Patient Dielectric Mapping for Anatomy-Specific TTFields Placement
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Solution Overview
Problem
Existing methods for simulating tumor treating fields (TTFields) delivery in cancer treatment face challenges in accurately mapping dielectric properties to three-dimensional patient models, leading to variations in treatment efficacy due to inconsistent tissue type segmentation and labeling.
Innovation Solution
A method involving determining dielectric property information for patient voxels using predictive models trained on image data, allowing for optimized transducer array placement to enhance electric field intensity and distribution based on individual patient anatomy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If segmentation and tissue type labeling procedures are used to map dielectric properties to 3D models, then dielectric property mapping can be achieved, but the accuracy varies based on tissue types and the process is complex
Solution Approach 1:
The patent replaces manual segmentation and labeling procedures with an automated machine learning model that processes medical images to predict dielectric properties. This substitution of automated computational methods for manual mechanical processes reduces variability and increases accuracy while simplifying the workflow.
Solution Approach 2:
The patent creates a predictive model that learns from training data containing labeled tissue types and corresponding dielectric properties. The model then copies this learned knowledge to automatically predict dielectric properties for new patient images without requiring manual segmentation, thereby improving consistency and reducing complexity.
2Reliability
If manual segmentation and labeling procedures are used, then dielectric properties can be assigned to tissue types, but the process is time-consuming and produces varying accuracy
Solution Approach 1:
The patent performs preliminary training of the machine learning model using a training set of medical images with known dielectric properties. This preliminary action creates a ready-to-use predictive model that can quickly and reliably predict dielectric properties for new patients without requiring time-consuming manual segmentation during the actual treatment planning process.
Solution Approach 2:
The patent replaces the time-consuming manual segmentation and labeling process with an automated machine learning-based prediction system. This substitution eliminates the variability and time requirements of manual procedures while maintaining or improving accuracy through consistent automated application across all patients.
3Power
If transducer arrays are placed on patient scalp for TTFields delivery, then electric fields can be delivered to target regions, but the field intensity and distribution vary based on patient anatomy
Solution Approach 1:
The patent uses the predictive model to determine anatomy-specific dielectric properties for different regions of each patient's head. This allows the simulation to account for local variations in tissue properties, enabling optimized transducer array placement that delivers appropriate field intensity to specific target regions while considering the unique anatomical characteristics of each patient.
Solution Approach 2:
The patent performs preliminary simulation of electric field distribution using the predictive dielectric properties before finalizing transducer array placement. This preliminary action allows treatment planners to predict and optimize field intensity and distribution for each patient's specific anatomy, ensuring effective treatment delivery tailored to individual variations.
Data Source
AI summary
Methods, systems, and apparatuses are described for associating dielectric properties with a patient model.