, accounting for thermal interactions and cumulative effects
Data Source
AI summary
The invention relates to a method and device for planning a surgical procedure aiming to ablate a tissue in an anatomical area of interest of a patient. On the basis of a preoperative image of the anatomical area of interest and of a set of planning parameters (P), a simulated image is generated by a neural network that has been previously trained using learning elements corresponding, respectively, to a similar surgical procedure for ablating a tissue in an anatomical area of interest for another patient. Each learning element comprises a preoperative image of the anatomical area of interest of a patient, planning parameters (P) used for the surgical procedure on this patient, and a postoperative image of the anatomical area of interest of this patient after the surgical procedure. An estimated ablation region may be segmented in the simulated image in order to be compared with a segmented region to be treated in the preoperative image.