MRI Dielectric Property Mapping for Consistent TTFields Delivery
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Solution Overview
Problem
Existing tumor treating field (TTFields) therapies face challenges in accurately delivering effective electric fields to target regions within patients due to variations in tissue types, leading to inconsistent treatment efficacy.
Innovation Solution
A method involving predictive modeling to determine dielectric properties of patient tissues using machine learning techniques, enabling precise optimization of transducer array placement for enhanced electric field delivery.
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 various tissue types within the specimen
Solution Approach 1:
The patent uses MRI images as a copy or representation of the actual tissue structures. By training a machine learning model to map dielectric properties directly from MRI image intensities, the system creates a simplified mapping process that avoids complex segmentation while maintaining accuracy. The MRI images serve as a surrogate for direct dielectric measurement, enabling the predictive model to estimate dielectric properties without manual tissue labeling.
2Productivity
If manual segmentation and tissue labeling are performed to determine dielectric properties, then treatment planning can be conducted, but the process is time-consuming and labor-intensive
Solution Approach 1:
The machine learning model performs dielectric property mapping autonomously without requiring manual segmentation or labeling by operators. The system self-learns the relationship between MRI image intensities and dielectric properties from training data, then automatically applies this knowledge to new patients. This eliminates the need for time-consuming manual procedures while maintaining or improving mapping accuracy.
Solution Approach 2:
The patent performs preliminary training of the machine learning model using labeled data from multiple patients before actual treatment planning. This pre-computed knowledge is stored in the model and can be rapidly applied to individual patients during treatment planning, eliminating the need to perform segmentation and labeling procedures each time treatment planning is needed.
3Reliability
If transducer arrays are placed on patient scalp to deliver TTFields, then electric fields can be induced, but variations in tissue types lead to inconsistent field delivery to target regions
Solution Approach 1:
The patent assigns different dielectric properties to different tissue types (brain tissue, skull, scalp, etc.) based on the predictive model's estimation from MRI images. This allows the electric field simulation to account for local variations in tissue properties, enabling more accurate prediction of field distribution. The system adapts the field delivery model to each patient's specific tissue composition rather than using uniform properties.
Solution Approach 2:
The machine learning model provides feedback about the patient's specific tissue dielectric properties based on their MRI scan. This information feeds into the electric field simulation, allowing the system to predict and optimize transducer array placement and parameters for each individual patient's unique tissue characteristics, thereby improving field delivery consistency.
Data Source
AI summary
Methods, systems, and apparatuses are described for associating dielectric properties with a patient model.