3D Model Generation for TTFields Transducer Placement
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Solution Overview
Problem
The generation of a three-dimensional model of a patient's body for effective positioning of Tumor Treating Fields (TTFields) transducers is hindered by incomplete or inconsistent image data, such as missing body portions, insufficient resolution, or different image modalities.
Innovation Solution
A computer-implemented method that receives image data in different modalities, uses predictive modeling and artificial intelligence techniques like Generative Adversarial Networks to modify and combine image data, generating a complete three-dimensional model by converting image data from one modality to another and filling in missing portions, thereby enabling accurate transducer placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data from multiple modalities are used to generate a three-dimensional model, then the completeness and accuracy of the model is improved, but the complexity of processing and integrating different image modalities increases
Solution Approach 1:
The patent employs an intermediary conversion process that transforms image data from a second modality into the first modality using trained machine learning models. This intermediary step enables seamless integration of multi-modality image data without requiring complex direct multi-modal processing, thereby improving model accuracy while managing processing complexity through standardized conversion pathways.
Solution Approach 2:
The system changes the modality parameter of image data by converting images from one modality to another using trained conversion models. This parameter transformation allows heterogeneous image data to be unified into a consistent format, enabling accurate three-dimensional model generation while simplifying the integration process through standardized parameter representation.
2Measurement precision
If image data with insufficient resolution is used, then the processing time and computational resources are reduced, but the quality and detail of the three-dimensional model deteriorates
Solution Approach 1:
The system performs preliminary enhancement of image resolution using trained machine learning models before generating the three-dimensional model. By pre-processing images to improve resolution, the system ensures high-quality model output without requiring excessive computational resources during the main modeling process, thus balancing model quality with processing efficiency.
3Measurement precision
If complete image data covering all body portions is available, then the accuracy of transducer placement is improved, but the difficulty of acquiring all necessary image data increases
Solution Approach 1:
The system creates a complete three-dimensional model by copying and integrating available image data portions with converted data from other modalities. Instead of requiring complete direct imaging of all body portions, the system reconstructs missing information through modality conversion and data fusion, thereby achieving accurate transducer placement while simplifying data acquisition requirements.
Data Source
AI summary
A computer-implemented method to generate a three-dimensional model, wherein the computer comprises one or more processors and memory accessible by the one or more processors, and the memory stores instructions that when executed by the one or more processors cause the computer to perform the computer-implemented method, includes: receiving first image data of a first portion of the patient's body in a first image modality, receiving second image data of a second portion of the patient's body in a second image modality, modifying the second image data from the second image modality to the first image modality, and generating, based on the first image data in the first image modality and the modified second image data in the second image modality, a three-dimensional model of the first portion and the second portion of the patient's body.